product classification

Details

File: mistral/classifier_factory/product_classification.ipynb
Type: Jupyter Notebook
Use Cases: Classification
Content

Notebook content (JSON format):
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "AcooU1NoTWl4"
      },
      "source": [
        "# Product Classification: Customise your own classifier for tailored food categorization\n",
        "\n",
        "In this cookbook, we will delve into classification, specifically focusing on how to leverage the Classifier Factory to create classifiers tailored to your needs and use cases.\n",
        "\n",
        "For simplicity, we will concentrate on a specific example that requires multitarget classification.\n",
        "\n",
        "## Food Classification\n",
        "\n",
        "The specific use case we will explore is food classification. We aim to classify different dishes and recipes into various categories and further classify them by the main language of the recipe.\n",
        "\n",
        "We will focus on three values:\n",
        "- The dish or food name\n",
        "- The country it belongs to\n",
        "- The multi-categories\n",
        "\n",
        "This means we need to classify two main aspects: the country and the categories to which the food belongs.\n",
        "\n",
        "We will also arbitrarily decide that there should be no food without any category; there should always be at least one.\n",
        "\n",
        "## Dataset\n",
        "\n",
        "For this purpose, we will use a [subset](https://huggingface.co/datasets/pandora-s/openfood-classification) of the [Open Food Facts product database](https://huggingface.co/datasets/openfoodfacts/product-database) as the data relevant to our use case.\n",
        "\n",
        "This subset was curated to focus on the most prevalent labels and underwent a few steps for balancing.\n",
        "\n",
        "### Labels\n",
        "There are 2 main labels:\n",
        "- Country *single target*: The corresponding country of the food/dish among 8 possible values: `italy`, `spain`, `germany`, `france`, `united-states`, `belgium`, `united-kingdom` and `switzerland`.\n",
        "- Category *multi-target*: The category it belongs to among 8 possible values: `snacks`, `beverages`, `cereals-and-potatoes`, `plant-based-foods`, `dairies`, `plant-based-foods-and-beverages`, `meats-and-their-products` and `sweet-snacks`.\n",
        "\n",
        "There are 8 countries and 8 different categories.\n",
        "Due to the nature of each label, the dataset is split as follows:\n",
        "- `name`: The name of the food/dish, extracted from the `product_name` of the openfoodfacts/product-database dataset.\n",
        "- `country_label`: The country ID, extracted from `countries_tags` of the openfoodfacts/product-database dataset.\n",
        "- `category_labels`: The categories it belongs to, extracted from `categories_tags` of the openfoodfacts/product-database dataset.\n",
        "\n",
        "### Distribution\n",
        "![image/png](https://cdn-uploads.huggingface.co/production/uploads/64161701107962562e9b1006/UjF0RWnrYMTkN3SQZCjMl.png)\n",
        "Note that the food categories overlap each other, since a sample can have multiple categories.\n",
        "\n",
        "### Splits\n",
        "The dataset was split into 3 sets:\n",
        "- `train`: 80%\n",
        "- `validation`: 10%\n",
        "- `test`: 10%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7lZo_t--T1FV"
      },
      "source": [
        "### Data Preparation\n",
        "Lets download the dataset, we will install `datasets` and load it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YAwwzneFhci9"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wDe_aYx5hd7p"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset('pandora-s/openfood-classification')\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3g0lSUNVUtgK"
      },
      "source": [
        "We can take a look at the test set directly via colab by converting it to a pandas dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "fnH0zEJ8UtHq",
        "outputId": "c11e1dc1-6e8a-4e40-a215-6e565cbf91a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>country_label</th>\n",
              "      <th>category_labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Avena e nocciole cioccolato fondente</td>\n",
              "      <td>italy</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pomodori in pezzi</td>\n",
              "      <td>belgium</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Grandyoats, Nori Sesame Cashews</td>\n",
              "      <td>united-states</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Jus d'orange Profit</td>\n",
              "      <td>switzerland</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rote Beete</td>\n",
              "      <td>germany</td>\n",
              "      <td>{'plant-based-foods': 'true', 'cereals-and-pot...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10030</th>\n",
              "      <td>Yaourt doux</td>\n",
              "      <td>france</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10031</th>\n",
              "      <td>Mirtillo di bosco</td>\n",
              "      <td>italy</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10032</th>\n",
              "      <td>Rôti de porc cuit supérieur</td>\n",
              "      <td>france</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10033</th>\n",
              "      <td>Mix de vegetales con pepinillo</td>\n",
              "      <td>spain</td>\n",
              "      <td>{'plant-based-foods': 'true', 'cereals-and-pot...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10034</th>\n",
              "      <td>Olives vertes ail et persil bio</td>\n",
              "      <td>france</td>\n",
              "      <td>{'plant-based-foods': 'false', 'cereals-and-po...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10035 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       name  country_label  \\\n",
              "0      Avena e nocciole cioccolato fondente          italy   \n",
              "1                         Pomodori in pezzi        belgium   \n",
              "2           Grandyoats, Nori Sesame Cashews  united-states   \n",
              "3                       Jus d'orange Profit    switzerland   \n",
              "4                                Rote Beete        germany   \n",
              "...                                     ...            ...   \n",
              "10030                           Yaourt doux         france   \n",
              "10031                     Mirtillo di bosco          italy   \n",
              "10032           Rôti de porc cuit supérieur         france   \n",
              "10033        Mix de vegetales con pepinillo          spain   \n",
              "10034       Olives vertes ail et persil bio         france   \n",
              "\n",
              "                                         category_labels  \n",
              "0      {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "1      {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "2      {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "3      {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "4      {'plant-based-foods': 'true', 'cereals-and-pot...  \n",
              "...                                                  ...  \n",
              "10030  {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "10031  {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "10032  {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "10033  {'plant-based-foods': 'true', 'cereals-and-pot...  \n",
              "10034  {'plant-based-foods': 'false', 'cereals-and-po...  \n",
              "\n",
              "[10035 rows x 3 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = dataset[\"test\"].to_pandas()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HKm6rNHUF_n"
      },
      "source": [
        "Now that we have loaded our dataset, we will convert it to the proper desired format to upload for training.\n",
        "\n",
        "The data will be converted to a jsonl format as follows:\n",
        "```json\n",
        "{\"text\": \"Avena e nocciole cioccolato fondente\", \"labels\": {\"food\": [\"sweet-snacks\"], \"country_label\": \"italy\"}}\n",
        "{\"text\": \"Pomodori in pezzi\", \"labels\": {\"food\": [\"plant-based-foods-and-beverages\"], \"country_label\": \"belgium\"}}\n",
        "{\"text\": \"Grandyoats, Nori Sesame Cashews\", \"labels\": {\"food\": [\"snacks\"], \"country_label\": \"united-states\"}}\n",
        "{\"text\": \"Jus d'orange Profit\", \"labels\": {\"food\": [\"beverages\", \"plant-based-foods-and-beverages\"], \"country_label\": \"switzerland\"}}\n",
        "{\"text\": \"Rote Beete\", \"labels\": {\"food\": [\"plant-based-foods\", \"plant-based-foods-and-beverages\"], \"country_label\": \"germany\"}}\n",
        "...\n",
        "```\n",
        "With an example of a label being:\n",
        "```json\n",
        "\"labels\": {\n",
        "  \"food\": [\n",
        "    \"beverages\",\n",
        "    \"plant-based-foods-and-beverages\"\n",
        "  ],\n",
        "  \"country_label\": \"switzerland\"\n",
        "}\n",
        "```\n",
        "For multi-target classification."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltHoh_QXJh0n",
        "outputId": "64499d2e-f496-4fdc-b58a-d57dfa8f4385"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 80273/80273 [00:02<00:00, 38886.76it/s]\n",
            "100%|██████████| 10034/10034 [00:00<00:00, 28462.58it/s]\n",
            "100%|██████████| 10035/10035 [00:00<00:00, 42115.74it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "JSONL files have been saved.\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "import json\n",
        "\n",
        "def dataset_to_jsonl(split):\n",
        "    jsonl_data = []\n",
        "\n",
        "    all_category_labels = set()\n",
        "    all_countries = set()\n",
        "\n",
        "    # Collect all unique category labels and countries\n",
        "    for example in dataset[split]:\n",
        "        all_category_labels.update(example['category_labels'].keys())\n",
        "        all_countries.add(example['country_label'])\n",
        "\n",
        "    # Convert sets to sorted lists for consistent formatting\n",
        "    all_category_labels = sorted(all_category_labels)\n",
        "    all_countries = sorted(all_countries)\n",
        "\n",
        "    # Process each example in the split\n",
        "    for example in tqdm(dataset[split]):\n",
        "        labels = {\n",
        "            \"food\": [\n",
        "                tag\n",
        "                for tag in all_category_labels\n",
        "                if example['category_labels'][tag] == \"true\"\n",
        "            ]\n",
        "        }\n",
        "        labels[\"country_label\"] = example['country_label']\n",
        "\n",
        "        jsonl_data.append({\n",
        "            \"text\": example['name'],\n",
        "            \"labels\": labels\n",
        "        })\n",
        "\n",
        "    return jsonl_data, all_category_labels, all_countries\n",
        "\n",
        "# Process each split\n",
        "train_jsonl, _, _ = dataset_to_jsonl('train')\n",
        "validation_jsonl, _, _ = dataset_to_jsonl('validation')\n",
        "test_jsonl, all_category_labels, all_country_labels = dataset_to_jsonl('test')\n",
        "\n",
        "# Save the formatted data as JSONL files\n",
        "for split, data in zip(['train', 'validation', 'test'], [train_jsonl, validation_jsonl, test_jsonl]):\n",
        "    with open(f'{split}_openfood_classification.jsonl', 'w') as f:\n",
        "        for entry in data:\n",
        "            f.write(json.dumps(entry) + '\\n')\n",
        "\n",
        "print(\"JSONL files have been saved.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-wOQ0B9WCwm"
      },
      "source": [
        "The data was converted and saved properly. We can now train our model.\n",
        "\n",
        "## Training\n",
        "There are two methods to train the model: either upload and train via [la platforme](https://console.mistral.ai/build/finetuned-models) or via the [API](https://classifier-factory.platform-docs-9m1.pages.dev/capabilities/finetuning/classifier_factory/).\n",
        "\n",
        "First, we need to install `mistralai`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Td3wp01pWJkC",
        "outputId": "17f15cbc-1047-45ea-a581-c1a897b805a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "zsh:1: command not found: pip\n"
          ]
        }
      ],
      "source": [
        "!pip install mistralai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLDxqIh_WRAu"
      },
      "outputs": [],
      "source": [
        "from mistralai import Mistral\n",
        "\n",
        "# Set the API key for Mistral\n",
        "api_key = \"API_KEY\"\n",
        "\n",
        "# Set your Weights and Biases key\n",
        "wandb_key = \"WANDB_KEY\"\n",
        "\n",
        "# Initialize the Mistral client\n",
        "client = Mistral(api_key=api_key)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqzOnqF00HwF"
      },
      "source": [
        "We will upload 2 files, the training set and the validation set ( optional ) that will be used for validation loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WQPCfr5izp7a"
      },
      "outputs": [],
      "source": [
        "# Upload the training data\n",
        "training_data = client.files.upload(\n",
        "    file={\n",
        "        \"file_name\": \"train_openfood_classification.jsonl\",\n",
        "        \"content\": open(\"train_openfood_classification.jsonl\", \"rb\"),\n",
        "    }\n",
        ")\n",
        "\n",
        "# Upload the validation data\n",
        "validation_data = client.files.upload(\n",
        "    file={\n",
        "        \"file_name\": \"validation_openfood_classification.jsonl\",\n",
        "        \"content\": open(\"validation_openfood_classification.jsonl\", \"rb\"),\n",
        "    }\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz9agHvZ0JR5"
      },
      "source": [
        "With the data uploaded, we can create a job.\n",
        "\n",
        "We allow users to keep track of aconsiderable amount of metrics via our Weights and Biases integration that we strongly recommend, you can make use of it by providing the project name and your key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6w1mH7v0CJq",
        "outputId": "6cad81cb-a4db-44f6-85ff-235e6c9272dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"id\": \"1905e7d8-c6b1-4eb1-b349-e656944276d6\",\n",
            "    \"auto_start\": false,\n",
            "    \"model\": \"ministral-3b-latest\",\n",
            "    \"status\": \"QUEUED\",\n",
            "    \"created_at\": 1744810479,\n",
            "    \"modified_at\": 1744810479,\n",
            "    \"training_files\": [\n",
            "        \"7587cbfd-0c3e-4413-834e-b7e1c588d892\"\n",
            "    ],\n",
            "    \"hyperparameters\": {\n",
            "        \"training_steps\": 250,\n",
            "        \"learning_rate\": 7e-05,\n",
            "        \"weight_decay\": 0.1,\n",
            "        \"warmup_fraction\": 0.05,\n",
            "        \"epochs\": null,\n",
            "        \"seq_len\": 16384\n",
            "    },\n",
            "    \"validation_files\": [\n",
            "        \"e37c81ca-01c0-4b78-b335-8682d961e8be\"\n",
            "    ],\n",
            "    \"fine_tuned_model\": null,\n",
            "    \"suffix\": null,\n",
            "    \"integrations\": [\n",
            "        {\n",
            "            \"project\": \"product-classifier\",\n",
            "            \"name\": null,\n",
            "            \"run_name\": null,\n",
            "            \"url\": null\n",
            "        }\n",
            "    ],\n",
            "    \"trained_tokens\": null,\n",
            "    \"metadata\": {\n",
            "        \"expected_duration_seconds\": null,\n",
            "        \"cost\": 0.0,\n",
            "        \"cost_currency\": null,\n",
            "        \"train_tokens_per_step\": null,\n",
            "        \"train_tokens\": null,\n",
            "        \"data_tokens\": null,\n",
            "        \"estimated_start_time\": null\n",
            "    }\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Create a fine-tuning job\n",
        "created_job = client.fine_tuning.jobs.create(\n",
        "    model=\"ministral-3b-latest\",\n",
        "    job_type=\"classifier\",\n",
        "    training_files=[{\"file_id\": training_data.id, \"weight\": 1}],\n",
        "    validation_files=[validation_data.id],\n",
        "    hyperparameters={\"training_steps\": 250, \"learning_rate\": 0.00007},\n",
        "    auto_start=False,\n",
        "    integrations=[\n",
        "        {\n",
        "            \"project\": \"product-classifier\",\n",
        "            \"api_key\": wandb_key,\n",
        "        }\n",
        "    ]\n",
        ")\n",
        "print(json.dumps(created_job.model_dump(), indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKYBTLEL0dFd"
      },
      "source": [
        "Once the job is created, we can review details such as the number of epochs and other relevant information. This allows us to make informed decisions before initiating the job.\n",
        "\n",
        "We'll retrieve the job and wait for it to complete the validation process before starting. This validation step ensures the job is ready to begin."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OHPqiWt10Eyh",
        "outputId": "b4229360-1f20-47aa-dff2-53cdf3157853"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"id\": \"1905e7d8-c6b1-4eb1-b349-e656944276d6\",\n",
            "    \"auto_start\": false,\n",
            "    \"model\": \"ministral-3b-latest\",\n",
            "    \"status\": \"VALIDATED\",\n",
            "    \"created_at\": 1744810479,\n",
            "    \"modified_at\": 1744810483,\n",
            "    \"training_files\": [\n",
            "        \"7587cbfd-0c3e-4413-834e-b7e1c588d892\"\n",
            "    ],\n",
            "    \"hyperparameters\": {\n",
            "        \"training_steps\": 250,\n",
            "        \"learning_rate\": 7e-05,\n",
            "        \"weight_decay\": 0.1,\n",
            "        \"warmup_fraction\": 0.05,\n",
            "        \"epochs\": 6.414733841792868,\n",
            "        \"seq_len\": 16384\n",
            "    },\n",
            "    \"classifier_targets\": [\n",
            "        {\n",
            "            \"name\": \"food\",\n",
            "            \"labels\": [\n",
            "                \"plant-based-foods-and-beverages\",\n",
            "                \"meats-and-their-products\",\n",
            "                \"sweet-snacks\",\n",
            "                \"snacks\",\n",
            "                \"dairies\",\n",
            "                \"plant-based-foods\",\n",
            "                \"cereals-and-potatoes\",\n",
            "                \"beverages\"\n",
            "            ]\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"country_label\",\n",
            "            \"labels\": [\n",
            "                \"united-states\",\n",
            "                \"italy\",\n",
            "                \"germany\",\n",
            "                \"france\",\n",
            "                \"switzerland\",\n",
            "                \"united-kingdom\",\n",
            "                \"spain\",\n",
            "                \"belgium\"\n",
            "            ]\n",
            "        }\n",
            "    ],\n",
            "    \"validation_files\": [\n",
            "        \"e37c81ca-01c0-4b78-b335-8682d961e8be\"\n",
            "    ],\n",
            "    \"fine_tuned_model\": null,\n",
            "    \"suffix\": null,\n",
            "    \"integrations\": [\n",
            "        {\n",
            "            \"project\": \"product-classifier\",\n",
            "            \"name\": null,\n",
            "            \"run_name\": null,\n",
            "            \"url\": null\n",
            "        }\n",
            "    ],\n",
            "    \"trained_tokens\": null,\n",
            "    \"metadata\": {\n",
            "        \"expected_duration_seconds\": 1000,\n",
            "        \"cost\": 8.2,\n",
            "        \"cost_currency\": \"EUR\",\n",
            "        \"train_tokens_per_step\": 65536,\n",
            "        \"train_tokens\": 16384000,\n",
            "        \"data_tokens\": 2554120,\n",
            "        \"estimated_start_time\": null\n",
            "    },\n",
            "    \"events\": [\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744810479,\n",
            "            \"data\": {\n",
            "                \"status\": \"QUEUED\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744810480,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATING\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744810483,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATED\"\n",
            "            }\n",
            "        }\n",
            "    ],\n",
            "    \"checkpoints\": []\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Retrieve the job details\n",
        "retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "print(json.dumps(retrieved_job.model_dump(), indent=4))\n",
        "\n",
        "import time\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Wait for the job to be validated\n",
        "while retrieved_job.status not in [\"VALIDATED\"]:\n",
        "    retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "\n",
        "    clear_output(wait=True)  # Clear the previous output (User Friendly)\n",
        "    print(json.dumps(retrieved_job.model_dump(), indent=4))\n",
        "    time.sleep(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qr1uaK9L0fz4"
      },
      "source": [
        "We can now run the job."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gaRvSsmN0iB0",
        "outputId": "d1357fb4-7262-44b5-fcea-d0af0e9457e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"id\": \"1905e7d8-c6b1-4eb1-b349-e656944276d6\",\n",
            "    \"auto_start\": false,\n",
            "    \"model\": \"ministral-3b-latest\",\n",
            "    \"status\": \"QUEUED\",\n",
            "    \"created_at\": 1744810479,\n",
            "    \"modified_at\": 1744810486,\n",
            "    \"training_files\": [\n",
            "        \"7587cbfd-0c3e-4413-834e-b7e1c588d892\"\n",
            "    ],\n",
            "    \"hyperparameters\": {\n",
            "        \"training_steps\": 250,\n",
            "        \"learning_rate\": 7e-05,\n",
            "        \"weight_decay\": 0.1,\n",
            "        \"warmup_fraction\": 0.05,\n",
            "        \"epochs\": 6.414733841792868,\n",
            "        \"seq_len\": 16384\n",
            "    },\n",
            "    \"classifier_targets\": [\n",
            "        {\n",
            "            \"name\": \"food\",\n",
            "            \"labels\": [\n",
            "                \"plant-based-foods-and-beverages\",\n",
            "                \"meats-and-their-products\",\n",
            "                \"sweet-snacks\",\n",
            "                \"snacks\",\n",
            "                \"dairies\",\n",
            "                \"plant-based-foods\",\n",
            "                \"cereals-and-potatoes\",\n",
            "                \"beverages\"\n",
            "            ]\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"country_label\",\n",
            "            \"labels\": [\n",
            "                \"united-states\",\n",
            "                \"italy\",\n",
            "                \"germany\",\n",
            "                \"france\",\n",
            "                \"switzerland\",\n",
            "                \"united-kingdom\",\n",
            "                \"spain\",\n",
            "                \"belgium\"\n",
            "            ]\n",
            "        }\n",
            "    ],\n",
            "    \"validation_files\": [\n",
            "        \"e37c81ca-01c0-4b78-b335-8682d961e8be\"\n",
            "    ],\n",
            "    \"fine_tuned_model\": null,\n",
            "    \"suffix\": null,\n",
            "    \"integrations\": [\n",
            "        {\n",
            "            \"project\": \"product-classifier\",\n",
            "            \"name\": null,\n",
            "            \"run_name\": null,\n",
            "            \"url\": null\n",
            "        }\n",
            "    ],\n",
            "    \"trained_tokens\": null,\n",
            "    \"metadata\": {\n",
            "        \"expected_duration_seconds\": 1000,\n",
            "        \"cost\": 8.2,\n",
            "        \"cost_currency\": \"EUR\",\n",
            "        \"train_tokens_per_step\": 65536,\n",
            "        \"train_tokens\": 16384000,\n",
            "        \"data_tokens\": 2554120,\n",
            "        \"estimated_start_time\": null\n",
            "    },\n",
            "    \"events\": [\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744810479,\n",
            "            \"data\": {\n",
            "                \"status\": \"QUEUED\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744810480,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATING\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744810483,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATED\"\n",
            "            }\n",
            "        }\n",
            "    ],\n",
            "    \"checkpoints\": []\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Start the fine-tuning job\n",
        "client.fine_tuning.jobs.start(job_id=created_job.id)\n",
        "\n",
        "# Retrieve the job details again\n",
        "retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "print(json.dumps(retrieved_job.model_dump(), indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqW9NYAv0r5v"
      },
      "source": [
        "The job is now starting. Let's keep track of the status and plot the loss.\n",
        "\n",
        "For that, we highly recommend making use of our Weights and Biases integration, but we will also keep track of it directly in this notebook.\n",
        "\n",
        "### WANDB\n",
        "\n",
        "**Training:**\n",
        "\n",
        "![product-train-loss.png](https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/classifier_factory/product-train-loss.png)\n",
        "\n",
        "**Eval/Validation:**\n",
        "\n",
        "![product-validation-loss.png](https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/classifier_factory/product-eval-loss.png)\n",
        "\n",
        "**More:**\n",
        "\n",
        "![product-panel-loss.png](https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/classifier_factory/product-panel-loss.png)\n",
        "\n",
        "![product-loss.png](https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/classifier_factory/product-loss.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "afQRQHHj0muJ",
        "outputId": "f74abaae-236b-4b5f-ebf8-3c4f7fc7c735",
        "cellView": "form"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SUCCESS\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Loss Plot\n",
        "import pandas as pd\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Initialize DataFrames to store the metrics\n",
        "train_metrics_df = pd.DataFrame(columns=[\"Step Number\", \"Train Loss\"])\n",
        "valid_metrics_df = pd.DataFrame(columns=[\"Step Number\", \"Valid Loss\"])\n",
        "\n",
        "# Total training steps\n",
        "total_training_steps = retrieved_job.hyperparameters.training_steps\n",
        "\n",
        "# Wait for the job to complete\n",
        "while retrieved_job.status in [\"QUEUED\", \"RUNNING\"]:\n",
        "    retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "\n",
        "    if retrieved_job.status == \"QUEUED\":\n",
        "        time.sleep(5)\n",
        "        continue\n",
        "\n",
        "    # Clear the previous output (User Friendly)\n",
        "    clear_output(wait=True)\n",
        "    print(retrieved_job.status)\n",
        "\n",
        "    # Extract metrics from all checkpoints\n",
        "    for checkpoint in retrieved_job.checkpoints[::-1]:\n",
        "        metrics = checkpoint.metrics\n",
        "        step_number = checkpoint.step_number\n",
        "\n",
        "        # Check if the step number is already in the DataFrame\n",
        "        if (\n",
        "            step_number\n",
        "            not in train_metrics_df[\"Step Number\"]\n",
        "        ):\n",
        "            # Prepare the new row for train loss\n",
        "            train_row = {\n",
        "                \"Step Number\": step_number,\n",
        "                \"Train Loss\": metrics.train_loss,\n",
        "            }\n",
        "\n",
        "            # Append the new train metrics to the DataFrame\n",
        "            train_metrics_df = pd.concat(\n",
        "                [train_metrics_df, pd.DataFrame([train_row])], ignore_index=True\n",
        "            )\n",
        "\n",
        "            # Prepare the new row for valid loss if available\n",
        "            if metrics.valid_loss != 0:\n",
        "                valid_row = {\n",
        "                    \"Step Number\": step_number,\n",
        "                    \"Valid Loss\": metrics.valid_loss,\n",
        "                }\n",
        "                # Append the new valid metrics to the DataFrame\n",
        "                valid_metrics_df = pd.concat(\n",
        "                    [valid_metrics_df, pd.DataFrame([valid_row])], ignore_index=True\n",
        "                )\n",
        "\n",
        "    if len(retrieved_job.checkpoints) > 0:\n",
        "        # Sort the DataFrames by step number\n",
        "        train_metrics_df = train_metrics_df.sort_values(by=\"Step Number\")\n",
        "        valid_metrics_df = valid_metrics_df.sort_values(by=\"Step Number\")\n",
        "\n",
        "        # Plot the evolution of train loss and valid loss\n",
        "        plt.figure(figsize=(10, 6))\n",
        "\n",
        "        # Plot train loss\n",
        "        plt.plot(\n",
        "            train_metrics_df[\"Step Number\"],\n",
        "            train_metrics_df[\"Train Loss\"],\n",
        "            label=\"Train Loss\",\n",
        "            linestyle=\"-\",\n",
        "        )\n",
        "\n",
        "        # Highlight start and end points of train loss\n",
        "        plt.scatter(\n",
        "            train_metrics_df.iloc[[0, -1]][\"Step Number\"],\n",
        "            train_metrics_df.iloc[[0, -1]][\"Train Loss\"],\n",
        "            color=\"blue\",\n",
        "            zorder=5,\n",
        "        )\n",
        "\n",
        "        # Plot valid loss only if available\n",
        "        if not valid_metrics_df.empty:\n",
        "            plt.plot(\n",
        "                valid_metrics_df[\"Step Number\"],\n",
        "                valid_metrics_df[\"Valid Loss\"],\n",
        "                label=\"Valid Loss\",\n",
        "                linestyle=\"--\",\n",
        "            )\n",
        "\n",
        "            # Highlight start and end points of valid loss\n",
        "            plt.scatter(\n",
        "                valid_metrics_df.iloc[[0, -1]][\"Step Number\"],\n",
        "                valid_metrics_df.iloc[[0, -1]][\"Valid Loss\"],\n",
        "                color=\"orange\",\n",
        "                zorder=5,\n",
        "            )\n",
        "\n",
        "        plt.xlabel(\"Step Number\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        plt.title(\"Train Loss and Valid Loss\")\n",
        "        plt.legend()\n",
        "        plt.grid(True)\n",
        "        plt.show()\n",
        "\n",
        "    time.sleep(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-ZvhYEi1KQF"
      },
      "source": [
        "### Inference\n",
        "Our model is trained and ready for use! Let's test it on a sample from our test set!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S5ifw9pG1J51",
        "outputId": "a25436ae-b9fc-42dd-a60a-4898a377102f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text: Avena e nocciole cioccolato fondente\n",
            "Classifier Response: {\n",
            "    \"id\": \"05f72b5b50bf4d4b9b58bf650dadfe3c\",\n",
            "    \"model\": \"ft:classifier:ministral-3b-latest:8e2706f0:20250416:1905e7d8\",\n",
            "    \"results\": [\n",
            "        {\n",
            "            \"food\": {\n",
            "                \"scores\": {\n",
            "                    \"plant-based-foods-and-beverages\": 0.19011782109737396,\n",
            "                    \"meats-and-their-products\": 0.00018027107580564916,\n",
            "                    \"sweet-snacks\": 0.17583023011684418,\n",
            "                    \"snacks\": 0.13909316062927246,\n",
            "                    \"dairies\": 0.0024885653983801603,\n",
            "                    \"plant-based-foods\": 0.16261638700962067,\n",
            "                    \"cereals-and-potatoes\": 0.32593774795532227,\n",
            "                    \"beverages\": 0.003735779784619808\n",
            "                }\n",
            "            },\n",
            "            \"country_label\": {\n",
            "                \"scores\": {\n",
            "                    \"united-states\": 0.0010206910083070397,\n",
            "                    \"italy\": 0.9425649046897888,\n",
            "                    \"germany\": 0.0010048666736111045,\n",
            "                    \"france\": 0.02936650812625885,\n",
            "                    \"switzerland\": 0.020262280479073524,\n",
            "                    \"united-kingdom\": 0.0014394049067050219,\n",
            "                    \"spain\": 0.003071120008826256,\n",
            "                    \"belgium\": 0.0012702704407274723\n",
            "                }\n",
            "            }\n",
            "        }\n",
            "    ]\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Load the test samples\n",
        "with open(\"test_openfood_classification.jsonl\", \"r\") as f:\n",
        "    test_samples = [json.loads(l) for l in f.readlines()]\n",
        "\n",
        "# Classify the first test sample\n",
        "classifier_response = client.classifiers.classify(\n",
        "    model=retrieved_job.fine_tuned_model,\n",
        "    inputs=[test_samples[0][\"text\"]],\n",
        ")\n",
        "print(\"Text:\", test_samples[0][\"text\"])\n",
        "print(\"Classifier Response:\", json.dumps(classifier_response.model_dump(), indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8w2XmKOWewV"
      },
      "source": [
        "We can go even further and compare side by side normal prompting techniques with LLMs VS our new classifier, for this we will run the test set on multiple llms with structured outputs and compare the results to our classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dTkda9Ja3aVs",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Load Test and Set Response Type\n",
        "import json\n",
        "from pydantic import BaseModel\n",
        "from enum import Enum\n",
        "from typing import List\n",
        "\n",
        "# Load the JSONL file\n",
        "file_path = 'test_openfood_classification.jsonl'\n",
        "test_dataset = []\n",
        "\n",
        "with open(file_path, 'r') as file:\n",
        "    for line in file:\n",
        "        test_dataset.append(json.loads(line))\n",
        "\n",
        "# Define the enumerators for categories and countries\n",
        "Category = Enum('Category', {category.replace('-', '_'): category for category in all_category_labels})\n",
        "Country = Enum('Country', {country.replace('-', '_'): country for country in all_country_labels})\n",
        "\n",
        "# Define the Food model using the enumerators\n",
        "class Food(BaseModel):\n",
        "    categories: List[Category]\n",
        "    country: Country"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZhbeM8FZ29e4",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Define the Classify Function\n",
        "import random\n",
        "\n",
        "instruction_prompt = \"\"\"Classify the following food product, you need to classify the country of the dish and the food categories it belongs to.\n",
        "\n",
        "Product Name: {}\"\"\"\n",
        "\n",
        "def classify(text: str, model: str) -> tuple:\n",
        "    try:\n",
        "        if model[\"type\"] == \"random\":\n",
        "            possible_categories = list(all_category_labels)\n",
        "            possible_countries = list(all_country_labels)\n",
        "            predicted_categories = random.sample(possible_categories, random.randint(0, len(possible_categories)))\n",
        "            predicted_country = random.choice(possible_countries)\n",
        "            return predicted_categories, predicted_country\n",
        "        elif model[\"type\"] == \"classifier\":\n",
        "            classifier_response = client.classifiers.classify(\n",
        "                model=model[\"model_id\"],\n",
        "                inputs=[text],\n",
        "            )\n",
        "            results = classifier_response.results[0]\n",
        "\n",
        "            # Extract all labels with their scores\n",
        "            labels_with_scores = {label: results['food'].scores[label] for label in results['food'].scores.keys()}\n",
        "\n",
        "            # Find the country with the highest score\n",
        "            country_scores = results['country_label'].scores\n",
        "            country_with_highest_score = max(country_scores, key=country_scores.get)\n",
        "\n",
        "            return labels_with_scores, country_with_highest_score\n",
        "        else:\n",
        "            chat_response = client.chat.parse(\n",
        "                model=model[\"model_id\"],\n",
        "                messages=[\n",
        "                    {\n",
        "                        \"role\": \"user\",\n",
        "                        \"content\": instruction_prompt.format(text),\n",
        "                    },\n",
        "                ],\n",
        "                response_format=Food,\n",
        "                max_tokens=512,\n",
        "                temperature=0\n",
        "            )\n",
        "\n",
        "            return [c.value for c in chat_response.choices[0].message.parsed.categories], chat_response.choices[0].message.parsed.country.value\n",
        "    except Exception as e:\n",
        "        return {}, None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSRPWKMP5VIK",
        "outputId": "4da5a389-f6be-4745-cf77-fc20b7837660",
        "cellView": "form"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Running {'type': 'random', 'model_name': 'Random'} ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/300 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [00:00<00:00, 67671.89it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Scores: {'beverages': 0.09944751381215469, 'cereals-and-potatoes': 0.12637362637362637, 'dairies': 0.1393939393939394, 'meats-and-their-products': 0.09090909090909091, 'plant-based-foods': 0.14606741573033707, 'plant-based-foods-and-beverages': 0.14871794871794872, 'snacks': 0.14673913043478262, 'sweet-snacks': 0.17543859649122806}\n",
            "Average Category Score: 0.13413590773288847\n",
            "Country Score: 0.15\n",
            "\n",
            "Running {'type': 'classifier', 'model_name': 'Finetuned Classifier 3B', 'model_id': 'ft:classifier:ministral-3b-latest:8e2706f0:20250416:1905e7d8', 'thresholds': [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.7, 0.8, 0.9]} ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [02:08<00:00,  2.33it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Scores: {'beverages': 0.6346153846153846, 'cereals-and-potatoes': 0.7368421052631579, 'dairies': 0.7391304347826086, 'meats-and-their-products': 0.7878787878787878, 'plant-based-foods': 0.3132530120481928, 'plant-based-foods-and-beverages': 0.2823529411764706, 'snacks': 0.4430379746835443, 'sweet-snacks': 0.6470588235294118}\n",
            "Average Category Score: 0.5730211829971948\n",
            "Country Score: 0.7666666666666667\n",
            "Best Threshold: 0.25\n",
            "\n",
            "Running {'type': 'instruction', 'model_name': 'Ministral 3B', 'model_id': 'ministral-3b-latest'} ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [08:09<00:00,  1.63s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Scores: {'beverages': 0.39080459770114945, 'cereals-and-potatoes': 0.3888888888888889, 'dairies': 0.6346153846153846, 'meats-and-their-products': 0.6086956521739131, 'plant-based-foods': 0.1282051282051282, 'plant-based-foods-and-beverages': 0.0, 'snacks': 0.2684563758389262, 'sweet-snacks': 0.35526315789473684}\n",
            "Average Category Score: 0.3468661481647659\n",
            "Country Score: 0.47\n",
            "\n",
            "Running {'type': 'instruction', 'model_name': 'Ministral 8B', 'model_id': 'ministral-8b-latest'} ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [07:26<00:00,  1.49s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Scores: {'beverages': 0.5961538461538461, 'cereals-and-potatoes': 0.41818181818181815, 'dairies': 0.631578947368421, 'meats-and-their-products': 0.7428571428571429, 'plant-based-foods': 0.24731182795698925, 'plant-based-foods-and-beverages': 0.09523809523809523, 'snacks': 0.29365079365079366, 'sweet-snacks': 0.3220338983050847}\n",
            "Average Category Score: 0.4183757962140239\n",
            "Country Score: 0.5566666666666666\n",
            "\n",
            "Running {'type': 'instruction', 'model_name': 'Mistral Small 24B', 'model_id': 'mistral-small-latest'} ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [03:05<00:00,  1.62it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Scores: {'beverages': 0.5555555555555556, 'cereals-and-potatoes': 0.37037037037037035, 'dairies': 0.7083333333333334, 'meats-and-their-products': 0.7631578947368421, 'plant-based-foods': 0.297029702970297, 'plant-based-foods-and-beverages': 0.15, 'snacks': 0.2926829268292683, 'sweet-snacks': 0.449438202247191}\n",
            "Average Category Score: 0.4483209982553572\n",
            "Country Score: 0.5566666666666666\n",
            "\n",
            "Running {'type': 'instruction', 'model_name': 'Mistral Large 123B', 'model_id': 'mistral-large-latest'} ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [11:02<00:00,  2.21s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Scores: {'beverages': 0.7083333333333334, 'cereals-and-potatoes': 0.6, 'dairies': 0.7222222222222222, 'meats-and-their-products': 0.7837837837837838, 'plant-based-foods': 0.2912621359223301, 'plant-based-foods-and-beverages': 0.06153846153846154, 'snacks': 0.22666666666666666, 'sweet-snacks': 0.48484848484848486}\n",
            "Average Category Score: 0.4848318860394103\n",
            "Country Score: 0.6233333333333333\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# @title Run Evaluation\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "\n",
        "# Number of samples to evaluate\n",
        "n_samples = 300\n",
        "\n",
        "def calculate_score(actual, predicted):\n",
        "    \"\"\"\n",
        "    Calculate the accuracy score for a single label.\n",
        "\n",
        "    Parameters:\n",
        "    - actual: List of actual labels for each entry.\n",
        "    - predicted: List of predicted labels for each entry.\n",
        "\n",
        "    Returns:\n",
        "    - The accuracy score as a float.\n",
        "    \"\"\"\n",
        "    correct_predictions_count = 0\n",
        "    total_predictions_count = 0\n",
        "\n",
        "    for actual_labels, predicted_labels in zip(actual, predicted):\n",
        "        if actual_labels or predicted_labels:\n",
        "            total_predictions_count += 1\n",
        "            if actual_labels and predicted_labels:\n",
        "                correct_predictions_count += 1\n",
        "\n",
        "    return correct_predictions_count / total_predictions_count if total_predictions_count > 0 else 0\n",
        "\n",
        "def calculate_country_score(actual, predicted):\n",
        "    \"\"\"\n",
        "    Calculate the accuracy score for country predictions.\n",
        "\n",
        "    Parameters:\n",
        "    - actual: List of actual country labels for each entry.\n",
        "    - predicted: List of predicted country labels for each entry.\n",
        "\n",
        "    Returns:\n",
        "    - The accuracy score as a float.\n",
        "    \"\"\"\n",
        "    correct_predictions_count = sum(actual_country == predicted_country for actual_country, predicted_country in zip(actual, predicted))\n",
        "    total_predictions = len(actual)\n",
        "    accuracy_score = correct_predictions_count / total_predictions if total_predictions > 0 else 0\n",
        "\n",
        "    return accuracy_score\n",
        "\n",
        "def evaluate_classifier(dataset, model):\n",
        "    \"\"\"\n",
        "    Evaluate the classifier model on the dataset.\n",
        "\n",
        "    Parameters:\n",
        "    - dataset: List of entries with text and labels.\n",
        "    - model: Dictionary containing model details.\n",
        "\n",
        "    Returns:\n",
        "    - Category scores, country score, average category score, and best threshold (if applicable).\n",
        "    \"\"\"\n",
        "\n",
        "    # Initialize dictionaries to store actual and predicted labels for each category\n",
        "    category_scores = {label: {\"actual\": [], \"predicted\": []} for label in all_category_labels}\n",
        "    all_actual_countries = []\n",
        "    all_predicted_countries = []\n",
        "\n",
        "    # Store raw scores for classifier models\n",
        "    raw_scores = []\n",
        "\n",
        "    # Evaluate each entry in the dataset\n",
        "    for entry in tqdm(dataset[:n_samples]):\n",
        "        text = entry[\"text\"]\n",
        "        actual_categories = [cat for cat in entry[\"labels\"][\"food\"]]\n",
        "        actual_country = entry[\"labels\"][\"country_label\"]\n",
        "\n",
        "        # Predict categories and country using the model\n",
        "        if model[\"type\"] == \"classifier\":\n",
        "            predicted_categories, predicted_country = classify(text, model)\n",
        "            raw_scores.append((predicted_categories, predicted_country))\n",
        "        else:\n",
        "            predicted_categories, predicted_country = classify(text, model)\n",
        "\n",
        "        # Accumulate actual and predicted categories and countries\n",
        "        all_actual_countries.append(actual_country)\n",
        "        all_predicted_countries.append(predicted_country)\n",
        "\n",
        "        for label in all_category_labels:\n",
        "            actual_label = [label] if label in actual_categories else []\n",
        "            predicted_label = [label] if label in predicted_categories else []\n",
        "            category_scores[label][\"actual\"].append(actual_label)\n",
        "            category_scores[label][\"predicted\"].append(predicted_label)\n",
        "\n",
        "    if model[\"type\"] == \"classifier\":\n",
        "        best_threshold = None\n",
        "        best_average_category_score = 0\n",
        "\n",
        "        # Find the best threshold for the classifier model\n",
        "        for threshold in model[\"thresholds\"]:\n",
        "            actual_labels_per_category = {label: data[\"actual\"] for label, data in category_scores.items()}\n",
        "            predicted_labels_per_category = {label: [] for label in all_category_labels}\n",
        "\n",
        "            for raw_score in raw_scores:\n",
        "                predicted_categories, _ = raw_score\n",
        "                predicted_labels = [label for label, score in predicted_categories.items() if score > threshold]\n",
        "                for label in all_category_labels:\n",
        "                    predicted_labels_per_category[label].append([label] if label in predicted_labels else [])\n",
        "\n",
        "            category_score_results = {\n",
        "                label: calculate_score(actual_labels_per_category[label], predicted_labels_per_category[label])\n",
        "                for label in all_category_labels\n",
        "            }\n",
        "            average_category_score = np.mean(list(category_score_results.values()))\n",
        "\n",
        "            if average_category_score > best_average_category_score:\n",
        "                best_average_category_score = average_category_score\n",
        "                best_threshold = threshold\n",
        "\n",
        "        # Use the best threshold to compute final scores\n",
        "        predicted_labels_per_category = {label: [] for label in all_category_labels}\n",
        "\n",
        "        for raw_score in raw_scores:\n",
        "            predicted_categories, _ = raw_score\n",
        "            predicted_labels = [label for label, score in predicted_categories.items() if score > best_threshold]\n",
        "            for label in all_category_labels:\n",
        "                predicted_labels_per_category[label].append([label] if label in predicted_labels else [])\n",
        "\n",
        "        category_score_results = {\n",
        "            label: calculate_score(actual_labels_per_category[label], predicted_labels_per_category[label])\n",
        "            for label in all_category_labels\n",
        "        }\n",
        "        country_score = calculate_country_score(all_actual_countries, all_predicted_countries)\n",
        "        average_category_score = best_average_category_score\n",
        "        return category_score_results, country_score, average_category_score, best_threshold\n",
        "    else:\n",
        "        # Prepare the actual and predicted labels for each category\n",
        "        actual_labels_per_category = {label: data[\"actual\"] for label, data in category_scores.items()}\n",
        "        predicted_labels_per_category = {label: data[\"predicted\"] for label, data in category_scores.items()}\n",
        "\n",
        "        # Calculate score for each category and overall country score\n",
        "        category_score_results = {\n",
        "            label: calculate_score(actual_labels_per_category[label], predicted_labels_per_category[label])\n",
        "            for label in all_category_labels\n",
        "        }\n",
        "        country_score = calculate_country_score(all_actual_countries, all_predicted_countries)\n",
        "\n",
        "        # Calculate average category score\n",
        "        average_category_score = np.mean(list(category_score_results.values()))\n",
        "\n",
        "        return category_score_results, country_score, average_category_score, None\n",
        "\n",
        "# Dictionary to store model evaluation results\n",
        "model_results = {}\n",
        "\n",
        "# List of models to evaluate\n",
        "models = [\n",
        "    {\"type\": \"random\", \"model_name\": \"Random\"},\n",
        "    {\n",
        "        \"type\": \"classifier\",\n",
        "        \"model_name\": \"Finetuned Classifier 3B\",\n",
        "        \"model_id\": retrieved_job.fine_tuned_model,\n",
        "        \"thresholds\": [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.7, 0.8, 0.9] # the thresholds to try our multilabels on, depending on your data, training and use case, you may want to change your threshold to get the best score out of your model for your specific metric\n",
        "    },\n",
        "    {\"type\": \"instruction\", \"model_name\": \"Ministral 3B\", \"model_id\": \"ministral-3b-latest\"},\n",
        "    {\"type\": \"instruction\", \"model_name\": \"Ministral 8B\", \"model_id\": \"ministral-8b-latest\"},\n",
        "    {\"type\": \"instruction\", \"model_name\": \"Mistral Small 24B\", \"model_id\": \"mistral-small-latest\"},\n",
        "    {\"type\": \"instruction\", \"model_name\": \"Mistral Large 123B\", \"model_id\": \"mistral-large-latest\"},\n",
        "]\n",
        "\n",
        "# Evaluate each model\n",
        "for model in models:\n",
        "    print(\"\\nRunning\", model, \"...\")\n",
        "    category_scores, country_score, average_category_score, best_threshold = evaluate_classifier(test_dataset, model)\n",
        "\n",
        "    result = {\n",
        "        \"category_scores\": category_scores,\n",
        "        \"average_category_score\": average_category_score,\n",
        "        \"country_score\": country_score,\n",
        "    }\n",
        "\n",
        "    model_name = model['model_name']\n",
        "    if model[\"type\"] == \"classifier\":\n",
        "        model_name = f\"{model['model_name']} Threshold: {best_threshold}\"\n",
        "\n",
        "    model_results[model_name] = result\n",
        "\n",
        "    print(f\"Category Scores: {category_scores}\")\n",
        "    print(f\"Average Category Score: {average_category_score}\")\n",
        "    print(f\"Country Score: {country_score}\")\n",
        "    if model[\"type\"] == \"classifier\":\n",
        "        print(f\"Best Threshold: {best_threshold}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5W98pgFJ1-Lt",
        "outputId": "3679d25c-083d-49b6-e137-f6bd2c2d2b6e",
        "cellView": "form"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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KARVlHQkAADA8DUwAAAAAlJumn7xyAAAAAGxHAxMAwCgddNBBzZ4CAED+NP1klqOv8W0gI9aRAAAAw9PABAAwSg899FCzpwAAkDdNP/nlWL6g8e0gI9aRAAAAw9PABAAwShs2bGj2FAAA8qXpJ88cvd2NbwsZsY4EAAAYngYmAIBRmjx5crOnAACQJ00/+eaYM7/x7SEj1pEAAADDG0XFAgCA5MADD2z2FAAA8qPpJ+8cbW2N7wMyYh0JAAAwPGdgAgAYpTvvvLPZUwAAyIumnxo5IFvWkQAAAMPTwAQAAABA85WhWSaXpp9ccgAAAACVoYEJAGCUdt9992ZPAQAgD2Volsml6SeXHJAp60gAAIDhaWACABiljo6OZk8BAKD1laFZJpemn1xyQMasIwEAAIangQkAYJQWL17c7CkAALS2MjTL5NL0k0sOyJx1JAAAwPBGUckAAAAAgEdp7dJo61qi6UcOAAAAAGdgAgAYrQMOOKDZUwAAaFmal+SAKrKOBAAAGJ4GJgCAUVq5cmWzpwAA0LL6Z8zT9CMHVI51JAAAwPA0MAEAjNK6deuaPQUAgNY1c27j22j6yS8HVIx1JAAAwPA0MAEAjNLEiaP4IxEAAKOj6Se/HFBB1pEAAADD08AEADBKhxxySLOnAABQDZp+8ssBFWUdCQAAMDwNTAAAo3T77bc3ewoAAPnT9JNfDqgw60gAAIDhaWACAAAAoJw0/eSXo2d949sAAAAA2dPABAAwSrvttluzpwAAkC9NP3nmWL6g8e0gI9aRAAAAw9PABAAwSlOnTm32FAAA8qTpJ98ckzob3xYyYh0JAAAwPA1MAACj9OCDDzZ7CgAA+dH0k3eOOfMb3x4yYh0JAAAwPA1MAAAAAJSDpp8K5GhvfB8AAABA9jQwAQCM0v77j+KPQAAADE/TzyA5IFvWkQAAAMPTwAQAMEqrV69u9hQAAPJQlmaZXJp+cskBGbKOBAAAGJ4GJgCAUerq6mr2FAAAWl9ZmmVyafrJJQdkyjoSAABgeBqYAABGacIEv0oBADwqZWmWyaXpJ5cckDHrSAAAgOFZLQEAjNJhhx3W7CkAALSuLX3laJbJpeknlxyQOetIAACA4WlgAgAYpTvuuKPZUwAAaF3LFzS/WSaXpp9cckAFWEcCAAAMTwMTAMAo9ff3N3sKAACtq7db048cUDnWkQAAAMPTwAQAMEqzZs1q9hQAAFrXnPmafuSAyrGOBAAAGJ4GJgCAUZoxYxR/4AEAoKZjWuPbaPrJLwdUjHUkAADA8DQwAQCM0v3339/sKQAAVIemn/xyQAVZRwIAAAxPAxMAAAAA5abpJ78cAAAAAENoYAIAGKV999232VMAAMifpp/8ckCFWUcCAAAMTwMTAMAorVu3rtlTAADIm6af/HKsXdr4NpAR60gAAIDhaWACABilNWvWNHsKAAD50vSTZ46uJY1vBxmxjgQAABieBiYAgFFqa2tr9hQAAPKk6SffHDPmNb4tZMQ6EgAAYHgamAAARunwww9v9hQAAPKj6SfvHDPnNr49ZMQ6EgAAYHgamAAARumuu+5q9hQAAPKi6WeQHJAl60gAAIDhaWACABilvr6+Zk8BACAfZWiWyaXpJ5cckCHrSAAAgOFpYAIAGKUZM0bxhyAAAMrZLJNL008uOSBT1pEAAADD08AEADBKs2fPbvYUAABaXxmaZXJp+sklB2TMOhIAAGB4GpgAAEZp0aJFzZ4CAEBrK0OzTC5NP7nkgMxZRwIAAAxvFFUIAAAAAHiUetZHrLhH008iBwAAAFBxzsAEADBKe++9d7OnAADQupYv0PSTyAGVYh0JAAAwPA1MAACj1N3d3ewpAAC0rkmdmn7kgMqxjgQAABieBiYAgFFatWpVs6cAANC65szX9CMHVI51JAAAwPA0MAEAAAAw/ia0N76Npp+8cgAAAAAM0MAEADBKhx9+eLOnAABQHZp+8soBFWUdCQAAMDwNTAAAo7RgwYJmTwEAoBo0/eSVAyrMOhIAAGB4GpgAAEZp8+bNzZ4CAED+NP1klqOv8W0gI9aRAAAAw9PABAAwStOnT2/2FAAA8qbpJ78cy519hmqzjgQAABieBiYAgFHaY489mj0FAIB8afrJM0dvd+PbQkasIwEAAIangQkAYJTuvffeZk8BACBPmn7yzTFnfuPbQ0asIwEAAIY3iooFAAAAAOwimn7yztHW1vg+AAAAgOw9qjMwbdq0KW6//fbYvHnz2M0IAKBFzJs3r9lTAADIi6afGjkgW9aRAAAAY9jAtGHDhnjDG94QU6dOjcc+9rGxaNGi4vE3v/nN8eEPf3g0uwQAaDmpmRsAgDFShmaZXJp+cskBGbKOBAAAGMMGprPPPjtuuummuPrqq6Ozs3Pr48997nPjsssuG80uAQBazsqVK5s9BQCAPJShWSaXpp9cckCmrCMBAACGN4oKRsQVV1xRNCo99alPjbYh161PZ2O6++67R7NLAAAAAKqoDM0yuTT95JIDAAAAqJxRNTAtW7Ys9tprrx0eX79+/TYNTQAAOTv00EObPQUAgNZWhmaZXJp+cskBmbOOpMrWnL13s6dAE806b3GzpwAA5HgJueOOOy6++93vbr1fb1r63Oc+FyeccMLYzQ4AoMQWLVrU7CkAALSutUub3yyTS9NPLjmgAqwjAQAAxvAMTB/60Ifi5JNPjj/+8Y+xefPm+I//+I9i/Mtf/jKuueaa0ewSAKDlbNq0qdlTAABoWW1dSzT9yAGVYx0JAAAwhmdgesYznhE33XRT0bz0uMc9Ln70ox8Vl5S79tpr49hjjx3NLgEAWs7UqVObPQUAgJbVP2Oeph85oHKsIwEAAIbXcFWjt7c3/vZv/zbe8573xEUXXdTo5gDldfP7Im45d9vHZh4R8ae3RaxbGPGtg4ff7hlfjTjgLyN6VkZce2rEQz+JmHFYxPEXR+z+xMHX/eZNEdPnRxz59l2bAxg3qYEbAIBRmjm38W00/eSXAyrGOhIAAGCMzsA0adKkuPzyyxvdDKA1zHpsxJ8vHrw99+e1x6fuv+3j6fa4cyMmTo/Y++Taa/7wLxGbuyJecEPEXs+KuO70wf0u/1XEil9HHPHW5uQCdomFCxc2ewoAANWh6Se/HFBB1pEAAABjeAm5U045Ja644orRbApQbm0TI6bMG7x1zqk9PqF928fT7f5vRhzwVxGTptdes+bWiANfETHz8IhD/6Z2P9nSG3Hd30U8+cLafgAAAGiMpp/8cgAAAAA82gamww47LN7//vfHy172sjjvvPPiE5/4xDa3RlxwwQVx0EEHRWdnZxx//PFx3XXX7fT1q1evjje96U2x9957R0dHRxx++OHxve99bzQxAHbUdWfEN/eJ+N/5Eb94VcT6RcO/buX1EatujDjkDYOPzT4mYun/DRRyfxix2+Nrj//x/Ii5z4rY47jxyQCMG6f+BwAYB5p+8ssBFWYdCQAAMLxRVDkiPv/5z8duu+0W119/fXEbqq2tLf7hH/5hRPu57LLL4swzz4wLL7ywaF76+Mc/HieddFLcfvvtwy7kNm3aFM973vOK577+9a/HvvvuG/fee28xF4BHbc7xESd8IWLGEREbF0fccm7ElSdGvOiWiEnbFWTv/nzEzCMj9nza4GNHnRXxmzdGfOuQiOkHRTz18xFr74y455KI519bOwvT4h/VGpmeclHE5FnjHhEYW1u2bGn2FAAA8qbpJ78cPesb3wYyYh0JAAAwhg1M99xzT4yFj370o3H66afHaaedVtxPjUzf/e534+KLL46zzjprh9enx1euXBm//OUvY9KkScVj6exNAGNin5MHx7MfX2to+t8DIxZ9ddszLW3eGLHwKxFHv2fb7VND0tO/su1jV/2/iCd+JGLhlyPWLYh48e0Rvz494pb3Rzzp33dxIGBXW758eeyxxx7NngYAQJ40/eSZY/mCxreDjFhHAgAAjOEl5Ibq7+8vbo1KZ1NKZ2967nOfOziZCROK+9dee+2w23zrW9+KE044obiE3Ny5c+Poo4+OD33oQ9HX1/ew79PT0xNr167d5gYwIpN3i5hxeETXXds+ft/XI/o2RBz82p1vf/d/RUzaLWK/P4tYenXEfqdETJgUccBf1u4DAAAwPE0/+eaY1Nn4tgAAAED2RnUGpuSLX/xifOQjH4k777yzuH/44YfHO9/5znjNa14z4k+apMaj1Ig0VLp/2223DbvNggUL4v/+7//iVa96VXzve9+Lu+66K/7+7/8+ent7473vfe+w25x33nlx7rnn7vB4mvf06dPj0EMPjfvuu69odJoyZUrMmzdv6xmm0qXqUnPWsmXLivvz58+PBx98MLq7u6OzszP22WefYk7JnDlzigashx56aOuZodJ4w4YNMXny5DjggAOK+Sa777578diSJUuK+wceeGCsWLEi1q1bFxMnTize54477iiemz17dvFeixcvLu6n/axatSq6urqivb29mH96bZrnrFmzikwPPPBA8dr99tuveN2aNWuKS/ul71HKnU5TPGPGjOLSeyl7krKkua5evbq4f8QRR8Tdd98dmzdvLvaZ5rxo0aLiub333rs4XulsWMlhhx1WXMovNaVNnTq1OG4LFy7c+v1M3+f0/U4OOeSQuP/++7ce77Sv+jHcc889i69Dj3fKvXHjxujo6CjypDnVj3fKv3Tp0mGPdzqm9Z/NNPe0/dBjmOZeP95pTumyhUk6JilD+j4n+++/f3FM0nFM39+UdejxTscx5UnSJQ3TPoce7/Q9T/nT69L3cugxTD9H6XuZpNem41A/3ulTWOmYJulnMh3b+vFO3/O0n+GOdxqn72/9eO/sZzYd7zTP+s/swQcfXPxM1o93yl7/mU3zScdq6PFO36f169cXZ0NL9+vHe2c/s490vNP3uN5oWD/e9Z/ZmTNnFrf68d7Zz+wjHe+033TM0r8d06ZNK47F0J/ZtI/032Rb3/o4bN3dsXLjC2P57bdv/Tdi8+8/GX0znx2bN06M/g0rh/03Ymr7+tjvlvfHgkMuic233x4H92yMzevXxH233x7TVy+Mvbdsjgfuu8+/Ef6N8G9EC/8bUf/vMz3n9wj/Rvg3wr8Rw/0bYa3h3wj/Rvg3wr8Rw/8bMbt3Y0yJndD0k3eO3Q+MWHxLLLx3YfSsnubfCP9GVO73iPS+6WfR7xF+j6jivxHTintUVfoZ9W/E+P0bUf/3GgBaSVv/KE6flC799p73vCfOOOOMePrTn1489vOf/zwuuOCC+OAHPxhve9vbHnEf6X/safGULgeXzqpU94//+I9xzTXXxK9//esdtkkLpvQ/6/RLTloI1ueSGqnqv1RsL/0Cn2516ReI9AtMWrClXxoAtrrhHRH7vjhi2oERGx+M+P17I1bdGPGiP0Z01opVxdmYvn14xLO+F7HPCx5+X794VcScEyKOOKN2/4/n1y5Fd8J/R9zw9ojpB0c8+YLxyQXsMqkQkAp2AAA0YOkNEV86Nvr3PSaiY/q2z2n6yT9Hb3e0PXBTxKuvj5j7pMb3By3OOpIqW3P23s2eAk0067zh/47HrpH+HpqaWv09FIDsz8D0yU9+Mj7zmc/Ea187ePmkl7zkJfHYxz423ve+942ogWn7T6PUpfvp0wLDSd3GqSu63ryUHHnkkUV3deraTt3/20sd1+kG8Ig23B/xy1dG9KyI6NgzYs9nRDz/V4PNS8ndF0dM3S9i7+c//H4e/GHEursinvbfg48dfkbEyt9G/PD4iD2eEvG44c8aB7SW1FgNAMAY0fQzSA7IlnUkAADA8EZVNUhnO3ra0562w+PpsYc7E9L2UrPRscceG1dddVWccsopxWPpdIrpfjqz03DS2Z6+8pWvFK9Lp2FM0ilJU2PTcM1LAA15xqWP/JonfKh225l9Tqrdhpo4NeIZX3108wNKJ51aGgCAMVCWZplcmn5yyQEZso4EAAAYXq0LqEHp2qlf/eqOf4i/7LLLimuwjtSZZ54ZF110UVxyySVx6623xhvf+Mbi2rOnnXZa8Xw6w9PZZ5+99fXp+XRN7be85S1F49J3v/vd+NCHPhRvetObRhMDAOBRSdetBwDgUSpLs0wuTT+55IBMWUcCAAAMb1TVg3PPPTde/vKXx09/+tPirEjJL37xi+LsScM1Nj2ctI9ly5bFOeecU1wG7glPeEL84Ac/iLlz5xbPL1q0aOuZlpL9998/fvjDHxaXqHv84x8f++67b9HM9K53vWs0MQAAHpUFCxbEEUcc0expAAC0rrI0y+TS9JNLDsiYdSQAAMDwRlVB+Iu/+Iv49a9/HR/72MfiiiuuKB478sgj47rrrosnPvGJDe0rXS7u4S4Zd/XVV+/w2AknnBC/+tWvRjNtAAAAAMpiS185mmVyafrJJQcAAABQSaOuIhx77LHxpS99aWxnAwDQQvbcc89mTwEAoHUtXxCxuUfTjxxQKdaRAAAAwxu8PlsDvve97xWXctteeuz73//+aHYJANBy2tramj0FAIDW1dut6UcOqBzrSAAAgDFsYDrrrLOir69vh8f7+/uL5wAAquChhx5q9hQAAFrXnPmafuSAyrGOBAAAGMMGpjvvvDOOOuqoHR5/zGMeE3fddddodgkAAABAlXRMa3wbTT/55QAAAAAYbQPTrFmzYsGCBTs8npqXpk0bRfEJAKAFHXzwwc2eAgBAdWj6yS8HVJB1JAAAwPBGVVn4sz/7s3jrW98a3/zmN+OQQw7Z2rz09re/PV7ykpeMZpdUQV9PRP+WiIlTIvq6a4+1d0Zs3hjRNiGivSNi84aItvaB8fqItkkR7ZNr4wmTIyZMiuhdV9suFcZ6uyLapwyM10a0T4uY0F4bT5yeriofsbkrYmIqxPVHbF4XMWlmxJa+iL71A+PNEX0bIybNGBh3R0yaHrGlN2LLpoiJ0yL6NkX09w6MU46+iIlTZapnWrc4omdt7T3bJtYypdcU+SYOZO0YGKesU2rbF+OptUxp7ilrytS3oZY1zSntM41Tji09tfkW45Rjai1T/+baPrcZp3x9Q8ZbajlSviJTRy1fypTmWcy9fch4IIdMj5ypc3bE1D389yRTJTMtfWBB7H/gIVllyvH7JJNMMjUxE8BY0fSTXw6oqCVLlsQBBxzQ7GkAAADkcQam888/vzjTUrpkXPrESLql8R577BH/9m//NvazJA9/OC/i+jfXxje+u3ZL0mPpueTaUyPuuKA2/ulLI+75Ym181XMj7r+iNv7R8RFLrqqNv3NkxIrf1Mbf3C9i7W218ddmRWx8sPaHnzROX9P9NE7S69Lrk7R92k+S9pv2n6T3S++bpHmk+SRpfmmeMtXGP3lh9F92ZMSXjo340TMivvHU2vjHJ0Z8/fja+P/+JOJrA+OfPDPisqfUxtc8M+J/nhzxleNq4/Q13U/j9Hx63U8Gxmn7tJ80TvtN+0/j9H7pfdP4m0+N+OHA+H9PiPj+wPhbJ0R87+m18XeeVrulcXosPZfG6bX/OzBO+0j7kmlEmfq/+JiIb+7rvyeZKplpzs2vyC5Tjt8nmWSSqYmZAMaCpp/8ckCFbdy4sdlTAAAAKKW2/v7+/tFsmDa78sor46abboopU6bEMcccEyeeeGKU3dq1a4tL4K1ZsyZmzpzZ7OlUi0+v55npwV9E/M8zon/PwyImddT6ItvaavtL47SvFXdHbOqOmHNIxOTOHV9T2LLtOO2/+OdpS23b5XfXtt3jkNo8tn9NMU776B8yTodrYA5dD0Vb19LonzE3YsZeg48Pfc3WcZpX25DxwPNpDr07yfFImUacYyeZGsoxTKa070ZyDJdp08aI5Qtq287cJ9rS/l5xTcS+f+K/J5kql+neu/8YBx58WFaZcvw+ySSTTM7ABJTM0htqH4bY95iIjvTv3k5o+skvx7K7o61rScSrr4+Y+6TGt4cWt3DhwjjooIOaPQ1oijVn793sKdBEs85b3OwpVIq/hwKQfQPTtddeGytWrIg//dM/3frYJZdcEu9973tjw4YNccopp8QnP/nJ6OhITQzl5H/YMI6F57IUR3Mp8pYxR293tD1wk8IzldXX1xft7f5ADwCwSxqYcl1HyVEbW0dSUdaRVJkGpmrTwDS+/D0UgOwvIff+978//vCHP2y9//vf/z5OP/30eN7znhdnnXVWfPvb347zzhu45ABQbSUrjuZS5G35HJCZu+66q9lTAADIk3VUvjlmzGt8W8iIdSQAAMAYNDDdeOON8ZznPGfr/UsvvTSe8pSnxEUXXRRnnnlmfOITn4ivfvWrjewSyFHZiqO5FHlbPQcAAMBIWEflnWPm3Ma3BwAAALLXUAPTqlWrYu7cwSLDNddcEyeffPLW+09+8pPjvvvuG9sZAq2ljMXRXIq8rZwDMrXHHns0ewoAAHmxjhokB2TJOhIAAGAMGphS89I999xTjDdt2hQ33HBDPPWpT936fFdXV0yaNKmRXQI5yaU4KscgzUuwUxMn+m8CAGDMWEcNkgOyZR0JAAAwBg1ML3zhC+Oss86Kn/3sZ3H22WfH1KlT48QTT9z6/M033xyHHHJII7sEcrGlL4/iaC5F3rLkgMwtXbq02VMAAMiDddQgOSBr1pEAAADDa6j68IEPfCBe+tKXxjOf+cyYPn16XHLJJTF58uStz1988cXx/Oc/v5FdArlYviBic09rF0dzKfKWJQcAAMBIWEcNkoMK2PzJw6PK0sd/N/8oKmtT16ZmTwEAACiphioQc+bMiZ/+9KexZs2aooGpvb19m+e/9rWvFY8DFdTbHbHP0a1bHM2lyFuWHFARBx10ULOnAADQ2qyjBskBAAAAVFhDl5CrmzVr1g7NS8nuu+++zRmZgAqZM791i6O5FHnLkgMqZNmyZc2eAgBA6+pZbx1VJwcAAABQcaNqYALYQce01iyO5lLkLUsOqJj169c3ewoAAK19KXLrKDkAAAAANDABTVOG4mguRd6y5IAKmjRpUrOnAADQuiZ1WkfJAQAAAFDQwASMvzIUR3Mp8pYlB1TUQQcd1OwpAAC09qXIq7yOkgMAAABgKw1MwPgqQ3E0lyJvWXJAhd15553NngIAQOua0F7ddZQcAAAAANvQwASMnzIUR3Mp8pYlx9qljW8DAAAwGrmso+QAAAAA2IEGJmB8lKE4mkuRt0w5upY0vh1kZPbs2c2eAgBANeS0jpIDAAAAYAcamIBdrwzF0VyKvGXLMWNe49tCRjo7O5s9BQCA/OW2jqp8jr7GtwEAAACyp4EJ2LVKURzNpchbwhwz5za+PWRk8eLFzZ4CAEDeclxHVT3H8gWNbwcAAABkTwMTsOuUpTiaS5E3hxwAAAAjZR2VZ47e7sa3BQAAALKngQnYNcpUHM2lyNvqOSBDBxxwQLOnAACQJ+uofHPMmd/49gAAAED2NDABY69sxdFcirytnAMytWrVqmZPAQAgP9ZReefomNb4PgAAAIDsaWACxlYZi6O5FHlbNQdkrKurq9lTAADIi3VUjRwAAABAxWhgAsZOLsVROQZpXoKdmjhxFP9dAQAwPOuoGjkAAACACvJXN2Bs9KyPWHFP6xdHcynyliEHVMAhhxzS7CkAAOTBOqpGDgAAAKCinIEJGBvLF7R+cTSXIm8ZckBF3H777c2eAgBA67OOqpEDAAAAqDANTMDYmNTZ2sXRXIq8ZcgBAAAwUtZRNXIAAAAAFecScsDYmDO/dYujuRR5y5ADKma33XZr9hQAAFrX2qXR1rXEOkoOAAAAAGdgAsbIhPbWLI7mUuQtQw6ooKlTpzZ7CgAALUvzkhwAAAAAdRqYgOYoQ3E0lyJvGXJART344IPNngIAQMvqnzGv2usoOQAAAAC20sAEjL8yFEdzKfKWIQcAAMBozJxb3XWUHAAAAADb0MAEjK8yFEdzKfKWIkdf49tARvbbb79mTwEAoDqyWUfJAQAAALA9DUzA+ClDcTSXIm9Zcixf0Ph2kJG1a9c2ewoAANWQ0zpKDgAAAIAdaGACxkcZiqO5FHnLlKO3u/FtISMamAAAxkFu6yg5AAAAAHaggQnY9cpQHM2lyFu2HHPmN749ZGTCBL9KAQDsUjmuo6qeo2d949sAAAAA2RtFpQOgxYqjuRR5y5ijra3xfUBGDjvssGZPAQAgX7muo6qew6XIAQAAgGE4bQCw65SlOJpLkTeHHJCZO++8s9lTAADIk3VUvjkmdTa+LQAAAJA9DUzArlGm4mguRd5WzwEZ2rJlS7OnAACQH+uovHO4FDkAAAAwDA1MwNgrW3E0lyJvK+eATM2cObPZUwAAyIt1VAVytDe+DwAAACB7GpiAsVXK4mguRd4WzQEZ08AEADCGrKMGyQEAAABUjAYmYOzkUhyVo0bzEjyi+++/v9lTAADIg3XUIDkAAACAClI5AMbG2qXR1rWk9YujuRR5y5ADAABgJKyjBskBAAAAVJTqATAmNC/JAVW0zz77NHsK0DRrzt672VOgiWadt7jZUwByYR01SA4AAACgwlxCDhgT/TPmtXZxNJcibxlyQIVs2LCh2VMAAGhdW/qso+rkAAAAACpOAxMwNmbObd3iaC5F3jLkgIpZvXp1s6cAANC6li+wjkrkAAAAANDABDRJWYqjuRR5y5ADAACgEb3d1lFyAAAAABQ0MAHjryzF0VyKvGXIARV1xBFHNHsKAACta878aq+j5AAAAADYSgMTML7KUhzNpchbhhxQYXfffXezpwAA0Lo6plV3HSUHAAAAwDY0MAHjpyzF0VyKvGXI0bO+8W0gI5s3b272FAAAqiOXdZQcAAAAADtQWQDGR1mKo7kUecuSY/mCxreDjMyY4axlAADjIqd1lBzRP+cJET94Q+PvCwAAAGTLGZiAXa8ExdFcirylyzGps/FtISOzZ89u9hQAAPKX2zpKDgAAAIAdaGACdq2yFEdzKfKWLcec+Y1vDxlZtGhRs6cAAJC3HNdRVc+xdmnj2wAAAADZ08AE7DplKY7mUuQtZY72xvcBAABQ6XVUxXN0LWl8OwAAACB7GpiAXaNMxdFcirw55IDM7L333s2eAgBAnqyj8s0xY17j2wIAAADZ08AEjL2yFUdzKfK2eg7IUHd3d7OnAACQH+uovHPMnNv49gAAAED2NDABY6uMxdFcirytnAMytWrVqmZPAQAgL9ZRg+QAAAAAKkQDEzB2cimOyjFI8xIAADBerKMGyQEAAABUjAYmYGxs6cujOJpLkbcsOSBzhx12WLOnAACQB+uoQXIAAAAAFaSBCRgbyxe0fnE0lyJvWXJABSxcuLDZUwAAaH3WUYPkAAAAACrKX6SBsdHbHbHP0a1bHM2lyFuWHFARvb29UWUbPnRQs6cAALQ666hBcgAAAAAV5q/SwNiYM791i6O5FHnLkgMqZNq0ac2eAgBA6+pZH7HiHuuoRA4AAACg4lxCDhgbHdNasziaS5G3LDmgYvbcc89mTwEAoLUvRW4dJQcAAACABiagacpQHM2lyFuWHFBBCxcubPYUAABa16RO6yg5AAAAAAoamIDxV4biaC5F3rLkAAAAGM2lyKu8jpIDAAAAYCsNTMD4KkNxNJcib1lyQIXNnTu32VMAAGhdE9qru46SAwAAAGAbGpiA8VOG4mguRd6y5Fi7tPFtICObN29u9hQAAKojl3WUHAAAAAA70MAEjI8yFEdzKfKWKUfXksa3g4ysWLGi2VMAAKiGnNZRcgAAAADsQAMTsOuVoTiaS5G3bDlmzGt8WwAAgCqvoyqfo6/xbQAAAIDsaWACdq1SFEdzKfKWMMfMuY1vDxk59NBDmz0FAIC85biOqnqO5Qsa3w4AAADIngYmYNcpS3E0lyJvDjkgM/fdd1+zpwAAkC/rqDxz9HY3vi0AAACQPQ1MwK5RpuJoLkXeVs8BGerp6Wn2FAAA8mQdlW+OOfMb3x4AAADI3igqFgAtVhzNpcjbyjkgU1OmTGn2FAAA8mMdlXeOtrYInwMAAAAAtuMMTMDYKmNxNJcib6vmgIzNmzev2VMAAMiLdVSNHAAAAEDFOAMTMHZyKY7KMUjzEo9g8ycPj6p3gm9u9iQAAHJhHVUjBwAAAFBBGpiAsdGzPmLFPa1fHM2lyFuGHAAAACNlHVUjBwAAAFBRLiEHjI3lC1q/OJpLkbcMOQAAAEbKOqpGDgAAAKDCNDABY2NSZ2sXR3Mp8pYhBwAAwEhZR9XIAQAAAFRcKRqYLrjggjjooIOis7Mzjj/++LjuuutGtN2ll14abW1tccopp+zyOQKPYM781i2O5lLkLUMOAACAkVq71DoqkQMAAACg+Q1Ml112WZx55pnx3ve+N2644YY45phj4qSTToqHHnpop9stXLgw3vGOd8SJJ544bnMFdmJCe2sWR3Mp8pYhBwAAQAPaupZYR8kBAAAAUI4Gpo9+9KNx+umnx2mnnRZHHXVUXHjhhTF16tS4+OKLH3abvr6+eNWrXhXnnntuzJ8/f1znC4yRMhRHcynyliEHAABAg/pnzKv2OkoOAAAAgHI0MG3atCmuv/76eO5znzs4oQkTivvXXnvtw273/ve/P/baa694wxve8Ijv0dPTE2vXrt3mBjRZGYqjuRR5y5ADAABgNGbOre46Sg4AAACAbYyisjF2li9fXpxNae7cbQtW6f5tt9027DY///nP4/Of/3zceOONI3qP8847rzhT0/buvPPOmD59ehx66KFx3333FY1OU6ZMiXnz5sU999xTvCY1SfX398eyZcuK++lsTw8++GB0d3dHZ2dn7LPPPrFgwYLiuTlz5hTNV/VL3x100EHFeMOGDTF58uQ44IAD4q677iqe23333YvHlixZUtw/8MADY8WKFbFu3bqYOHFi8T533HFH8dzs2bOL91q8eHFxP+1n1apV0dXVFe3t7cX802vTPGfNmlVkeuCBB4rX7rfffsXr1qxZE21tbXH44YcXubds2RIzZsyI3XbbrciepCxprqtXry7uH3HEEXH33XfH5s2bi32mOS9atKh4bu+99y6O18qVK4v7hx12WNx7771FQ1o6e1Y6bukSf/XvZfoep+91csghh8T999+/9XinfdWP4Z577ll8HXq8U+6NGzdGR0dHkSfNqX68U/6lS5cOe7zTMU1Z68c7bT/0GKa51493mtPtt99ePJeOScqQvs/J/vvvXxyTdBzT9zdlHXq803FMeZJ999232OfQ452+5yl/el36Xg49hunnKH0vk/TadBzqx3uPPfYojmmSfibTsa0f7/Q9T/sZ7nincfr+1o/3zn5m0/FO86z/zB588MHFz2T9eKfs9Z/ZNJ90rIYe7/R9Wr9+fUxbf3/sFy1WHM2lyFuKHH21qfR0x70D/x35N6Ja/0Yc0vhPDQAZSL8HWmv4PWJXrzUmTZpU3K8f752tjx/peKfvcf1DTfXjXf+ZnTlzZnGrH++d/cw+0vFO+03HrLe3N6ZNm1Yci6E/s2kfaf1fP95VrUfM7t0YUyIqvI6SAwConvS7rrXG+K016us+AGglbf2p0tQk6X/uqRD7y1/+Mk444YStj//jP/5jXHPNNfHrX/96m9enXxQe//jHx6c//ek4+eSTi8de97rXFf+jv+KKK4Z9j1QITLe69AtE+gUmFdHSLw3Ao7T0hogvHRv9+x4T0TG9/MXRXIq8ZcnxwO+jrXdDxKuvj5j7pMb3Qcvb/MnDmz0FmmhT16ZmT4Em6u0a/B2b6pl1Xq2oC7DL15E5rqOqnqNnXURPW+PvSTb61q5v9hRoIuvIarOOrDbryPGV/h6aPmjg76EAtJKmnoFp+0+21qX76ZOH20tdyKmb+MUvfvHWx1IHc5I6oFP3c+qCHip1XKcb0GSKvHnm6O1ufFsAAIAqr6PkAAAAANjBhGiidNryY489Nq666qptGpLS/aFnZKp7zGMeE7///e+Ly8fVby95yUvi2c9+djFOZ1YCSqgMxdFcirxlyzFnfuPbAwAAVHkdVfUcPc6+AwAAAJTsDEzJmWeeGaeeemocd9xx8ZSnPCU+/vGPF9efPe2004rnX/va1xaXmTvvvPOK688effTR22yfrjWbbP84UBJlKI7mUuQtY442p/0HAAB2kVzXUVXPsXxBxJzHN74tAAAAkLWmNzC9/OUvj2XLlsU555wTS5YsiSc84Qnxgx/8IObOnVs8v2jRopgwoaknigJGqyzF0VyKvGXM0bOu8f0AAABUeR1V9RyTOhvfFgAAAMhe0xuYkjPOOKO4Defqq6/e6bZf+MIXdtGsgEelTMXRXIq8rZ4DAABgJKyj8s6x+4ERvY3vAgAAAMibUxsBY69sxdFcirytnAMAAGAkrKMqkKO98X0AAAAA2dPABIytUhZHcynytmgOAACAkbCOGiQHAAAAUDEamICxk0txVI4azUsAAMB4sY4aJAcAAABQQSoHwNhYuzTaupa0fnE0lyJvGXIAAACMhHXUIDkAAACAinIGJmBMaF6SAwAAoGHWUYPkAAAAACpMAxMwJvpnzGvt4mguRd4y5AAAABiJLX3WUXVyAAAAABWnigCMjZlzW7c4mkuRtww5AAAARmr5gojNPdZRcgAAAAA4AxPQJGUpjuZS5C1DDgAAgEb0dltHyQEAAABQ0MAEjL+yFEdzKfKWIQcAAECj5syv9jpKDgAAAICtNDAB46ssxdFcirxlyAEAADAaHdOqu46SAwAAAGAbGpiA8VOW4mguRd4y5OhZ3/g2AAAAo5HLOkoOAAAAgB1oYALGR1mKo7kUecuSY/mCxrcDAACo8jpKDgAAAIAdaGACdr2yFEdzKfKWKcekzsa3BQAAqPI6Sg4AAACAHWhgAnatshRHcynyli3HnPmNbw8AAFDldVTVc6xd2vg2AAAAQPY0MAG7TlmKo7kUeUuZo73xfQAAAFR6HVXxHF1LGt8OAAAAyJ4GJmDXKFNxNJcibw45AAAARsI6Kt8cM+Y1vi0AAACQPX99BsZe2YqjuRR5Wz0HAADASFhH5Z1j6uyInsZ3AQAAAOTNGZiAsVXG4mguRd5WzgEAADAS1lGD5AAAAAAqxF+hgbGTS3FUjoZy9M95QsQP3tD4vgEAACq6jnpEcgAAAAAV4wxMwNjY0pdHcTSXIm9ZcgAAAIyEddQgOQAAAIAK8tdoYGwsXxCxuae1i6O5FHnLkgMAAGAkrKMGyQEAAABUlDMwAWOjt7u1i6O5FHnLkgMAAGAkrKMGyQEAAABUmL9KA2NjzvzWLY7mUuQtSw4AAICR6FkfseIe66hEDgAAAKDinIEJGBsd01qzOJpLkbcsOQAAABq5FLl1lBwAAAAAGpiApilDcTSXIm9ZcgAAADRiUqd1lBwAAAAABQ1MwPgrQ3E0lyJvWXIAAACM5lLkVV5HyQEAAACwlQYmYHyVoTiaS5G3LDkAAABGY0J7dddRcgAAAABsQwMTMH7KUBzNpchblhxrlza+DQAAwGjkso6SAwAAAGAHGpiA8VGG4mguRd4y5eha0vh2AAAAVV5HyQEAAACwAw1MwK5XhuJoLkXesuWYMa/xbQEAAKq8jqp8jr7GtwEAAACyp4EJ2LVKURzNpchbwhwz5za+PQAAQJXXUVXPsXxB49sBAAAA2dPABOw6ZSmO5lLkzSEHAADASFlH5Zmjt7vxbQEAAIDsaWACdo0yFUdzKfK2eg4AAICRso7KN8ec+Y1vDwAAAGRvFBULgBYrjuZS5G3lHAAAACNlHZV3jra2iJ7GdwMAAADkzRmYgLFVxuJoLkXeVs0BAAAwUtZRNXIAAAAAFaOBCRg7uRRH5RikeQkAABgv1lE1cgAAAAAV5BJywNjoWR+x4p7WL47mUuQtQw4AAICRso6qkQMAAACoKGdgAsbG8gWtXxzNpchbhhwAAAAjZR1VIwcAAABQYRqYgLExqbO1i6O5FHnLkAMAAGCkrKNq5AAAAAAqziXkgLExZ37rFkdzKfKWIQcAAMBIrV0abV1LrKPkAAAAAHAGJmCMTGhvzeJoLkXeMuQAAABogOYlOQAAAADqNDABzVGG4mguRd4y5AAAAGhQ/4x51V5HyQEAAACwlQYmYPyVoTiaS5G3DDkAAABGY+bc6q6j5AAAAADYhgYmYHyVoTiaS5G3FDn6Gt8GAABgNLJZR8kBAAAAsD0NTMD4KUNxNJcib1lyLF/Q+HYAAABVXkfJAQAAALADDUzA+ChDcTSXIm+ZcvR2N74tAABAlddRcgAAAADsQAMTsOuVoTiaS5G3bDnmzG98ewAAgCqvo6qeo2d949sAAAAA2dPABOxaZSiO5lLkLWOOjmmN7wMAAKDK66iq53ApcgAAAGAYGpiAXacsxdFcirw55AAAABgJ66h8c0zqbHxbAAAAIHsamIBdo0zF0VyKvK2eAwAAYCSso/LO4VLkAAAAwDA0MAFjr2zF0VyKvK2cAwAAYCSsoyqQo73xfQAAAADZ08AEjK1SFkdzKfK2aA4AAICRsI4aJAcAAABQMRqYgLGTS3FUjhrNSwAAwHixjhokBwAAAFBBKgfA2Fi7NNq6lrR+cTSXIm8ZcgAAAIyEddQgOQAAAICKcgYmYExoXpIDAACgYdZRg+QAAAAAKkwDEzAm+mfMa+3iaC5F3jLkAAAAGIktfdZRdXIAAAAAFaeKAIyNmXNbtziaS5G3DDkAAABGavmCiM091lFyAAAAADgDE9AkZSmO5lLkLUMOAACARvR2W0fJAQAAAFDQwASMv7IUR3Mp8pYhBwAAQKPmzK/2OkoOAAAAgK00MAHjqyzF0VyKvGXIAQAAMBod06q7jpIDAAAAYBsamIDxU5biaC5F3jLk6Fnf+DYAAACjkcs6Sg4AAACAHWhgAsZHWYqjuRR5y5Jj+YLGtwMAAKjyOkoOAAAAgB1oYAJ2vbIUR3Mp8pYpx6TOxrcFAACo8jpKDgAAAIAdaGACdq2yFEdzKfKWLcec+Y1vDwAAUOV1VNVzrF3a+DYAAABA9jQwAbtOWYqjuRR5S5mjvfF9AAAAVHodVfEcXUsa3w4AAADIngYmYNcoU3E0lyJvDjkAAABGwjoq3xwz5jW+LQAAAJA9f30Gxl7ZiqO5FHlbPQcAAMBIWEflnWPq7IiexncBAAAA5M0ZmICxVcbiaC5F3lbOAQAAMBLWUYPkAAAAACpEAxMwdnIpjsoxSPMSAAAwXqyjBskBAAAAVIwGJmBsbOnLoziaS5G3LDkAAABGwjpqkBwAAABABflrNDA2li+I2NzT2sXRXIq8ZckBAAAwEtZRg+QAAAAAKsoZmICx0dvd2sXRXIq8ZckBAAAwEtZRg+QAAAAAKsxfpYGxMWd+6xZHcynyliUHAADASPSsj1hxj3VUIgcAAABQcc7ABIyNjmmtWRzNpchblhwAAACNXIrcOkoOAAAAAA1MQNOUoTiaS5G3LDkAAAAaManTOkoOAAAAgIIGJmD8laE4mkuRtyw5AAAARnMp8iqvo+QAAAAA2EoDEzC+ylAczaXIW5YcAAAAozGhvbrrKDkAAAAAtqGBCRg/ZSiO5lLkLUuOtUsb3wYAAGA0cllHyQEAAACwAw1MwPgoQ3E0lyJvmXJ0LWl8OwAAgCqvo+QAAAAA2IEGJmDXK0NxNJcib9lyzJjX+LYAAABVXkdVPkdf49sAAAAA2dPABOxapSiO5lLkLWGOmXMb3x4AAKDK66iq51i+oPHtAAAAgOxpYAJ2nbIUR3Mp8uaQAwAAYKSso/LM0dvd+LYAAABA9jQwAbtGmYqjuRR5Wz0HAADASFlH5ZtjzvzGtwcAAACyV4oGpgsuuCAOOuig6OzsjOOPPz6uu+66h33tRRddFCeeeGLMnj27uD33uc/d6euBJihbcTSXIm8r5wAAABgp66i8c3RMa3wfAAAAQPaa3sB02WWXxZlnnhnvfe9744YbbohjjjkmTjrppHjooYeGff3VV18dr3zlK+MnP/lJXHvttbH//vvH85///HjggQfGfe5AixRHcynytmoOAACAkbKOqpEDAAAAqJimNzB99KMfjdNPPz1OO+20OOqoo+LCCy+MqVOnxsUXXzzs67/85S/H3//938cTnvCEeMxjHhOf+9znYsuWLXHVVVeN+9yBTIujcgzSvAQAAIwX66gaOQAAAIAKamoD06ZNm+L6668vLgO3dUITJhT309mVRmLDhg3R29sbu++++y6cKfCIetbnURzNpchbhhwAAAAjZR1VIwcAAABQUaOoYIyd5cuXR19fX8ydO3ebx9P92267bUT7eNe73hX77LPPNk1QQ/X09BS3urVr1z7KWQPDWr4gomNaaxdHcynyliEHAADASFlH1cgBAAAAVFhTG5gerQ9/+MNx6aWXxtVXXx2dnZ3Dvua8886Lc889d4fH77zzzpg+fXoceuihcd999xVNTlOmTIl58+bFPffcU7xmr732iv7+/li2bFlxf/78+fHggw9Gd3d38X6pcWrBggXFc3PmzCnOHvXQQw8V9w866KBinM4QNXny5DjggAPirrvuKp5LZ4tKjy1ZsqS4f+CBB8aKFSti3bp1MXHixOJ97rjjjuK52bNnF++1ePHi4n7az6pVq6Krqyva29uL+afXpnnOmjWryPTAAw8Ur91vv/2K161Zsyba2tri8MMPL3KnS+7NmDEjdttttyJ7krKkua5evbq4f8QRR8Tdd98dmzdvLvaZ5rxo0aLiub333rs4XitXrizuH3bYYXHvvfcWZ9RKl/9Lx23hwoVbm9FSk1pqVksOOeSQuP/++7ce77Sv+jHcc889i69Dj3fKvXHjxujo6CjypDnVj3fKv3Tp0mGPdzqmKWv9eKfthx7DNPf68U5zuv3224vn0jFJGdL3Odl///2LY5KOY/r+pqxDj3c6jilPsu+++xb7HHq80/c85U+vS9/Loccw/Ryl72WSXpuOQ/1477HHHsUxTdLPZDq29eOdvudpP8Md7zRO39/68d7Zz2w63mme9Z/Zgw8+uPiZrB/vlL3+M5vmk47V0OOdvk/r16+Paevvj/3Sg5M6W7s4mkuRtww5AIDspd8DrTWsNXb1WmPSpEnF/frx3tn6+JGOd/oepw80pVv9eNd/ZmfOnFnc6sd7Zz+zj3S8037TMUtnip42bVpxLIb+zKZ9pPV//XhXtR4xu3djTKn/g2IdVSMHAJC59Luutcb4rTXq6z4AaCVt/anS1CT1X1S+/vWvxymnnLL18VNPPbX4n/f//u//Puy2//Zv/xYf/OAH48c//nEcd9xxD/u64c7AlH6BSUW09EsD8CgtvSHiS8dG/95HR0yZ1ZrF0VyKvM3I0bMuoqet8fchG31r1zd7CjTRpq5NzZ4CTdTbNfg7NtUz67xaURfgUa0jZ8yLtq4l1VtHVT2HdWTlWUdWm3VktVlHVpt15PhKfw9NHzTw91AAWsmEZr55+tTfscceG1ddddXWx1JHcrp/wgknPOx2559/fnzgAx+IH/zgBzttXkpSx3W9w7l+A3aBCe2Nb6PIm1cOAACABmhekgMAAACgFA1MyZlnnhkXXXRRXHLJJXHrrbfGG9/4xuL0jaeddlrx/Gtf+9o4++yzt77+X//1X+M973lPXHzxxcXpHdOpIdMtne4caCFlKI7mUuQtQw4AAKiqn/404sUvTteDiGhri7jiim2ff93rao8Pvb3gBYPPp7NGv+Y1EekDV4cfHvHjH2+7/Uc+EvHmN0eO0hmYKr2OkgMAAABgq1FUNcbWy1/+8uKas+ecc07RiPSEJzyhOLNSul5rkq71mq4ZW/eZz3ymuPTcy172sm328973vjfe9773jfv8gWjN4mguRd4y5AAAgCpbvz7imGMiXv/6iJe+dPjXpIal//qvwfsdHYPjz3424vrrI669NuL734/467+OWLq01uh0zz0RF10U8dvfRpZm1mo/lVxHyQEAAABQrgam5Iwzzihuw7n66qu3ub9w4cJxmhWwS5ShOJpLkbcUOfrK8r8SAABojpNPrt12JjUszZs3/HO33hrxkpdEPPaxEfPnR7zznRHLl0fsuWfEG9+YTkVdOzsTGa2j5AAAAAAo3SXkgAopQ3E0lyJvWXIsX9D4dgAAUDXpw1l77RVxxBG1pqQVKwafS2dv+vnPIzZujPjhDyP23jtizpyIL385orMz4s//vJkzL4+c1lFyAAAAAOzAaTOA8VGG4mguRd4y5ejtbnxbAACoknT5uHRpuYMPjrj77oh3v7t2xqZ0ybj29tql526+OeKoo2qNS1/9asSqVRHnnFNrfPrnf4649NKIQw6JuPjiiH33jcrJbR0lBwAAAMAONDABu14ZiqO5FHnLlmPO/Ma3BwCAKnnFKwbHj3tcxOMfX2tGSs1Jz3lOxKRJERdcsO02p50W8Q//EPG730VccUXETTdFnH9+7bHLL49KyXEdVfUcPesjYnrj2wEAAABZcwk5YNcqQ3E0lyJvGXN0TGt8HwAAUGXz59fOtHTXXcM//5OfRPzhDxFnnFFrcnrhCyOmTYv4q7+q3a+SXNdRVc/hUuQAAADAMJyBCdh1ylIczaXIW8YcPesa3w8AAFTZ/fdHrFgRsffeOz7X3R3xpjdFfPnLtcvL9fVF9PfXnuvtrd2vipzXUVXPMamz8W0BAACA7DkDEwz1059GvPjFEfvsE9HWVjtV/1Dve1/EYx5T+/Tr7NkRz31uxK9/Pfh8T0/Ea14TMXNmxOGHR/z4x9tu/5GPRLz5zVEJZSqO5lLkbfUcAACQo3XrIm68sXZL7rmnNl60qPbcO98Z8atfRSxcGHHVVRF/9mcRhx4acdJJO+7rAx+onXHpiU+s3X/60yO+8Y2Im2+O+NSnaverwDoq7xwuRQ4AAAAMwxmYYKj16yOOOSbi9a+PeOlLd3w+NSWlonE65f/GjREf+1jE859fO/X/nntGfPazEddfH3HttRHf/37EX/91xNKltWaoVMS+6KKI3/42sle24mguRd5WzgEAALlKa7xnP3vw/pln1r6eemrEZz5Taz665JKI1atrH5ZJa8jUqNTRse1+brkl4qtfHWyESl72stpl4048MeKIIyK+8pXInnVU/jl6uxvfBwAAAJA9DUww1Mkn124PJzUkDfXRj0Z8/vO1gvRznhNx660RL3lJxGMfW2tySp+0Xb681tz0xjdG/Ou/1s7OlLMyFkdzKfK2ag4AAMjZs541eJm34fzwhyPbz9FHR9x557aPTZgQ8elP125VYB01SA4AAACgYlxCDkZr06baGZdmzaqdtSlJX3/+89rZmVKReu+9I+bMifjylyM6OyP+/M8ja7kUR+Wo0bwEAACMF+uoQXIAAAAAFaRyAI36znciXvGKiA2piLd3xJVX1pqUknTpuXQ2pqOOqj2WTv+/alXEOefUTvv/z/8ccemlEYccEnHxxRH77hvZWLs02rqWtH5xNJcibxlyAAAAjIR11CA5AAAAgIpyBiZo1LOfHXHjjRG//GXEC14Q8Vd/FfHQQ7XnJk2KuOCCiHvuifjNbyKe8YyIt7894h/+IeJ3v4u44oqIm26KeOpTa49lRPOSHAAAAA2zjhokBwAAAFBhGpigUdOmRRx6aK0J6fOfj5g4sfZ1OD/5ScQf/hBxxhm1MzC98IW17VPTU7qfkf4Z81q7OJpLkbcMOQAAAEZiS591VJ0cAAAAQMWpIsCjtWVLRE/Pjo93d0e86U0RX/5yRHt7RF9fRH9/7bne3tr9nMyc27rF0VyKvGXIAQAAMFLLF0Rs7rGOkgMAAADAGZhgG+vW1S4Pl25JuhRcGi9aFLF+fcS73x3xq19F3HtvxPXXR7z+9REPPBDxl3+5474+8IHaGZee+MTa/ac/PeIb34i4+eaIT32qdr/KylIczaXIW4YcAAAAjejtto6SAwAAAKCgmgBD/fa3Ec9+9uD9M8+sfT311IgLL4y47baISy6JWL48Yo89Ip785Iif/SzisY/ddj+33BLx1a8ONkIlL3tZ7bJxJ54YccQREV/5SlRWWYqjuRR5y5ADAACgUXPmV3sdJQcAAADAVioKMNSznjV4mbfhpDMojcTRR0fceee2j02YEPHpT9duVVaW4mguRd4y5AAAABiNjmnVXUfJAQAAALANl5ADxk9ZiqO5FHnLkKNnfePbAADASPX0RGzcWBt3d9duSXosPZds2DA4Tpf+3rRpcNzbO3i58M2ba+OursHx2rURfX2D4y1bah9qSeP0Nd1P4yS9rj5O26f91Mdp/0l6v/S+SZpHfZzml+Yp00COjRED043uvojNWwZe3xfRN/Choo2bB8cbBsZpHXXPLRETp0TMOzIivU2Rqb/2miLTduONQ8d9tXXU8kXRP3W/2jqqd0ttDkXWLRE9w4w3bandinltilj0h9o6avcjI9oHmrDSPtI2I8k0NEdaD6YcKUP/wNxHkqmeY8pAjs3b5XikTN1DcuwxJEfPdjl2lqmeo31IjqHfs51lSuO0n6Hfm/q4PvfN/YPz3bx9ji2D4/r3Zuj3KX2tz71n6HhIjmI8MN/uod+n7cZbthunLGlcz1Sfu0wjz9T7COO0XT1HGvcNM+7eMjaZ0n7q455hxkW+YXJsP5apsUzp8XqmNK5nqs99ywhypHF9vpt3Mq7PtzeNB96/d8jcN+1k3DeCfDKNPlN97tuP6/PtfZjx0Bzbj+tz79nJOOVIt+3H9UwPN940TKbt88n0yJlG8jssAFBZGpiA8aHpJ88cyxc0vh0AACRtbY986+yMmDq1Np4ypXZL4/RYei6Np00bHE+fHtHRMTiePLk2njEjYtKk2njmzMHxrFkREycOjtvba2fPTeP0Nd1P4/R8el19nLZP+6mP0/7TOL1fet80TvOoj9P80jxlqo2PekbE9QM/B+/7Q8R1K2vjs26KuHl1bfzm30XcNdBQdfpvI+5ZXqyj2v5pS0TnoRE9bdH26l/X/iC+clNtnDywsfb6JG2f9pOk/b7z+to66p45ER9fU3v86ocizr+tNv7+4ohPDJxN+fL7Iz43sN758r21W1pHferGiB+sr62jPru4tk2S9pH29UiZ/rC0th78QPrD48HFerCY+8pNRZYRZTrjt7UcS/aM+PCK2uPp/dL7jiRTynHRjRFXDOT474dqzyXptSPJ9NvFtRwf6Y9Yf0BtXZvmmOaa/vN+pEyLuyPeNjDfezZEvHtgvn/sinj/wHxvWBNx/t218S9WRXxyYW181fKIi+6tjb+zNOJLA3O/fHHtlqTH0nNJem3aJkn7SPsq8t1de48kvWd67yTNJc0pSXNMc02Z3vj7iNW9xR/ri3H6o/3q3tpYpoYyTfh27ed2wteXxYQra3Of8KWl0faz2twnfH5xtF1Xa65sv+CBaLu51lDZ/u/3RdsdtRzt/3JvxKJajvZzFkYsrTV6TnzXgog1m4ssxThlWrO5Nk6Wbqq9PlnUXdtPOhZ3bCj2X4xvXle8bzG+bm0xn2L8szXFPIs5XrmqmH8x/vYKmRrMNPUD90VbV19ET38xTl/T/WKc5rWsN6acX/uZmfDApuj8+IO18YLu6LxwSW1/f9wQHRfX5t7+u3XR8eXa3Cf+qismX16b+6Rr1sTkb9fmPunKVcUtSY+l54rx5SuKbZK0j7SvYnzx0uI9kvSe6b2L8ccfLOaUpDmmuco08kxTv1n7f+LkGzbE1O/Uxh3XrospV9Z+ljqv6SpuxVyuXFs8V8zlO6uLbYrxN1fHpFtq/7+ZdumqmHhnrUF6+hdXxsSFtRwzPrc82hfXcsy8YFlMWFFrkpn1sYeirSs16/UX4/Q13S/Gae4rNhevL+a7uLfYT/E9WLip2H8xvrOneN/ie3DLRpkazBTHHx9x1VW18ZFHRvzmN7XxfvtF3Dbw/xkAoLLa+vt3dr2s/KxduzZmzZoVa9asiZmpMAc8OktviPjSsdG/7zERHdOHf42mn3xzTOyImHV449uTjb61zsJVZZu6Bs4GQSX1dg0UH6mkWecN/DEVHo3UyEI17R0Rb4noP/CYiP4pERNTM9WEWpPL5NRk1VY7y9Dk9tp45eqIVbdGdE6rrT+mpoaugbN5TGmPSJWtdDaYqRMHzvowZLwpvWZixPJF0bbsvuifd0DEjH1rZwfobK+dsaJvyDidjaBju3Fx9oPNEStuq52BKq3lZsyqvc+E1PA1ofb+7UPGw2VatTpixa0RUwZyTOmoPZ7OSpTefySZUo6H7ov+vQ+ImLlfbW7p9enMNyPJlM5ysHIgx7yjImZul2MkmVavjlg+kGO3IyI6Jw/mSO/zSJnauyPS3zPTGSDS4/UzPdTHaZ5p25Snb0ttnylf39DvTfpL6oTaOO03HeP62SLq4/TeRaYttY9w1vO1D+QoxhNqudK8JtV/9ob+HPbV3mfCkHHab2og6ZxQe+96jvpZLWR6xEx9Xetrcxuaaft8Raa22jh9bd9unOY7qe3RZ0pnC0mvSeOUpWO7cZGvf8cc248TmUaUadP6WtNcTE7NyilHmktb7b03Dcy9ODPMI+RI43RL802PbXmYcfrzR5pv/cwxaT4pX9HI21bLNOFhxu3DZNo+X8qRtpFpRJl613QPyTEw9+3HSZpPytc2zHhoju3HEwfmnjJNephxypSkuQ8ddwxk6n2YcZrb5O0ybf89k2mnmWadv6R2BqbU1J4a6dMZmFLjexqnMzClhvfUbM+Y8PdQAFqRBiZg1zYwafrJO8fuB0b0jiIH2dDAVG0amKpNA1O1aWBiTGhgqq5901lgYucfhMl5HVX1HMvujuiY1/h2ZMM6stqsI6vNOrLarCPHl7+HAtCKXEIO2HXKUhzNpchbyhw+EQMAAOwi2a6jKp6jq3ZZIQAAAIChNDABu0aZiqO5FHlzyAEAADAS1lH55pjh7EsAAADAjvz1GRh7ZSuO5lLkbfUcAAAAI2EdlXeOqbMjXEEIAAAA2I4zMAFjq4zF0VyKvK2cAwAAYCSsowbJAQAAAFSIBibGT09PxMaNtXF3d+2WpMfSc8mGDYPj9esjNjGjVRAAACEvSURBVG0aHPf21sbr1kVs3lwbd3UNjteujejrGxxv2RLR318bp6/pfhon6XX1cdo+7ac+TvtP0vul903SPOrjNL80T5kGcmyMGJhubOiJuP8PteLo7MdETJo+sO3miL7+gddsN97SX8uSxsXXtRH33FIrju51ZMTA2xfbpP1sHQ8cl81bIroHxr0D41QcXbYo+qfuVyuOpsd7Bl6zaUvtVmTtqz23/TjluK+e48iIidMGcvQNzv2RMm0cyDFpSsTc7XKk14wk085yjCTTxp6IRUNytA/kSPtO7zGSTKlYnXJMnBIxbyBH8bM3JEeRt2/HHMX29Uz9g/PdvH2OLYPjeo6hmdLXrd+noeMhOYrxwNy7h2babrxlu3HKksb1TPW5yzTyTL2PME7b1XOkcd8w4+4tY5Mp7ac+7hlmXOQbJsf2Y5kay5Qer2dK43qm+ty3jCBHGtfnu3kn4/p8e9N44P17h8x9007GfSPIJ9PoM9Xnvv24Pt/ehxkPzbH9uD73np2MU450235cz/Rw403DZNo+n0yPnGkkv8MCtFqzTC5NP7nkAAAAACpDAxNjo63tkW+dnRFTp9bGU6bUbmmcHkvPpfG0aYPj6dMjOjoGx5Mn18YzZkRMmlQbz5w5OJ41K2LixMFxe3vEhAm1cfqa7qdxej69rj5O26f91Mdp/2mc3i+9bxqnedTHaX5pnjLVxkc9I+L6VBzti/jnGyJuGCiOvndBxM2raz8fb/5dxF0DDVWn/zbigVqDVNurfx2xclPxh/BivGpNxO1/iLZ/2lIrji7urb0+Sdun/SRpv2fdVBtftzLifX+oja9+KOJfbqwVR2/YLeK/BxqxLr8/4nMLauMv31u7Jemx9FzyiTsjvr+4VuT94O8ifrm+luPD99beI0nvOZJMi1dGLBzIsdsREau31B5P0utGkumfb6jluHX3iM8ONKCl+aV5jiRTyvGRGyOuHsjxiftrxydJx2skme5ZXhSrixydh0b0tNVypMaFlZsGMy3ujnjbwPfgng0R776tNv5jV8T7B+Z7w5qI8++ujX+xKuKTC2vjq5ZHXDQw9+8sjfjSwPfj8sW1W5IeS88l6bVpmyTtI+0rSftO75Gk90zvnaS5pDklaY5prun79MbfR6zuLf5YX4zTH+1X99bGMjWUacK3VxTDCV9fFhOurM19wpeWRtvPanOf8PnF0XZdrbmy/YIHou3m2s9z+7/fF2131HK0/8u9EYtqOdrPWRixtNboOfFdCyLWbC6yFOOUac3m2jhZuqn2+mRRd20/6VjcsaHYfzG+eV3xvsX4urXFfIrxz9YU8yzmeOWqYv7F+NsrZGow09QP3BdtXX0RPf3FOH1N94txmtey3phyfu1nZsIDm6Lz4w/Wxgu6o/PCJbX9/XFDdFxcm3v779ZFx5drc5/4q66YfHlt7pOuWROTv12b+6QrVxW3JD2WnivGl68otknSPtK+ivHFS4v3SNJ7pvcuxh9/sJhTkuaY5irTyDNN/Wbt/x+Tb9gQU79TG3dcuy6mXFn7Weq8pqu4FXO5cm3xXDGX76wutinG31wdk26p/T902qWrYuKdtQbp6V9cGRMX1nLM+NzyaE+/E0TEzAuWxYQVtSaZWR97KNq6UrNefzFOX9P9YpzmvmJz8fpivot7i/0U34OFm4r9F+M7e4r3Lb4Ht2yUqcFMcfzxEVddVRsfeWTEb35TG++3X8RtA/+fAWilZplcmn5yyQEAAABUSlt/f/pIb3WsXbs2Zs2aFWvWrImZqcGDsZEaWaimvSPiLRH9U6ZGrO+O2O+xEdNn1ppcJqcmq7bamX0mt9fG6aw9HUPGne0R6ccnNS+tvjVi0tSI2UdETO8cOOtDX8TUiQNnEOiLmFIfb4mY0l47S0w6O0Daz7JF0bb8vuifd0DE9H1rZyNI75XOZFEf189CkOaW9j0hNXkNjKMvYvltEV3rI/Y7KmL6rNqZaSamBrEJI8u0cnXEqlsjOqbWmpemdUSkf2W7h+R4pEzb5+gbyDc0x84y1XOsWx+xz1ERMwZytA9kHUmmeo7OaRGzDo+YmhrvBs66kuZYz9TeHZH+npnOAJEeH5ojjdM809zT96hvS22+6XvWNzRH+kvqhNo47TfNZ2imNE7vXXyfttRab+vfs/aBHMV4Qi1Xmtekeqah+fpq7zNhyDjtNzWQdE6ovXc9R/2sFjI9Yqa+9N/LpO0ybZ+vyNRWG6ev7duN03wntT36TOlsIek1aZyydGw3LvL175hj+3Ei04gybVpfa5qLyalZOeVIc2mrvfemgbkXZ4Z5hBxpnG5pvumxLQ8zTr+2pvnWzxyT5pPyFY28bbVMEx5m3D5Mpu3zpRxpG5lGlKl3TfeQHANz336cpPmkfG3DjIfm2H48cWDuKdOkhxmnTEma+9Bxx0Cm3ocZp7lN3i7T9t8zmXaaadb5S2pnYEpN7amRPp2BKTW+p3E6A1NqeE/N9rAz1pHVtW9qxo/o3/eYiI7p5WiWyaXppxVy9KwrPiBDdfWtHTgjOJW0qWvg7PRUUm/XwIchqKRZ5w18uJNx4e+hALQiDUyMDYXn6qoXnlMHxj5Hl7M4mkuRt4w5FJ4rT+G52hSeq03hudoUnhkT1pHVNbSBKZWlqraOqnoO68jKs46sNuvIarOOrDbryPHl76EAtKJRVCEAhjFnfnmLo7kUeVslBwAAwEj0rI9YcY91VCIHAAAAUHHpojUAj17HtNYsjuZS5C1LDgAAgJFavsA6KpEDAAAAQAMT0CRlKI7mUuQtSw4AAIBGTOq0jpIDAAAAoKCBCRh/ZSiO5lLkLUsOAACA0VyKvMrrKDkAAAAAttLABIyvMhRHcynyliUHAADAaExor+46Sg4AAACAbWhgAsZPGYqjuRR5y5Jj7dLGtwEAABiNXNZRcgAAAADsQAMTMD7KUBzNpchbphxdSxrfDgAAoMrrKDkAAAAAdqCBCdj1ylAczaXIW7YcM+Y1vi0AAECV11GVz9HX+DYAAABA9jQwAbtWKYqjuRR5S5hj5tzGtwcAAKjyOqrqOZYvaHw7AAAAIHsamIBdpyzF0VyKvDnkAAAAGCnrqDxz9HY3vi0AAACQPQ1MwK5RpuJoLkXeVs8BAAAwUtZR+eaYM7/x7QEAAIDsjaJiAdBixdFcirytnAMAAGCkrKPyztHWFtHT+G4AAACAvDkDEzC2ylgczaXI26o5AAAARso6qkYOAAAAoGI0MAFjJ5fiqByDNC8BAADjxTqqRg4AAACgglxCDhgbPesjVtzT+sXRXIq8ZcgBAAAwUtZRNXIAAAAAFeUMTMDYWL6g9YujuRR5y5ADAABgpKyjauQAAAAAKkwDEzA2JnW2dnE0lyJvGXIAAACMlHVUjRwAAABAxbmEHDA25sxv3eJoLkXeMuQAAAAYqbVLo61riXWUHAAAAADOwASMkQntrVkczaXIW4YcAAAADdC8JAcAAABAnQYmoDnKUBzNpchbhhwAAAAN6p8xr9rrKDkAAAAAttLABIy/MhRHcynyliEHAADAaMycW911lBwAAAAA29DABIyvMhRHcynyliJHX+PbAAAAjEY26yg5AAAAALangQkYP2UojuZS5C1LjuULGt8OAACgyusoOQAAAAB2oIEJGB9lKI7mUuQtU47e7sa3BQAAqPI6Sg4AAACAHWhgAna9MhRHcynyli3HnPmNbw8AAFDldVTVc/Ssb3wbAAAAIHsamIBdqwzF0VyKvGXM0TGt8X0AAABUeR1V9RwuRQ4AAAAMQwMTsOuUpTiaS5E3hxwAAAAjYR2Vb45JnY1vCwAAAGRPAxOwa5SpOJpLkbfVcwAAAIyEdVTeOVyKHAAAABiGBiZg7JWtOJpLkbeVcwAAAIyEdVQFcrQ3vg8AAAAgexqYgLFVyuJoLkXeFs0BAAAwEtZRg+QAAAAAKkYDEzB2cimOylGjeQkAABgv1lGD5AAAAAAqSOUAGBtrl0Zb15LWL47mUuQtQw4AAICRsI4aJAcAAABQUc7ABIwJzUtyAAAANMw6apAcAAAAQIVpYALGRP+Mea1dHM2lyFuGHAAAACOxpc86qk4OAAAAoOJUEYCxMXNu6xZHcynyliEHAADASC1fELG5xzpKDgAAAABnYAKapCzF0VyKvGXIAQAA0IjebusoOQAAAAAKGpiA8VeW4mguRd4y5AAAAGjUnPnVXkfJAQAAALCVBiZgfJWlOJpLkbcMOQAAAEajY1p111FyAAAAAGxDAxMwfspSHM2lyFuGHD3rG98GAABgNHJZR8kBAAAAsAMNTMD4KEtxNJcib1lyLF/Q+HYAAABVXkfJAQAAALADDUzArleW4mguRd4y5ZjU2fi2AAAAVV5HyQEAAACwAw1MwK5VluJoLkXesuWYM7/x7QEAAKq8jqp6jrVLG98GAAAAyJ4GJmDXKUtxNJcibylztDe+DwAAgEqvoyqeo2tJ49sBAAAA2dPABOwaZSqO5lLkzSEHAADASFhH5ZtjxrzGtwUAAACy56/PwNgrW3E0lyJvq+cAAAAYCeuovHNMnR3R0/guAAAAgLw5AxMwtspYHM2lyNvKOQAAAEbCOmqQHAAAAECFaGACxk4uxVE5BmleAgAAxot11CA5AAAAgIrRwASMjS19eRRHcynyliUHAADASFhHDZIDAAAAqCB/jQbGxvIFEZt7Wrs4mkuRtyw5AAAARsI6apAcAAAAQEU5AxMwNnq7W7s4mkuRtyw5AAAARsI6apAcAAAAQIX5qzQwNubMb93iaC5F3rLkAAAAGIme9REr7rGOSuQAAAAAKs4ZmICx0TGtNYujuRR5y5IDAACgkUuRW0fJAQAAAKCBCWiaMhRHcynyliUHAABAIyZ1WkfJAQAAAFDQwASMvzIUR3Mp8pYlBwAAwGguRV7ldZQcAAAAAFtpYALGVxmKo7kUecuSAwAAYDQmtFd3HSUHAAAAQPkamC644II46KCDorOzM44//vi47rrrdvr6r33ta/GYxzymeP3jHve4+N73vjducwUehTIUR3Mp8pY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            "text/plain": [
              "<Figure size 2400x1600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Plot Results\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import cm\n",
        "\n",
        "def plot_bar_chart(ax, data, labels, title, xlabel, ylabel, colors, bar_width, best_model=None):\n",
        "    index = np.arange(len(labels))\n",
        "    total_bars = len(labels) * len(data)\n",
        "\n",
        "    group_spacing = 0.4\n",
        "    group_width = bar_width * len(data)\n",
        "\n",
        "    # Get the scores for the \"random\" model\n",
        "    random_scores = data.get('Random', [0] * len(labels))\n",
        "\n",
        "    # Get the scores for the best model\n",
        "    best_model_scores = data.get(best_model, [0] * len(labels)) if best_model else [0] * len(labels)\n",
        "\n",
        "    for i, (model, metrics) in enumerate(data.items()):\n",
        "        positions = index + i * bar_width - group_width / 2 + group_spacing / 2\n",
        "        color = 'red' if model == 'Random' else colors[i]\n",
        "        bars = ax.bar(positions, metrics, width=bar_width, label=model, color=color, zorder=2)\n",
        "\n",
        "        # Check if the model is the best model\n",
        "        if model == best_model:\n",
        "            for bar in bars:\n",
        "                bar.set_hatch('//')\n",
        "                bar.set_edgecolor('#FF8C00')\n",
        "                height = bar.get_height()\n",
        "                ax.annotate(f'{int(100 * height)}%',\n",
        "                            xy=(bar.get_x() + bar.get_width() / 2, height),\n",
        "                            xytext=(0, 5),\n",
        "                            textcoords=\"offset points\",\n",
        "                            ha='center', va='bottom',\n",
        "                            color='orange', fontsize=10)\n",
        "\n",
        "        # Annotate the random model's bars\n",
        "        if model == 'Random':\n",
        "            for bar in bars:\n",
        "                height = bar.get_height()\n",
        "                ax.annotate(f'{int(100 * height)}%',\n",
        "                            xy=(bar.get_x() + bar.get_width() / 2, height),\n",
        "                            xytext=(0, 5),  # 5 points vertical offset\n",
        "                            textcoords=\"offset points\",\n",
        "                            ha='center', va='bottom',\n",
        "                            color='red', fontsize=10)\n",
        "\n",
        "    # Add a red horizontal line for the \"random\" model's scores\n",
        "    for idx, score in enumerate(random_scores):\n",
        "        ax.hlines(y=score, xmin=index[idx] - group_width / 2 + bar_width,\n",
        "                  xmax=index[idx] + group_width / 2 + bar_width, color='red', linestyle=':', linewidth=0.8, zorder=3)\n",
        "\n",
        "    # Add a green horizontal line for the \"best\" model's scores\n",
        "    for idx, score in enumerate(best_model_scores):\n",
        "        ax.hlines(y=score, xmin=index[idx] - group_width / 2 + bar_width,\n",
        "                  xmax=index[idx] + group_width / 2 + bar_width, color='orange', linestyle=':', linewidth=0.8, zorder=3)\n",
        "\n",
        "    ax.set_title(title)\n",
        "    ax.set_xlabel(xlabel)\n",
        "    ax.set_ylabel(ylabel)\n",
        "    ax.set_xticks(index + group_spacing / 2)\n",
        "    ax.set_xticklabels(labels, rotation=0, ha='center', fontsize=6)\n",
        "    ax.set_ylim(0, 1.19)\n",
        "    ax.legend(title='Model', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "\n",
        "    # Add a light grid in the background\n",
        "    ax.grid(True, linestyle='--', linewidth=0.5, alpha=0.7, zorder=1)\n",
        "\n",
        "def plot_score_metrics(model_results, n_samples):\n",
        "    # Find best model\n",
        "    best_model = max(\n",
        "        (model for model in model_results.keys()),\n",
        "        key=lambda model: model_results[model]['average_category_score'],\n",
        "        default=None\n",
        "    )\n",
        "\n",
        "    models = [m for m in model_results.keys()]\n",
        "    colors = cm.YlOrBr(np.linspace(0.4, 0.6, len(models)))\n",
        "\n",
        "    # Create a figure with a 2x1 grid of subplots\n",
        "    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(24, 16))\n",
        "    fig.suptitle(f'Scores (n_samples = {n_samples})')\n",
        "\n",
        "    # Categories Score\n",
        "    categories_score = {model: [model_results[model]['category_scores'][category] for category in model_results[model]['category_scores']] for model in models}\n",
        "    plot_bar_chart(axes[0], categories_score, list(model_results[models[0]]['category_scores'].keys()),\n",
        "                   'Category Scores', 'Category', 'Score', colors, 0.14, best_model)\n",
        "\n",
        "    # Average Category Score and Countries Score as subplots in the second row\n",
        "    average_category_score = {model: [model_results[model]['average_category_score']] for model in models}\n",
        "    countries_score = {model: [model_results[model]['country_score']] for model in models}\n",
        "\n",
        "    # Combine the two metrics into one subplot\n",
        "    combined_metrics = {model: average_category_score[model] + countries_score[model] for model in models}\n",
        "    plot_bar_chart(axes[1], combined_metrics, ['Average Category Scores', 'Country Scores'],\n",
        "                   'Average Category and Country Scores', 'Metric', 'Score', colors, 0.14, best_model)\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "plot_score_metrics(model_results, n_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvE5fQsbW1IQ"
      },
      "source": [
        "For this specific use case, most llms are struggling, this can be due to various reasons, bad prompting, small models, too specific use case...\n",
        "\n",
        "However, our finetuned classifier performs extremely well, outperforming all other models by a decent margin! Making it not only better, but also more efficient and cheaper, as a considerably smaller model compared to its older brothers."
      ]
    }
  ],
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