notebooks/EJ_classify.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnJEz0Z5Bl7-"
      },
      "source": [
        "# LLM example Usecase: Climate Indicator Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfElznN3Bl7_"
      },
      "source": [
        "### Install Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCu_7LgYhlmi",
        "outputId": "5fe69672-0a5b-4c79-a462-084122c567a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Using cached openpyxl-3.1.2-py2.py3-none-any.whl (249 kB)\n",
            "Using cached et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: et-xmlfile, openpyxl\n",
            "Successfully installed et-xmlfile-1.1.0 openpyxl-3.1.2\n",
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install pandas accelerate transformers torch scikit-learn marvin openpyxl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YgiS4JKjBl8A",
        "outputId": "eb36aff2-67b9-4e8e-a111-4be85a51730c"
      },
      "outputs": [
        {
          "data": {
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              "\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>Indicators</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Health &amp; Air Quality</td>\n",
              "      <td>The Global 3-Year Running Mean Ground-Level Ni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Food Availability</td>\n",
              "      <td>The NASA Making Earth System Data Records for ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Extreme Heat</td>\n",
              "      <td>Within the viewer, select the parameter or fie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Human Dimensions</td>\n",
              "      <td>The INFORM Global Risk Index 2019 Mid Year, v0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Health &amp; Air Quality</td>\n",
              "      <td>The Global 3-Year Running Mean Ground-Level Ni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>Health &amp; Air Quality</td>\n",
              "      <td>The Daily and Annual PM2.5 Concentrations for ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>Human Dimensions</td>\n",
              "      <td>The Global Gridded Geographically Based Econom...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>Disasters</td>\n",
              "      <td>This dataset provides maps at 30-m resolution ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>Health &amp; Air Quality</td>\n",
              "      <td>Measurements of Pollution in the Troposphere (...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>Climate Change</td>\n",
              "      <td>This dataset contains the Global Mean Sea Leve...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 2 columns</p>\n",
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            ],
            "text/plain": [
              "               Indicators                                        Description\n",
              "0    Health & Air Quality  The Global 3-Year Running Mean Ground-Level Ni...\n",
              "1       Food Availability  The NASA Making Earth System Data Records for ...\n",
              "2            Extreme Heat  Within the viewer, select the parameter or fie...\n",
              "3        Human Dimensions  The INFORM Global Risk Index 2019 Mid Year, v0...\n",
              "4    Health & Air Quality  The Global 3-Year Running Mean Ground-Level Ni...\n",
              "..                    ...                                                ...\n",
              "247  Health & Air Quality  The Daily and Annual PM2.5 Concentrations for ...\n",
              "248      Human Dimensions  The Global Gridded Geographically Based Econom...\n",
              "249             Disasters  This dataset provides maps at 30-m resolution ...\n",
              "250  Health & Air Quality  Measurements of Pollution in the Troposphere (...\n",
              "251        Climate Change  This dataset contains the Global Mean Sea Leve...\n",
              "\n",
              "[252 rows x 2 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "excel_file_path = \"../data/ej_dataset.xlsx\" # you can get this file from: https://docs.google.com/spreadsheets/d/1Fk_WKMAYUz3Yl49QspZDGXrEMJq9pmAF/edit?usp=sharing&ouid=107577045329506013851&rtpof=true&sd=true\n",
        "df = pd.read_excel(excel_file_path, engine=\"openpyxl\")\n",
        "\n",
        "df = df.dropna()\n",
        "df.sample(frac=1).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CF2W9gGgBl8B"
      },
      "source": [
        "## Load the Encoder Model using Huggingface package\n",
        "\n",
        "### Tokenizer Initialization:\n",
        "Initializes the tokenizer using the specified model_name. The tokenizer is responsible for converting text input into a format that is understandable by the model, including tasks like splitting the text into tokens, converting tokens to their corresponding IDs in the model's vocabulary, and applying any model-specific preprocessing steps.\n",
        "\n",
        "### Model Initialization:\n",
        "Loads a pre-trained model for sequence classification from the given model_name, specifying that the model should be configured to handle 8 distinct labels. This model is designed for tasks like sentiment analysis or topic classification, where the goal is to categorize text sequences into one of several categories.\n",
        "\n",
        "### Data Collator Setup:\n",
        "Creates a data collator that automatically pads tokenized inputs to the longest sequence in a batch, ensuring consistent input sizes for model training or evaluation. The DataCollatorWithPadding uses the tokenizer's padding token to fill any shorter sequences up to the length of the longest sequence in the batch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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            "dd0ba0c082a44b65b43dd211f7fc7c2c",
            "4b2e2f66940445479988f040aa64e6ac",
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            "b56a982539be464784f03397212e7754",
            "0ddc1a6393d84f2ca193b63fb54b1f00",
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            "09eac7dcf75b42aa83a6b85b08e78770",
            "f6d90b4c5bfd47469d8d906091e6b3c9"
          ]
        },
        "id": "mhgp53AHBl8B",
        "outputId": "ee7cf8ae-d691-4822-8d3f-efb920e6d234"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, DataCollatorWithPadding\n",
        "\n",
        "# Load your domain-specific encoder model (replace 'model_name' with your model's name)\n",
        "model_name = 'distilbert-base-uncased'\n",
        "# model_name = 'nasa-impact/nasa-smd-ibm-v0.1'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=8)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ikaAGx8MBl8B"
      },
      "source": [
        "## Encode the data and Labels into ML-ready format\n",
        "\n",
        "### Tokenization and Model Preparation\n",
        "This following code block initializes a tokenizer and a `sequence classification` model from pre-trained model initialized in the previous step. The tokenizer is used to convert text into a format that the model can understand, while the model is set up for sequence classification tasks with a specified number of labels (num_labels=8). Additionally, a `data collator` is initialized to dynamically pad the tokenized inputs to the maximum length in a batch, ensuring consistent input sizes for model training.\n",
        "\n",
        "### Encoding Labels and Preparing Datasets\n",
        "The second code block imports necessary libraries and defines a custom dataset class, CustomDataset, for handling the encoding of text and labels. It utilizes LabelEncoder from scikit-learn to convert text labels into numerical form. The dataset is then split into training and testing sets, with text descriptions tokenized and attention masks generated using the previously initialized tokenizer. The custom dataset class takes these encodings and creates PyTorch compatible datasets for both training and testing, suitable for loading into a model for training or evaluation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y86rA9MFBl8B",
        "outputId": "989294f6-76fe-4264-c991-749904b37eda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Climate Change -> 0\n",
            "Disasters -> 1\n",
            "Extreme Heat -> 2\n",
            "Food Availability -> 3\n",
            "Health & Air Quality -> 4\n",
            "Human Dimensions -> 5\n",
            "Urban Flooding -> 6\n",
            "Water Availability -> 7\n"
          ]
        }
      ],
      "source": [
        "## encode labels\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "# Create a custom dataset class\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return {\n",
        "            'input_ids': self.encodings['input_ids'][idx],\n",
        "            'attention_mask': self.encodings['attention_mask'][idx],\n",
        "            'labels': self.labels[idx]\n",
        "        }\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "df['labels'] = label_encoder.fit_transform(df['Indicators'])\n",
        "# print the label mapping\n",
        "for index, item in enumerate(label_encoder.classes_):\n",
        "    print(item, '->', index)\n",
        "\n",
        "data = df\n",
        "train_data, test_data = train_test_split(data, test_size=0.2, random_state=42, stratify=data['labels'])\n",
        "train_encodings = tokenizer(list(train_data['Description']), return_tensors='pt', padding=True, truncation=True, max_length=512, return_attention_mask=True)\n",
        "test_encodings = tokenizer(list(test_data['Description']), return_tensors='pt', padding=True, truncation=True, max_length=512, return_attention_mask=True)\n",
        "train_dataset = CustomDataset(train_encodings, torch.tensor(train_data['labels'].tolist()))\n",
        "test_dataset = CustomDataset(test_encodings, torch.tensor(test_data['labels'].tolist()))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JqhedgJmBl8C"
      },
      "source": [
        "## Create training and evaluation arguments for the Huggingface Trainer\n",
        "\n",
        "### Training Configuration and Initialization\n",
        "This code block sets up the training arguments for fine-tuning a sequence classification model using the Hugging Face Transformers Trainer API. It configures various parameters such as batch size, evaluation strategy, output directory, number of training epochs, logging directory, optimizer type (adamw_torch), and learning rate.\n",
        "    a Trainer object is instantiated with the model, training arguments, training and evaluation datasets, data collator, and a custom function to compute metrics using the classification_report from scikit-learn. The Trainer is responsible for managing the training and evaluation processes, including batching, optimization, and logging.\n",
        "\n",
        "Finally, the trainer is used to fine-tune the model on the training dataset and evaluate its performance on the testing dataset. The resulting metrics, including precision, recall, and F1 score for each class, are printed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nEBTd3aMBl8C",
        "outputId": "086882fb-5d39-4a30-8c85-dbe575c9e510"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    output_dir=\"./output\",\n",
        "    num_train_epochs=1,\n",
        "    save_steps=50,\n",
        "    save_total_limit=2,\n",
        "    remove_unused_columns=True,\n",
        "    logging_dir=\"./logs\",\n",
        "    optim=\"adamw_torch\",\n",
        "    learning_rate=5e-5,\n",
        "    overwrite_output_dir=True,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=lambda p: classification_report(p.label_ids, p.predictions.argmax(-1), output_dict=True),\n",
        ")\n",
        "\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "results = trainer.evaluate()\n",
        "\n",
        "# Print classification report\n",
        "print(\"Classification Report:\")\n",
        "print(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6xUU6vvBl8C"
      },
      "source": [
        "## Evaluation on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvBko-K2Bl8C",
        "outputId": "46ab5ab1-8367-44b6-e785-54863c88c29a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.23960325121879578,\n",
              " 'eval_0': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 4},\n",
              " 'eval_1': {'precision': 1.0,\n",
              "  'recall': 0.3333333333333333,\n",
              "  'f1-score': 0.5,\n",
              "  'support': 3},\n",
              " 'eval_2': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6},\n",
              " 'eval_3': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3},\n",
              " 'eval_4': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13},\n",
              " 'eval_5': {'precision': 0.8947368421052632,\n",
              "  'recall': 1.0,\n",
              "  'f1-score': 0.9444444444444444,\n",
              "  'support': 17},\n",
              " 'eval_6': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 4},\n",
              " 'eval_7': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1},\n",
              " 'eval_accuracy': 0.9607843137254902,\n",
              " 'eval_macro avg': {'precision': 0.986842105263158,\n",
              "  'recall': 0.9166666666666666,\n",
              "  'f1-score': 0.9305555555555556,\n",
              "  'support': 51},\n",
              " 'eval_weighted avg': {'precision': 0.9649122807017544,\n",
              "  'recall': 0.9607843137254902,\n",
              "  'f1-score': 0.9520697167755992,\n",
              "  'support': 51},\n",
              " 'eval_runtime': 0.7381,\n",
              " 'eval_samples_per_second': 69.097,\n",
              " 'eval_steps_per_second': 2.71,\n",
              " 'epoch': 10.0}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZOK18DsBl8C"
      },
      "source": [
        "## Saving the model for later use"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQiq8FzYBl8D",
        "outputId": "6d47a61e-4494-456d-c660-7d59ddc2d3d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('ej_tokenizer/tokenizer_config.json',\n",
              " 'ej_tokenizer/special_tokens_map.json',\n",
              " 'ej_tokenizer/vocab.txt',\n",
              " 'ej_tokenizer/added_tokens.json',\n",
              " 'ej_tokenizer/tokenizer.json')"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.save_pretrained('ej_classifier')  # Replace 'your_model_directory' with your desired directory\n",
        "tokenizer.save_pretrained('ej_tokenizer')  # Save the tokenizer as well\n",
        "\n",
        "# load the model\n",
        "# from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "# model = AutoModelForSequenceClassification.from_pretrained('ej_classifier')\n",
        "# tokenizer = AutoTokenizer.from_pretrained('ej_tokenizer')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_w5-8w1Bl8D"
      },
      "source": [
        "## Predicting using the saved model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjhe7QtlBl8D",
        "outputId": "a233d10e-a152-4f39-a3b5-0490775bd7ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "This monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2).  The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1.\n",
            "True: Extreme Heat\n",
            "Predicted: Extreme Heat\n",
            "\n",
            "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-12-31\n",
            "\n",
            "True: Food Availability\n",
            "Predicted: Food Availability\n",
            "\n",
            "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.\n",
            "True: Extreme Heat\n",
            "Predicted: Extreme Heat\n",
            "\n",
            "Terrestrial AOD data (3km resolution, merged algorithm)\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The SEDAC Hazards Mapper enables users to visualize data and map layers related to Socioeconomic, Infrastructure, Natural Disasters, and Environment and analyze potential impacts and exposure. The web app applies layers from various sources including SEDAC, NASA LANCE, NASA GIBS, USGS, NOAA, ESRI, and others.\n",
            "True: Disasters\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the \"defenselessness\" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The U.S. Census Grids (Summary File 1), 2010 data set contains grids of demographic and socioeconomic data from the year 2010 in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2010 TIGER/Line Files and census variables (population, households, and housing variables).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI ﬁeld of view speciﬁc geometry dependent surface Lambertian Equivalent Reﬂectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (eﬀective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain reﬂectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. \n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016 consist of annual concentrations (micrograms per cubic meter) of ground-level fine particulate matter (PM2.5), with dust and sea-salt removed. This data set combines AOD retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "AIRS measures abundances of trace components in the atmosphere including CO. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of CO in the total vertical column profile of the atmosphere (from Earth’s surface to top-of-atmosphere). Data are in HDF format, and can be opened using Panoply.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "Through a combination of airborne radar and ground-based GPS, a research team has developed detailed models of how much land is sinking and rising in southern Louisiana.\n",
            "True: Urban Flooding\n",
            "Predicted: Urban Flooding\n",
            "\n",
            "The AIRS Level 1B Near Real Time (NRT) product (AIRIBRAD_NRT_005) differs from the routine product (AIRIBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. \n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface skin temperature, the air temperature at 2 m, and the air temperature at 10 m.\n",
            "True: Extreme Heat\n",
            "Predicted: Extreme Heat\n",
            "\n",
            "The U.S. Social Vulnerability Index Grids data set contains gridded layers for the overall Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) using four sub-category themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) based on census tract level inputs from 15 variables for the years 2000, 2010, 2014, 2016, and 2018. SVI values range between 0 and 1 based on their percentile position among all census tracts in the U.S., with 0 representing lowest vulnerability census tracts and 1 representing highest vulnerability census tracts. SEDAC has gridded these vector inputs to create 1 km spatial resolution raster surfaces allowing users to obtain vulnerability metrics for any user-defined area within the U.S. Utilizing inputs from CIESIN's Gridded Population of the World, Version 4, Revision 11 (GPWv4.11), a mask is applied for water, and optionally, for no population. The data are provided in two different projection formats, NAD83 as a U.S. specific standard, and WGS84 as a global standard. The goal of the SVI is to help identify vulnerable communities by ranking them on these inputs across the U.S.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "NASA's Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite gathers infrared energy emitted from Earth's surface and atmosphere globally every day. AIRS data are daily, 8-day, and monthly at 1 degree and the Level 3 data products are provided in either the descending (equatorial crossing North to South at 1:30 a.m. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. When you open the file in HDF format (in a program like Panoply or QGIS), you will see an ascending option and a descending option each with SurfAirTemp.\n",
            "True: Extreme Heat\n",
            "Predicted: Extreme Heat\n",
            "\n",
            "The OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. This is now the official NASA aerosol index product, replacing the aerosol index found in the OMPS-NPP L2 NM Total Ozone product. The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "This dataset contains along track Sea Surface Height Anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit. Altimeter data from the multi-mission Geophysical Data Records (GDRs) have been interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. Version 5.0 updates include improved Precise Orbit Determination (POD) with GSFC std2006 standards, and the application of internal tides.\n",
            "True: Climate Change\n",
            "Predicted: Climate Change\n",
            "\n",
            "The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "(daily global data coverage)\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC?s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC?s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).\n",
            "True: Extreme Heat\n",
            "Predicted: Extreme Heat\n",
            "\n",
            "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The Global Gridded Geographically Based Economic Data (G-Econ), Version 4 contains derived one degree grid cells of Gross Domestic Product (GDP) data in Grid and ASCII formats for both Market Exchange Rate (MER) and Purchasing Power Parity (PPP) for the years 1990, 1995, 2000 and 2005. MER is the exchange rate between local and U.S. dollar currencies for a given time period established by the market. PPP is the exchange rate between a country's currency and U.S. dollars adjusted to reflect the actual cost in U.S. dollars of purchasing a standardized market basket of goods in that country using the country's currency. The original data from the G-Econ Project at Yale University is also available in tabular format and includes latitude and longitude geographic coordinates of the grid cells, area of grid cells, as well as country names, distance to coast, elevation, vegetation, population, precipitation and temperature.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.\n",
            "True: Water Availability\n",
            "Predicted: Water Availability\n",
            "\n",
            "This dataset contains along track Sea Surface Height Anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit. Altimeter data from the multi-mission Geophysical Data Records (GDRs) have been interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. Version 5.0 updates include improved Precise Orbit Determination (POD) with GSFC std2006 standards, and the application of internal tides.\n",
            "True: Climate Change\n",
            "Predicted: Climate Change\n",
            "\n",
            "The SEDAC Hazards Mapper enables users to visualize data and map layers related to Socioeconomic, Infrastructure, Natural Disasters, and Environment and analyze potential impacts and exposure. The web app applies layers from various sources including SEDAC, NASA LANCE, NASA GIBS, USGS, NOAA, ESRI, and others.\n",
            "True: Disasters\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "Copernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Global Multihazard Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. (2003). Where applicable, the contributions of subnational units to national GDP estimates, the contribution ratio, are determined using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is generated by summing the top three deciles of the individual hazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 minute grid of cyclone hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Global Multihazard Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based economic loss risk as a proportion of the economic productivity of the analytical unit, the grid cell. Representation of multihazard risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and landslides are included in the hydro category. For each of the six hazards considered, a binary risk surface is constructed utilizing the three most-at-risk deciles of each hazard's global proportional economic loss risks data set (deciles 8-10). Each of the category risk surfaces are constructed by adding all the relevant hazard high-risk surfaces. These categorical risk surfaces are reclassified into binary high-risk surfaces. The combination of the category risk values forms a three digit identifier for determining those locations that are at higher-risk from multihazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the \"defenselessness\" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The INFORM Global Risk Index 2019 Mid Year, v0.3.7 data set identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM Global Risk Index (GRI) model is based on risk concepts published in the scientific literature and envisages three dimensions of risk: Hazard & Exposure, Vulnerability, and Lack of Coping Capacity. The INFORM GRI model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM GRI model supports a proactive crisis management framework, and will be helpful for an objective allocation of resources for disaster management, as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. Only the two main sections, Vulnerability and Lack of Coping Capacity, not the Hazard & Exposure section, were used in the IPCC AR6.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Flood Dashboard brings together multiple NASA soil moisture and flood products with products from the National Weather Service and USGS to give a more complete picture of potential flooding in the United States. Includes data from Soil Moisture Map, USGS Stream Gauges, and MODIS Flood Maps.\n",
            "True: Urban Flooding\n",
            "Predicted: Urban Flooding\n",
            "\n",
            "The TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel 5, is an ESA Mission. ESA's TROPOMI NO2 provides additional information on this level 2 data product. It is important to note that, because of the very small numbers in tropospheric vertical column of NO2, you will need to change the scaling factor in Panoply (see image from June 2018 to right). Data are in NetCDF format, and can be opened using Panoply.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The FLDAS Global model (McNally et al. 2017) is a custom instance of the NASA Land Information System (LIS; http://lis.gsfc.nasa.gov/) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, ET, and other variables based on multiple meteorological inputs or land surface models. The goal of the FLDAS project is to achieve more effective use of limited available hydroclimatic observations and is designed to be adopted for routine use for FEWS NET decision support.\n",
            "True: Food Availability\n",
            "Predicted: Food Availability\n",
            "\n",
            "The Natural Resource Protection and Child Health Indicators, 2021 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 135 countries and is calculated based on the weighted average percentage of biomes under protected status.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper \"Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter\" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).\n",
            "True: Urban Flooding\n",
            "Predicted: Urban Flooding\n",
            "\n",
            "Facebook collaborates with SEDAC on data; high resolution and updated frequently\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "The Precipitation Processing System (PPS) evolved from the Tropical Rainfall Measuring Mission (TRMM) Science Data and Information System (TSDIS). The purpose of the PPS is to process, analyze and archive data from the Global Precipitation Measurement (GPM) mission, partner satellites and the TRMM mission. The PPS also supports TRMM by providing validation products from TRMM ground radar sites.  All GPM, TRMM and Partner public data products are available to the science community and the general public from the TRMM/GPM FTPS and HTTPS Data Archives.\n",
            "True: Urban Flooding\n",
            "Predicted: Urban Flooding\n",
            "\n",
            "The MLS Nitric Acid (HNO3) Mixing Ratio at 46hPa layer indicates nitric acid levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv). It is derived from the MLS Nitric Acid (ML2HNO3_NRT) MLS/Aura NRT L2 HNO3 Mixing Ratio\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour. Animate shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For those variables near the surface, make sure to select 850 as your level.\n",
            "True: Extreme Heat\n",
            "Predicted: Extreme Heat\n",
            "\n",
            "This dataset provides daily-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense.\n",
            "True: Climate Change\n",
            "Predicted: Climate Change\n",
            "\n",
            "ESA TROPOMI O3 provides additional information on this level 2 data product. Data are in NetCDF format, and can be opened using Panoply.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The AERONET Data Display Interface allows users to find and download ground-based AOD (Aerosol Optical Depth, quantity of light removed from a beam by scattering or absorbing during its path through a medium and is a unitless measure) data for locations all across the world. Data are available from years 1993-2022, dependent on location.\n",
            "True: Health & Air Quality\n",
            "Predicted: Health & Air Quality\n",
            "\n",
            "The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response.\n",
            "True: Disasters\n",
            "Predicted: Disasters\n",
            "\n",
            "The VIIRS Plus DMSP Change in Lights (VIIRS+DMSP dLIGHT) data set fuses nighttime lights imagery from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) with a stable night light composite from the next generation Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band to map the spatial distribution and temporal evolution of global nighttime lights between 1992 and 2015. The product visualizes changes in both brightness and extent of nocturnal low lights over two decades while minimizing the spatial overextent (overglow) and bright saturation that compromise the DMSP-OLS composites.\n",
            "True: Human Dimensions\n",
            "Predicted: Human Dimensions\n",
            "\n",
            "FEWS NET, the Famine Early Warning Systems Network, is a leading provider of early warning and analysis on acute food insecurity around the world. Created in 1985 by the United States Agency for International Development (USAID) in response to devastating famines in East and West Africa, FEWS NET provides unbiased, evidence-based analysis to governments and relief agencies who plan for and respond to humanitarian crises. FEWS NET analyses support resilience and development programming as well. FEWS NET analysts and specialists work with scientists, government ministries, international agencies, and NGOs to track and publicly report on conditions in the world’s most food-insecure countries.\n",
            "True: Food Availability\n",
            "Predicted: Food Availability\n",
            "\n",
            "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 4.2 and version 5.x releases are described in detail in the user handbook.\n",
            "True: Climate Change\n",
            "Predicted: Climate Change\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import torch\n",
        "import pandas as pd\n",
        "\n",
        "# Load the saved model and tokenizer\n",
        "model = AutoModelForSequenceClassification.from_pretrained('ej_classifier')  # Load from the directory where you saved it\n",
        "tokenizer = AutoTokenizer.from_pretrained('ej_tokenizer')\n",
        "\n",
        "# Get predictions\n",
        "with torch.no_grad():\n",
        "    outputs = model(**test_encodings)\n",
        "\n",
        "# Extract predicted class labels\n",
        "predicted_labels = torch.argmax(outputs.logits, dim=1)\n",
        "\n",
        "predicted_class_labels = label_encoder.inverse_transform(predicted_labels.numpy())\n",
        "\n",
        "# print the text, true and predicted labels\n",
        "for i in range(len(test_data)):\n",
        "    print(test_data['Description'].iloc[i])\n",
        "    print('True:', test_data['Indicators'].iloc[i])\n",
        "    print('Predicted:', label_encoder.classes_[predicted_labels[i]])\n",
        "    print('')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5U6zwUYMBl8D"
      },
      "source": [
        "# using OpenAI GPT 3.5 to do the same task (using Marvin AI)\n",
        "\n",
        "## Marvin AI \n",
        "Marvin AI is a lightweight AI toolkit designed for building python interfaces using natural language, powered by LLMs. \n",
        "Marvin can be used for classification, entity extraction and other generation tasks using python type hints and docstrings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KbtRJL-BBl8D"
      },
      "outputs": [],
      "source": [
        "from typing import Literal, List\n",
        "from pprint import pprint\n",
        "import marvin\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import dotenv\n",
        "\n",
        "dotenv.load_dotenv()\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "# initialize Marvin\n",
        "marvin.settings.openai.chat.completions.model = 'gpt-3.5-turbo'\n",
        "marvin.settings.openai.api_key  = os.getenv('OPENAI_API_KEY')\n",
        "labels = tuple(label_encoder.classes_)\n",
        "\n",
        "\n",
        "# Define a function to classify the descriptions\n",
        "@marvin.fn\n",
        "def data_description_classifier(descriptions: str) ->  Literal[labels]:\n",
        "    \"\"\"\n",
        "    classify `descriptions` into one or more of the labels in the Literal\n",
        "    \"\"\"\n",
        "\n",
        "# Define a function to classify the descriptions, by providing a few examples\n",
        "@marvin.fn\n",
        "def data_description_classifier_few_shot(descriptions: str) ->  Literal[labels]:\n",
        "    \"\"\"\n",
        "    classify `descriptions` into one or more of the labels in the Literal\n",
        "    examples:\n",
        "    The Flood Dashboard brings together multiple NASA soil moisture and flood products with products from the National Weather Service and USGS to give a more complete picture of potential flooding in the United States. Includes data from Soil Moisture Map, USGS Stream Gauges, and MODIS Flood Maps. -> Urban Flooding\n",
        "    The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. -> Human Dimensions\n",
        "    The OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. -> Health & Air Quality\n",
        "    Landsat surface temperature measures the Earth’s surface temperature in Kelvin -> Extreme Heat\n",
        "    This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1 -> Climate Change\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "def accuracy(predicted_label: str, true_label: str) -> float:\n",
        "    \"\"\"\n",
        "    calculate the accuracy of the predicted label\n",
        "    \"\"\"\n",
        "    return 1 if predicted_label == true_label else 0\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1VG3JQIBl8D",
        "outputId": "0f331960-d3ae-4f41-a3d7-056982d8b456"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "51it [00:53,  1.06s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Accuracy with OpenAI GPT 3.5: 0.54\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# create empty dataframe\n",
        "df_result = pd.DataFrame(columns=['description', 'predicted_labels', 'true_labels', 'accuracy'])\n",
        "num_records = len(df)\n",
        "for i, data in tqdm(enumerate(df.to_dict(orient='records'))):\n",
        "    try:\n",
        "        predicted_category = data_description_classifier(\n",
        "            data['Description'],\n",
        "        )\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "        predicted_category = None\n",
        "    df_result.loc[i] = [data['Description'], predicted_category, data['Indicators'], accuracy(predicted_category, data['Indicators'])]\n",
        "    if i > 0.2*num_records:\n",
        "        break\n",
        "\n",
        "average_accuracy = df_result['accuracy'].mean()\n",
        "print(f\"Average Accuracy with OpenAI GPT 3.5: {average_accuracy:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YM2MKdfiBl8D"
      },
      "source": [
        "## Few Shot Learning with GPT-3.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sb6TXJD8Bl8D",
        "outputId": "9c331da2-f1e8-4b1a-eff0-5db3f0a7f5c1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "51it [00:51,  1.02s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Accuracy with OpenAI GPT 3.5 (few shot): 0.62\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "df_result = pd.DataFrame(columns=['description', 'predicted_labels', 'true_labels', 'accuracy'])\n",
        "num_records = len(df)\n",
        "for i, data in tqdm(enumerate(df.to_dict(orient='records'))):\n",
        "    try:\n",
        "        predicted_category = data_description_classifier_few_shot(\n",
        "            data['Description'],\n",
        "        )\n",
        "    except Exception as e:\n",
        "        # print(e)\n",
        "        predicted_category = None\n",
        "    df_result.loc[i] = [data['Description'], predicted_category, data['Indicators'], accuracy(predicted_category, data['Indicators'])]\n",
        "    if i > 0.2*num_records:\n",
        "        break\n",
        "\n",
        "average_accuracy_few_shot = df_result['accuracy'].mean()\n",
        "print(f\"Average Accuracy with OpenAI GPT 3.5 (few shot): {average_accuracy_few_shot:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "Iq-aCRihBl8E",
        "outputId": "efe0b83a-4ca0-4300-cbb0-62353c78300b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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nmEwmNW/eXFu3brUa1vTNN9+obNmyiZ4lMJlMlj558+ZV165dk+yTFp07d7Zpe/DXvsjISN24cUNVq1aV2WzW33//Len+WPpff/1V7du3V5EiRZKsp02bNoqJibEazrJ161bdu3dPrVu3Tra2PXv2yMfHxzKlsSTlzJlTnTp1UmhoqP79999H29hUevg9qV69utWQle3btyt37tyqW7eubty4YfmvQoUKcnZ2fqSZCFu1aqWzZ89aJjs5evSo5UzEw/bu3St3d3erIYMODg7q1q2bIiMj9euvv1r6ZcuWzWo77O3tbf52bt26pQMHDqhZs2a6ffu2ZTtu3rypevXq6cyZMzZD9RLkypVLkvTjjz/aDH9Lzp9//qnr16+rc+fOVtesNGzYUJ6enlZDPhOk9Hkkx8PDQ59//rnNf8mdRUmLgIAAq8c1atTQrVu3dPv2bUn3/5Yl2QwPS00d+/bt0507d9SnTx+b63we/K6l5nubnL1798re3t6mxl69eslsNmvv3r1W7TVr1lSZMmWs2lxcXHT37t0Uh249LOF9ypkz5yMt96AHtz8iIkI3btxQrVq1dP78ectQ1YQzqrt371ZsbGyi60nrNqRWavflD25PWFiYIiIiVL169VR9lon56aefFBsbq+7du8vO7v8OBTt27KhcuXJZ/kYTODs7W51JdHR0VKVKlXT+/Pk0vX6CB7crNjZWN2/eVPHixeXi4mK1bS4uLjpx4oTOnDmT4joPHDig119/Xb6+vgoODs6w69zw7GJYIPAExMXFacuWLapdu7bVgaCPj48WL16s/fv3q169epKkc+fOqUmTJsmu79y5cypVqpSyZTPuK5wtWzYVKlTIpv3ixYuaNWuWdu3aZXMNUcJBUMI/sF5eXsm+RunSpVWpUiWFhIRYpqIPCQlRlSpVUpw18eLFi6pcubJNu6enp+X5lF7/UWXPnl1ubm5Wba6urlbvw9mzZxUREWG5futhCRe2p0b58uXl6empzZs3y8XFRe7u7nr++ecT7RsaGqoSJUpYHRhJ999j6f77kdDP3d3d5kC1VKlSVo/PnTsns9msmTNnaubMmUluS8GCBW3aixUrptdee02ff/65QkJCVKNGDfn5+al169bJDglMqPHhWqT7n+tvv/1m1ZaazyM5zs7OqlOnTqr6Po6Hf2BIGNoYFhamXLlyKTQ0VHZ2djZDthL+lpOTcB3Oc889l2y/1HxvkxMaGqoCBQpYgnOChL+v0NBQq/aiRYvarOPVV1/Vtm3b9MYbb6hgwYKqW7eumjVrpgYNGiT72gmveefOnTRPTPPbb78pODhYv//+u03gj4iIUO7cuVWrVi01bdpUs2fP1pIlS1SrVi299NJLatWqleWAPK3bkFqp3Zd///33mjt3ro4dO6aYmBhLe1p/TEv47j38N+fo6KhixYrZfL6FChWyeS1XV1cdP348Ta+fICoqSvPnz9e6det05coVq+v5Hrxec/Dgwerfv7+aNm0qLy8v1atXT23atLGaMEO6f/1a3759VaFCBX3yySeG/hsJpBZ/dcATcODAAV27dk1btmzRli1bbJ4PCQmxhCujJPWPbnx8fKLtjo6ONgfqcXFxeu211yzj7T09PeXs7KwrV64oMDAwyXUlp23btpo4caIuX76smJgY/f777xozZswjr+dJSM3sWvHx8cqXL5+mT5+e6PMPh4GUJFyLlzNnTjVr1szmM0kvCZ9lr169krwvWHLXbwQGBurll1/Wd999p3379mnChAmaP3++vvzyy0RDe1pklVnzkvrMzA9NBJFe0uN7m5LErmfJly+fNmzYoB9//FF79+7V3r17tW7dOrVt21ZTp05Ncl0JB/z/+9//rM5Up9a5c+fUs2dPeXp6KjAwUIULF5aDg4P27NmjJUuWWLbfZDJp1qxZ+v333/X999/rhx9+0DvvvKPPP/9ca9asUc6cOdO8DUY6ePCg+vXrp5o1a+qDDz6Qu7u7HBwctHbtWm3evPmJ1JBe373x48dr3bp16tGjh6pUqaLcuXPLZDJp2LBhVt+XmjVraufOnZb9y9dff62lS5dq3LhxVveMdHR0VIMGDbRr1y798MMPatSoUbrUDSSHcAU8ASEhIcqXL1+iIWLnzp3auXOnxo0bJycnJxUvXlwnTpxIdn3FixfXH3/8odjY2CQnS0j4xffh2doe/kUyOf/73/905swZTZ06VW3btrW0PzxEplixYpb+KWnevLmmTJmizZs3KyoqSg4ODmrWrFmKyxUpUiTRG4eeOnXK8vyjepwhlAmKFy+u/fv3q1q1aoZcMN2qVSvNmjVL165d07Rp05Ls5+HhoePHjys+Pt7qYP7h98PDw0MHDhzQnTt3rM5ePfxeJnyGDg4OaT67kzBDWf/+/XXo0CF17txZq1at0rBhwxLtn1Dj6dOnbc78nT59Ok2faVbg4eGh+Ph4nTt3zurMQcJnl5yEgHvixIkkz/am9nsrJf0d8PDw0P79+3X79m2rs1cJNXp4eKRYq3T/YNfPz09+fn6Kj4/X2LFjtWbNGvXv3z/J+hs1aqT58+dr06ZNaQpXu3btUkxMjObOnWv1N5TUEN0qVaqoSpUqGjZsmEJCQjRixAht3brVctCelm1IrdTsy3fs2KHs2bNr0aJFVkPc1q5dm+bXTXhfTp06ZfnuS/cnp7lw4cITOcMr3d+2tm3bWmY1lO6ffUpsltE8efKoffv2at++ve7cuaOuXbsqODjYKlyZTCZNnz5d/fv315AhQ7Rw4cIMv4chnj1ccwWks6ioKH3zzTdq2LCh/P39bf7r0qWL7ty5Y5khq0mTJvrnn38SnbI84Ze8Jk2a6ObNm5ZplBPr4+HhIXt7e8u1NwlWrVqV6toTDtof/AXRbDZr2bJlVv3c3NxUs2ZNrV271jLc5OF6Huxbv359bdq0yXLGLjVnd1544QUdOXLEarr2yMhIffnll/Lw8LC53iM1Emaienjq4UfRrFkzxcXF6dNPP7V57t69e4+87uLFi+udd97RW2+9ZTWL5MMaNGiga9euaevWrVavt3z5cjk7O6tmzZqWfvfu3bP63OPi4rRixQqr9eXLl0+1atXSmjVrdPXqVZvXS2yWvQS3b9/WvXv3rNq8vLxkZ2dnNYTpYRUrVlS+fPm0evVqq3579uzRyZMnE50l8WmQMKRs+fLlVu0pTVEuSfXq1VPOnDk1f/58RUdHWz2X8F1L7fdWSvo70KBBA8XFxdnsY5YsWSKTyZSqYXE3b960emxnZ2eZNS+5v4uqVauqfv36+uqrr/Ttt9/aPB8TE5PsWaOEsywPDzF7OIyEhYXZ7J/KlStnVV9atyG1UrMvt7e3l8lksro298KFC4neIsHZ2TlV+5w6derIwcFBy5cvt3oPvv76a0VERDzWDICPIrEzYsuXL7e5vcLDn0POnDlVvHjxRD8DR0dHzZ49W5UqVdKbb76pI0eOGFs0kALOXAHpbNeuXbpz5478/PwSfb5KlSpyc3PTpk2b1Lx5c/Xu3Vs7duzQkCFD1L59e1WoUEFhYWHatWuXxo0bp7Jly6pt27basGGDJk+erCNHjqh69eq6e/eu9u/fr86dO+ull15S7ty55e/vrxUrVshkMqlYsWLavXv3I10D5OnpqeLFi2vq1Km6cuWKcuXKpR07diT6j/d7772nzp076+WXX1anTp1UtGhRhYaGavfu3dq4caNV37Zt22rw4MGSZJl2PCV9+vSx3GS3W7ducnV11YYNG3ThwgUFBwenafick5OTypQpo23btqlkyZLKkyePnnvuuUe6dqtWrVrq1KmT5s+fr2PHjqlu3bpycHDQmTNntH37dr377rvy9/d/pLpSM7FBp06dtGbNGgUGBuqvv/6Sh4eHduzYoUOHDumdd96xnG3w8/NTtWrV9NFHHyk0NFRlypTRN998k+gvwx988IFeffVVtWrVSq+88oqKFSum//77T7///rsuX76sTZs2JVrLgQMH9OGHH8rf318lS5ZUXFycNm7cKHt7+0TvU5TAwcFBI0aM0OjRo9W1a1e1aNHCMhW7h4eHevbsmbo3LJUiIiJs/hYTPMmbmpYrV04tW7bUF198oYiICFWtWlUHDhzQ2bNnU1w2V65cGj16tN577z116NBBLVu2lIuLi/755x9FRUVp6tSpj/S9rVChgiRpwoQJqlevnuzt7dWiRQv5+fmpdu3amjFjhkJDQ+Xt7a19+/bpu+++U48ePVJ1u4f33ntPYWFhev7551WwYEFdvHhRK1asULly5SzXbiUlKChIvXr10sCBA9WoUSP5+voqR44cOnv2rLZu3aqrV68mea+rhO/gm2++qYCAAN25c0dfffWV8uXLp2vXrln6rV+/XqtWrdJLL72k4sWL686dO/ryyy+VK1cuS3h8nG1IjdTsy1944QV9/vnnev3119WyZUtdv35dX3zxhYoXL25zzVOFChW0f/9+ff755ypQoICKFi2a6LWqbm5u6tu3r2bPnq3XX39dfn5+On36tL744gvLPfOehIYNG2rjxo3KlSuXypQpo99//10//fST8uTJY9WvRYsWqlWrlipUqKA8efLo6NGj2rFjR6ITgUj39+3z589X9+7d9cYbb2j58uWGX5MLJIVwBaSzTZs2KXv27Enei8POzk4NGzZUSEiIbt68qbx582rlypUKDg7Wzp07tX79euXLl0++vr6WyQTs7e21cOFCzZ07V5s3b9Y333yjPHnyqFq1alb3U3nvvfd07949rV69Wo6OjvL399fIkSOTvCntwxwcHDRv3jzL9TPZs2dX48aN1aVLF5uD0bJly+rLL7/UzJkztWrVKkVHR6tIkSKJDvlr1KiRXF1dFR8frxdffDFVteTPn1+rV6/WtGnTtGLFCkVHR8vb21vz5s17rDMcEyZM0Pjx4zV58mTFxsZq4MCBj/yP8IcffqiKFStq9erVmjFjhuzt7eXh4aHWrVurWrVqaa4tOU5OTlq+fLmmT5+u9evX6/bt2ypVqpQmT56sdu3aWfrZ2dlp7ty5mjRpkjZt2iSTySQ/Pz8FBgZaDRmTpDJlymjt2rWaPXu21q9fr1u3bsnNzU3ly5fXgAEDkqzF29tb9erV0/fff68rV64oR44c8vb21sKFC1WlSpVkt6Ndu3ZycnLSwoULNX36dDk7O+ull17S22+/nebJDJJy+fJljRw5MtHnEv6eHzxbkJ4mTZqkvHnzKiQkRN99951q166tBQsWpOqMQceOHZUvXz4tWLBAn376qbJlyyZPT09LGH2U722TJk3UrVs3bdmyRZs2bZLZbFaLFi0sfzezZs3S1q1btW7dOnl4eGjkyJHq1atXqraxdevW+vLLL/XFF18oPDxc7u7uatasmQYNGpTijyFubm5avXq1vvjiC23dulUzZsxQbGysPDw85Ofnp+7duye5rKenp2bNmqVPPvlEU6dOVf78+dW5c2e5ublZ3Z+rVq1aOnr0qLZu3ar//vtPuXPnlo+Pj6ZPn24ZKvc425AaqdmX+/r6auLEiVq4cKEmTZqkokWLasSIEQoNDbUJV4GBgRozZow++eQTRUVF6eWXX040XEnSoEGD5ObmphUrVmjy5MlydXXVK6+8ouHDh6fq3nxGePfdd2VnZ6eQkBBFR0erWrVqliD5oG7dumnXrl3at2+fYmJiVKRIEQ0dOlS9e/dOct25cuXSokWL1LVrV/Xq1UsrV6587GGcQGqYzE/qClsA+P/u3bun+vXrq1GjRpo0aVJGlwNYLFu2TBMnTtTOnTvTdDNuAMCzjWuuADxx3377rW7cuGFz5gTIaEePHpWzs/NTO5kGACB9MSwQwBPzxx9/6Pjx4/r0009Vvnx51apVK6NLAiTdn7Xsl19+sdyDjfvjAADSgn89ADwxq1at0qZNm1S2bFlNmTIlo8sBLKZOnao7d+6oQ4cOGj16dEaXAwDIorjmCgAAAAAMwDVXAAAAAGCATBWuzp49qzFjxqhNmzYqX758qqeLNpvNWrBggRo2bCgfHx916tRJv//+e/oWCwAAAAAPyFTXXJ04cUJ79uxR5cqVFR8fb3Pn9KQsXLhQs2bN0ogRI+Tt7a2VK1eqV69e2rhxo+VeFY/q8OHDMpvNT+xeDwAAAAAyp9jYWJlMJlWtWjXZfpnqzJWfn5/27NmjWbNmWe4an5Lo6GjNnz9fvXr1Us+ePeXr66uPP/5YefLk0aJFi9Jci9lsTnW4AwAAAPD0Sm02yFRnrtJyt/NDhw7p9u3batasmaXN0dFRjRs31s6dO9NcS8IZq0qVKqV5Hch4MTExmjlzpjZu3Kjw8HB5e3tr6NChqlu3borLbtmyRZ999pn+/fdf5cyZU35+fhoxYoTc3Nxs+v7333+aNWuWvv/+e926dUvu7u56/vnnuUEuAADAU+Do0aOp6pepzlylxalTpyRJnp6eVu2lS5fWxYsXFRUVlRFlIZMIDAzUkiVL1KpVK7377ruyt7dXnz59dPDgwWSX++KLLzR8+HC5uroqMDBQr7zyirZu3aqePXsqOjraqu+lS5fUoUMH7d27VwEBAfrggw/UoUMH3bhxIz03DQAAAJlMpjpzlRbh4eFydHRU9uzZrdpdXFxkNpsVFhYmJyenNK3bbDYrMjLSiDKRAf78809t2bJFw4YNU/fu3SVJTZo0UceOHTV16lQtXbo00eViY2P18ccfq1q1apozZ45MJpMkqXz58hoyZIhWrFihzp07W/q/++67srOz04oVK5QnTx6rdfH3AwAAkPWZzWbLMWFysny4Sk+xsbE6duxYRpeBNFqzZo3s7OxUoUIFq8+xTp06WrNmjX788Ufly5fPZrnTp08rIiJClStX1j///GNpd3d3l5OTkzZs2KAqVapIkkJDQ7Vv3z699tprunTpks6ePSs7Oztly8ZXCwAA4Gni6OiYYp8sfwTo4uKimJgYRUdHW529Cg8Pl8lkkqura5rX7eDgoDJlyhhRJjLAtWvXVKJECVWvXt2qPTw8XGvWrFFcXJzKlStns1xMTIwkqUSJEjbP58iRQ+fOnZO3t7fs7Oz0xx9/SJIqVKigTz75RL/88ovs7e1Vu3ZtvfvuuypSpEg6bR0AAACelH///TdV/bJ8uEq41ur06dMqW7aspf3UqVMqUqRImocESpLJZJKzs/Nj14iMcePGDRUsWNDmM0yYnj8sLCzRz9fb21smk0l//vmn1fC/U6dO6ebNm5Lun9XMmzevLl68KEmaMGGCKlWqpBkzZujSpUuaPXu2+vXrp02bNilHjhzptYkAAAB4AlIzJFB6CsJVtWrVlCtXLm3bts0SrmJjY/XNN9+oQYMGGVwdMlJUVFSip28TznAmNdmJm5ubmjVrpg0bNqh06dJq3Lixrly5ovHjx8vBwUGxsbGWSS0Srqlyd3fXggULLDNeFipUSMOHD9fmzZvVsWPH9Ng8AAAAZDKZKlzdvXtXe/bskXT/Wpbbt29r+/btkqRatWrJzc1NPXr00MWLFy3TrGfPnl19+/ZVcHCw3Nzc5OXlpVWrVunWrVvq3bt3hm0LMp6Tk5NliN+DEoJRcmc1P/zwQ0VFRWnq1KmaOnWqJKl169YqXry4vvnmG8sZr4R1+Pv7W91KwN/fXyNHjtShQ4cIVwAAAM+ITBWurl+/riFDhli1JTxetmyZateurfj4eMXFxVn1eeONN2Q2m7V48WLduHFD5cqV06JFiyzDv/Bscnd315UrV2zar127JkkqUKBAksvmzp1bc+fO1cWLFxUaGqoiRYrIw8NDAQEBcnNzk4uLi9U68ufPb7W8vb298uTJo/DwcKM2BwAAAJlcpgpXRYsW1fHjx5Pts3z5cps2k8mkvn37qm/fvulVGrKgsmXL6ueff9bt27eVK1cuS3vCJBSJTWbxsCJFilgmpQgPD9eff/6ppk2bWp6vUKGCJNmEuJiYGN28eTPRGw4DAADg6ZTlbyIMJMXf319xcXFas2aNpS0mJkbr1q1T5cqVVbhwYUnSxYsXdfLkyRTX99FHHykuLk49evSwtNWuXVv58uVTSEiI1c2F169fr7i4ONWpU8fALQIAAEBmlqnOXAFGqly5svz9/fXxxx/r+vXrKlGihNavX6/Q0FBNnDjR0m/UqFH65ZdfrM6aLliwQP/73/9UuXJl2dvb67vvvtOPP/6ooUOHysfHx9LP0dFRI0eO1KhRo9SlSxe1adNGFy9e1PLly1WjRg01adLkiW4zAAAAMg7hCk+1oKAgffLJJ9q0aZPCwsLk7e2tefPmqWbNmsku5+XlpZ07d2rXrl2Kj4+Xt7e3PvnkEzVr1symb9u2beXg4KAFCxYoKChILi4u6tSpk4YNGyZ7e/v02jQAAABkMiaz2WzO6CIyo6NHj0qSKlWqlMGVAAAAAMhIqc0GXHMFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMJVJhAfz63GgLTguwMAADKTbBldACQ7O5PmrNqn0KthGV0KkGV4FHDVgM51M7oMAAAAC8JVJhF6NUxnQm9mdBkAAAAA0ohhgQAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYIBMF65Onjyp1157TVWqVFHdunUVFBSkmJiYFJe7efOmxowZo4YNG6pKlSpq2bKlVq1a9QQqBgAAAAApW0YX8KCwsDD16NFDJUuWVHBwsK5cuaIpU6YoKipKY8aMSXbZIUOG6NSpUxo+fLgKFy6svXv3auzYsbK3t9crr7zyhLYAAAAAwLMqU4Wr1atX686dO5o9e7by5MkjSYqLi9O4cePUt29fFSxYMNHlrl27pp9//lmTJ09Wu3btJEm+vr46evSotmzZQrgCAAAAkO4y1bDAvXv3ytfX1xKsJKlZs2aKj4/Xvn37klzu3r17kqTcuXNbtefKlUtmszldagUAAACAB2WqM1enTp1S+/btrdpcXFzk7u6uU6dOJblc4cKFVa9ePc2bN0+lSpVSoUKFtHfvXu3bt0/Tp09Pcz1ms1mRkZFpXj41TCaTcuTIka6vATzN7t69y48oAAAgXZnNZplMphT7ZapwFR4eLhcXF5t2V1dXhYWFJbtscHCwhg0bphYtWkiS7O3t9d5776lp06Zpric2NlbHjh1L8/KpkSNHDpUvXz5dXwN4mp0+fVp3797N6DIAAMBTztHRMcU+mSpcpZXZbNbo0aN15swZffTRR3J3d9dPP/2kSZMmydXV1RK4HpWDg4PKlCljcLXWUpOAASStVKlSnLkCAADp6t9//01Vv0wVrlxcXBQREWHTHhYWJldX1ySX2717t7Zv365NmzbJ29tbklS7dm1dv35dU6ZMSXO4MplMcnZ2TtOyAJ4MhtUCAID0ltoTIplqQgtPT0+ba6siIiJ07do1eXp6Jrncv//+K3t7e3l5eVm1lytXTlevXmXIEAAAAIB0l6nCVYMGDfTTTz8pPDzc0rZ9+3bZ2dmpbt26SS7n4eGhuLg4HT9+3Kr9r7/+Ur58+fhlGwAAAEC6y1ThKiAgQDlz5tSAAQP0448/au3atQoKClJAQIDVPa569Oihxo0bWx43aNBARYoU0eDBg7Vx40bt379f06ZN0/r169W1a9eM2BQAAAAAz5hMdc2Vq6urli5dqvHjx2vAgAHKmTOnOnTooGHDhln1i4+PV1xcnOVxrly5tGTJEs2YMUPTp09XRESEihYtqsDAQMIVAAAAgCciU4UrSSpdurSWLFmSbJ/ly5fbtJUoUUKffPJJ+hQFAAAAACnIVMMCAQAAACCrIlwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYIFtGFwAAAACkt5iYGM2cOVMbN25UeHi4vL29NXToUNWtWzdVy2/dulVLly7V8ePHlS1bNpUpU0ZDhgyRr6+vJOnSpUtau3atdu/erbNnz8rOzk5eXl7q16+f6tSpk56bhkyEM1cAAAB46gUGBmrJkiVq1aqV3n33Xdnb26tPnz46ePBgissGBwdr+PDhKlSokAIDAzV06FB5eXnpypUrlj7fffedFi5cqBIlSmjo0KHq37+/7ty5o9dee01r165Nz01DJmIym83mjC4iMzp69KgkqVKlSk/k9d6ZuVVnQm8+kdcCngYlPfJq0pDmGV0GACALOHLkiDp27KiRI0eqd+/ekqTo6Gi1bNlS+fLl0+rVq5Nc9vfff1dAQIACAwPVs2fPJPudOHFC+fLlk5ubm6UtJiZGbdq0UWRkpPbs2WPY9uDJS2024MwVAAAAnmrbt2+Xvb29OnXqZGnLnj27OnTooMOHD+vSpUtJLrt06VLlz59f3bt3l9ls1p07dxLt99xzz1kFK0lydHTUCy+8oMuXL+v27dvGbAwyNcIVAAAAnmrHjh1TyZIllStXLqt2Hx8fy/NJ2b9/vypVqqRly5bp+eefV7Vq1VSvXj2tWLEiVa997do15ciRQzly5Ej7BiDLYEILAAAAPNWuXbsmd3d3m/aEtqtXrya6XFhYmG7evKlDhw7pwIEDGjhwoAoXLqx169Zp/PjxypYtmwICApJ83bNnz2rnzp3y9/eXvb29MRuDTI1wBQAAgKdaVFSUHB0dbdqzZ89ueT4xkZGRkqRbt25pxowZat78/rW+/v7+atWqlebOnZtkuLp7966GDBkiJycnvfXWW0ZsBrIAhgUCAADgqebk5KSYmBib9ujoaMvziUkIXw4ODmratKml3c7OTs2aNdPly5d18eJFm+Xi4uI0bNgw/fvvv5o5c6YKFixoxGYgCyBcAQAA4Knm7u6ua9eu2bQntBUoUCDR5fLkyaPs2bMrT548NsP68uXLJ0kKDw+3We69997T7t27NWXKFMt9sPBsIFwBAADgqVa2bFmdOXPGZsa+P/74Q5JUrly5RJezs7NTuXLldOPGDZszXwnXaeXNm9eqferUqVq3bp1Gjx6tli1bGrUJyCIIVwAAAHiq+fv7Ky4uTmvWrLG0xcTEaN26dapcubIKFy4sSbp48aJOnjxptWyzZs0UFxenDRs2WNqio6MVEhKiMmXKWA35++yzz7R48WK9+eab6tGjR/puFDIlJrQAAADAU61y5cry9/fXxx9/rOvXr6tEiRJav369QkNDNXHiREu/UaNG6ZdfftHx48ctbQEBAfr666/14Ycf6vTp0ypSpIg2btyoixcvau7cuZZ+O3fu1LRp01SyZEl5enpq48aNVjXUrVtX+fPnT/+NRYYiXAEAAOCpFxQUpE8++USbNm1SWFiYvL29NW/ePNWsWTPZ5ZycnLR06VJNmzZN69atU2RkpMqVK6f58+erfv36ln7//POPJOnMmTMaOXKkzXqWLVtGuHoGmMxmszmji8iMjh49KkmqVKnSE3m9d2Zu1ZnQm0/ktYCnQUmPvJo0pHlGlwEAAJ4Bqc0GXHMFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAABAJhEfz+1HgUeVmb432TK6AAAAANxnZ2fSnFX7FHo1LKNLAbIEjwKuGtC5bkaXYZHpwtXJkyc1YcIEHT58WDlz5lSbNm00dOhQOTo6prjslStX9PHHH2vPnj2KjIyUh4eH+vXrp9atWz+BygEAAB5f6NUwnQm9mdFlAEiDTBWuwsLC1KNHD5UsWVLBwcG6cuWKpkyZoqioKI0ZMybZZa9evapOnTqpVKlSGj9+vHLlyqUTJ04oJibmCVUPAAAA4FmWqcLV6tWrdefOHc2ePVt58uSRJMXFxWncuHHq27evChYsmOSy06ZNU6FChfTZZ5/J3t5ekuTr6/skygYAAACAzDWhxd69e+Xr62sJVpLUrFkzxcfHa9++fUkud/v2bW3btk2vvvqqJVgBAAAAwJOUqc5cnTp1Su3bt7dqc3Fxkbu7u06dOpXkcn/99ZdiY2OVLVs2de3aVYcPH1aePHnUtm1bDR06VA4ODmmqx2w2KzIyMk3LppbJZFKOHDnS9TWAp9ndu3dlNmeeWYIAIK04JgDSLr2PB8xms0wmU4r9MlW4Cg8Pl4uLi027q6urwsKSnjXnv//+kyS99957euWVVzRw4EAdOXJEs2bNkp2dnd5666001RMbG6tjx46ladnUypEjh8qXL5+urwE8zU6fPq27d+9mdBkA8Ng4JgDS7kkcD6Rmgr1MFa7SKj4+XpJUp04dBQYGSpKef/553blzR4sXL9aAAQPk5OT0yOt1cHBQmTJlDK31YalJwACSVqpUKc5cAXgqcEwApF16Hw/8+++/qeqXqcKVi4uLIiIibNrDwsLk6uqa7HLS/UD1IF9fX82bN09nz56Vt7f3I9djMpnk7Oz8yMsBeHIYQgMAANL7eCC1P35kqgktPD09ba6tioiI0LVr1+Tp6ZnkcimdXYqOjjakPgAAAABISqYKVw0aNNBPP/2k8PBwS9v27dtlZ2enunWTvvOyh4eHvLy89NNPP1m1//TTT3Jyckr3oX0AAAAAkKnCVUBAgHLmzKkBAwboxx9/1Nq1axUUFKSAgACre1z16NFDjRs3tlp22LBh2rVrlyZOnKh9+/Zp3rx5Wrx4sXr27MnQPgAAAADpLlNdc+Xq6qqlS5dq/PjxGjBggHLmzKkOHTpo2LBhVv3i4+MVFxdn1ebn56ePP/5Yn376qVatWqUCBQpo0KBB6tOnz5PcBAAAAADPqEwVriSpdOnSWrJkSbJ9li9fnmh78+bN1bx583SoCgCQVcXExGjmzJnauHGjwsPD5e3traFDhyY73FySgoODNXv2bJt2R0dHHT16NMnlDh48qC5dukiS9u/fLzc3t8fbAABAlpGmcPXHH3+ocuXKRtcCAIDhAgMDtWPHDnXv3l0lS5bU+vXr1adPHy1dulQ1atRIcfmxY8daDS+3t7dPsm98fLwmTJggZ2fndL8JPQAg80lTuOrUqZNKlCih1q1bq3Xr1ipWrJjRdQEA8NiOHDmiLVu2aOTIkerdu7ckqW3btmrZsqWmT5+u1atXp7iOpk2bpvrs05o1a3Tp0iV16NBBy5Yte6zaAQBZT5omtJg2bZpKlCihuXPnqkmTJgoICNCqVat069Ytg8sDACDttm/fLnt7e3Xq1MnSlj17dnXo0EGHDx/WpUuXUrWe27dvp3hzylu3bumTTz7R4MGDLfdfBAA8W9IUrlq1aqUFCxZo7969evfddyVJ48aNU/369dW/f39t375dMTExhhYKAMCjOnbsmEqWLKlcuXJZtfv4+FieT8mLL76o6tWrq1q1ahoxYoT++++/RPvNnDlT7u7uCggIePzCAQBZ0mNNaOHm5qauXbuqa9euOnfunEJCQhQSEqJhw4Ypd+7catq0qdq0aZOqMe0AABjt2rVrcnd3t2lPaLt69WqSy7q4uKhr166qUqWKHB0ddfDgQX3xxRc6evSo1q5daxXY/vnnH61Zs0YLFixI9posAMDTzbDZArNnz64cOXIoe/bsMpvNMplM+u677/T111+rfPnymjp1KjfzBQA8UVFRUXJ0dLRpz549u+X5pPTo0cPqcdOmTeXj46MRI0boiy++sLrVx8SJE9WgQQPVq1fPoMoBAFnRY91E+Pbt21q7dq169uxpuc+Uh4eHZs2apR9//FE//PCDZsyYoRs3bmj06NFG1QwAQKo4OTklOkw9Ojra8vyjaNWqldzd3fXTTz9Z2rZu3arDhw9r1KhRj1csACDLS9OZq2+//VYhISHavXu3oqOjValSJb3zzjtq3ry58ubNa9XX399f4eHh+vDDDw0pGACA1HJ3d9eVK1ds2q9duyZJKlCgwCOvs1ChQgoLC7M8DgoKUtOmTeXg4KALFy5IksLDwyVJly9fVmxsrAoWLJiW8gEAWUyawtXAgQNVuHBh9ezZU23atJGnp2ey/cuWLatWrVqlqUAAANKqbNmy+vnnn3X79m2ra6T++OMPSVK5cuUeaX1ms1mhoaEqX768pe3SpUvavHmzNm/ebNP/5ZdfVtmyZbVx48Y0bgEAICtJU7haunSpateuner+Pj4+lpmZAAB4Uvz9/bV48WKtWbPGcp+rmJgYrVu3TpUrV1bhwoUlSRcvXtTdu3dVunRpy7I3btywub/VF198oRs3bqh+/fqWtjlz5ti87pYtW7R161ZNnTpVhQoVSo9NAwBkQmkKV48SrAAAyCiVK1eWv7+/Pv74Y12/fl0lSpTQ+vXrFRoaqokTJ1r6jRo1Sr/88ouOHz9uaWvUqJGaN28uLy8vOTo66tChQ9qyZYvKlStndd+sl156yeZ1E6Z4b9CgQapvQAwAyPrSFK5mzJih3bt3JznMoW3btnrppZc0cODAxyoOAIDHFRQUpE8++USbNm1SWFiYvL29NW/ePNWsWTPZ5Vq1aqXDhw9rx44diomJUZEiRfT666/rzTffVI4cOZ5Q9QCArCRN4WrHjh1q3Lhxks+/8MIL2rp1K+EKAJDhsmfPrlGjRiU7m9/y5ctt2iZMmJDm1xw0aJAGDRqU5uUBAFlTmqZiv3TpkooXL57k80WLFtXFixfTXBQAAAAAZDVpClfOzs4KDQ1N8vkLFy5YbtAIAAAAAM+CNIWrWrVqac2aNYneO+TSpUtas2YNk14AAAAAeKak6ZqrIUOGqGPHjmrRooU6dOigMmXKSJJOnDihtWvXymw2a8iQIYYWCgAAAACZWZrClaenp1auXKkJEyZoyZIlVs/VrFlT7777rtW9QgAAAADgaZemcCXdv+v9ihUrdOPGDV24cEHS/YksuJ8HAAAAgGdRmsNVAjc3NwIVAAAAgGfeY4Wry5cv6++//1ZERITMZrPN823btn2c1QPAMyM+3iw7O1NGlwFkOXx3AGQmaQpX0dHRGjVqlL755hvFx8fLZDJZwpXJ9H87OMIVAKSOnZ1Jc1btU+jVsIwuBcgyPAq4akDnuhldBgBYpClcffzxx9q5c6eGDh2qqlWrqlu3bpoyZYoKFCigpUuX6urVq5o6darRtQLAUy30apjOhN7M6DIAAEAapek+Vzt27FC7du3Up08fyzTsBQsWVJ06dTR//nzlzp1bK1euNLRQAAAAAMjM0hSurl+/Lh8fH0mSk5OTJOnu3buW55s2baqdO3caUB4AAAAAZA1pClf58+fXzZv3h67kyJFDrq6uOn36tOX527dvKzo62pgKAQAAACALSNM1Vz4+Pjp06JDlcaNGjbRo0SK5u7srPj5eS5YsUZUqVYyqEQAAAAAyvTSFq27dumn79u2KiYmRo6OjhgwZosOHD2vkyJGSpOLFi+vdd981tFAAAAAAyMzSFK5q1KihGjVqWB4XLlxY27Zt0//+9z/Z2dnJ09NT2bI99v2JAQAAACDLeORrru7evauBAwdq06ZN1iuys1PZsmXl5eVFsAIAAADwzHnkcJUjRw799NNPioqKSo96AAAAACBLStNsgdWrV9fhw4eNrgUAAAAAsqw0hasxY8bot99+04wZM3T58mWjawIAAACALCdNF0e1bt1acXFxWrBggRYsWCB7e3s5Ojpa9TGZTPrtt98MKRIAAAAAMrs0haumTZvKZDIZXQsAAAAAZFlpCldTpkwxug4AAAAAyNLSdM0VAAAAAMBams5cbdiwIVX92rZtm5bVAwAAAECWk6ZwFRgYmORzD16LRbgCAAAA8KxIU7j67rvvbNri4+N14cIFrVq1ShcvXtTUqVMfuzgAAAAAyCrSFK48PDwSbS9WrJh8fX3Vp08frVixQh988MFjFQcAAAAAWUW6TGjRsGFDbd26NT1WDQAAAACZUrqEq/PnzysmJiY9Vg0AAAAAmVKahgX++uuvibaHh4fr4MGDWr58uV588cXHKgwAAAAAspI0hatu3bpZzQqYwGw2y97eXv7+/nrvvfceuzgAAAAAyCrSFK6WLVtm02YymeTi4iIPDw/lypXrsQsDAAAAgKwkTeGqVq1aRtcBAAAAAFlamia0OH/+vHbt2pXk87t27dKFCxfSXBQAAAAAZDVpOnMVFBSk27dvy8/PL9HnV65cKRcXF82YMeOxigMAAACArCJNZ64OHz6sOnXqJPm8r6+vDh48mOaiAAAAACCrSVO4Cg8PV86cOZN83tnZWbdu3UprTQAAAACQ5aQpXBUuXFiHDh1K8vnffvtNhQoVSnNRAAAAAJDVpClctWzZUlu2bNGyZcsUHx9vaY+Li9PSpUu1detWtWzZ0rAiAQAAACCzS9OEFn379tVvv/2mSZMmad68eSpVqpQk6fTp07px44Zq1aqlfv36GVooAAAAAGRmaQpXjo6OWrx4sdavX6+dO3fq3LlzkiQfHx81adJEbdu2lZ1dmk6KAQAAAECWlKZwJUl2dnZq37692rdvb2Q9AAAAAJAlpen00q1bt/TPP/8k+fzx48cVFhaW5qIAAAAAIKtJU7iaPHmyxowZk+TzH3zwgaZOnZrmogAAAAAgq0lTuDpw4ID8/PySfL5Ro0bav39/mosCAAAAgKwmTeHqxo0byps3b5LP58mTR9evX09zUQAAAACQ1aQpXLm7u+vvv/9O8vm//vpLbm5uaS4KAAAAALKaNIWrl156SWvXrtV3331n89y3336rdevW6aWXXnrs4gAAAAAgq0jTVOyDBg3S/v37NXDgQJUtW1bPPfecJOnEiRM6duyYypQpo8GDBxtaKAAAAABkZmk6c5U7d26tWbNG/fr1071797Rjxw7t2LFD9+7d04ABA/TVV1/JbDYbXSsAAAAAZFppvomws7OzBg8ebHWGKjo6Wrt27dJbb72lH374QUePHjWkSAAAAADI7NIcrhKYzWbt379fISEh2rlzp+7cuaO8efOqZcuWRtQHAAAAAFlCmsPVn3/+qZCQEG3ZskX//fefTCaTmjdvrq5du6pKlSoymUxG1gkAAAAAmdojhavz589r06ZNCgkJ0dmzZ1WwYEG1atVKPj4+GjZsmJo2baqqVaumV60AAAAAkGmlOlx16tRJR44cUd68edW0aVNNmDBBNWrUkCSdO3cu3QoEAAAAgKwg1eHqjz/+UNGiRRUYGKiGDRsqW7bHvlwLAAAAAJ4aqZ6K/f3335e7u7sGDhyounXrasyYMTpw4ABTrgMAAACAHuHMVZcuXdSlSxedP39eISEh2rx5s7788kvlz59ftWvXlslkYhILAAAAAM+sR76JcLFixdS/f39t3bpVX3/9tVq0aKFffvlFZrNZ48aN0/vvv6/vv/9e0dHR6VEvAAAAAGRKj3XhVMWKFVWxYkWNGjVKBw4c0KZNm7R161Z99dVXypEjhw4fPmxUnQAAAACQqRkyK4WdnZ3q1KmjOnXqaNy4cfruu+8UEhJixKoBAAAAIEswfMq/7Nmzq3nz5mrevLnRqwYAAACATOuRr7kCAAAAANgiXAEAAACAATJduDp58qRee+01ValSRXXr1lVQUJBiYmIeaR1LliyRt7e3+vbtm05VAgAAAIA1w6+5ehxhYWHq0aOHSpYsqeDgYF25ckVTpkxRVFSUxowZk6p1XLt2TXPmzFG+fPnSuVoAAAAA+D+ZKlytXr1ad+7c0ezZs5UnTx5JUlxcnMaNG6e+ffuqYMGCKa5j2rRp8vPz08WLF9O5WgAAAAD4P5lqWODevXvl6+trCVaS1KxZM8XHx2vfvn0pLn/w4EF9++23euutt9KxSgAAAACwlanC1alTp+Tp6WnV5uLiInd3d506dSrZZePi4jR+/Hi9+eabKlCgQHqWCQAAAAA2MtWwwPDwcLm4uNi0u7q6KiwsLNllv/jiC929e1c9e/Y0rB6z2azIyEjD1pcYk8mkHDlypOtrAE+zu3fvymw2Z3QZj4X9APB4nob9gMS+AHgc6b0fMJvNMplMKfbLVOEqra5fv65Zs2Zp6tSpcnR0NGy9sbGxOnbsmGHrS0yOHDlUvnz5dH0N4Gl2+vRp3b17N6PLeCzsB4DH8zTsByT2BcDjeBL7gdTkjEwVrlxcXBQREWHTHhYWJldX1ySXmzlzpry9vVWjRg2Fh4dLku7du6d79+4pPDxczs7Oypbt0TfVwcFBZcqUeeTlHkVqEjCApJUqVSrL/2LNfgB4PE/DfkBiXwA8jvTeD/z777+p6pepwpWnp6fNtVURERG6du2azbVYDzp9+rR+/fVX1axZ0+a5mjVrauHChWrQoMEj12MymeTs7PzIywF4chhCA4D9AID03g+k9sePTBWuGjRooHnz5llde7V9+3bZ2dmpbt26SS73zjvvWM5YJZg0aZKcnJw0fPhweXt7p2vdAAAAAJCpwlVAQICWL1+uAQMGqG/fvrpy5YqCgoIUEBBgdY+rHj166OLFi9q5c6ckqVy5cjbrcnFxkbOzs2rXrv3E6gcAAADw7MpUU7G7urpq6dKlsre314ABA/TRRx+pQ4cOCgwMtOoXHx+vuLi4DKoSAAAAAGxlqjNXklS6dGktWbIk2T7Lly9PcT2p6QMAAAAARslUZ64AAAAAIKsiXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABggGwZXcDDTp48qQkTJujw4cPKmTOn2rRpo6FDh8rR0THJZa5evaolS5Zo3759OnfunHLnzq2aNWtq+PDh8vDweILVAwAAAHhWZapwFRYWph49eqhkyZIKDg7WlStXNGXKFEVFRWnMmDFJLvfXX39p586dat++vSpXrqybN29q7ty56tixozZv3iw3N7cnuBUAAAAAnkWZKlytXr1ad+7c0ezZs5UnTx5JUlxcnMaNG6e+ffuqYMGCiS5XvXp1bdu2Tdmy/d/mVKtWTQ0bNtSGDRvUq1evJ1E+AAAAgGdYprrmau/evfL19bUEK0lq1qyZ4uPjtW/fviSXc3FxsQpWklSoUCG5ubnp6tWr6VUuAAAAAFhkqjNXp06dUvv27a3aXFxc5O7urlOnTj3Suk6fPq3r16+rdOnSaa7HbDYrMjIyzcunhslkUo4cOdL1NYCn2d27d2U2mzO6jMfCfgB4PE/DfkBiXwA8jvTeD5jNZplMphT7ZapwFR4eLhcXF5t2V1dXhYWFpXo9ZrNZEyZMUIECBdSiRYs01xMbG6tjx46lefnUyJEjh8qXL5+urwE8zU6fPq27d+9mdBmPhf0A8Hiehv2AxL4AeBxPYj+Q3AR7CTJVuDJKcHCwDhw4oM8++0zOzs5pXo+Dg4PKlCljYGW2UpOAASStVKlSWf4Xa/YDwON5GvYDEvsC4HGk937g33//TVW/TBWuXFxcFBERYdMeFhYmV1fXVK3jyy+/1Jw5czRx4kT5+vo+Vj0mk+mxwhmA9McQGgDsBwCk934gtT9+ZKoJLTw9PW2urYqIiNC1a9fk6emZ4vI7d+7U2LFjNXjwYHXo0CG9ygQAAAAAG5kqXDVo0EA//fSTwsPDLW3bt2+XnZ2d6tatm+yyP//8s4YPH66OHTtqwIAB6V0qAAAAAFjJVOEqICBAOXPm1IABA/Tjjz9q7dq1CgoKUkBAgNU9rnr06KHGjRtbHp88eVIDBgxQyZIl1aZNG/3++++W/86dO5cRmwIAAADgGZOprrlydXXV0qVLNX78eA0YMEA5c+ZUhw4dNGzYMKt+8fHxiouLszz+448/FBERoYiICHXu3Nmq78svv6wpU6Y8kfoBAAAAPLsyVbiSpNKlS2vJkiXJ9lm+fLnV43bt2qldu3bpWBUAAAAAJC9TDQsEAAAAgKyKcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAYgXAEAAACAAQhXAAAAAGAAwhUAAAAAGIBwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYgHAFAAAAAAbIdOHq5MmTeu2111SlShXVrVtXQUFBiomJSXE5s9msBQsWqGHDhvLx8VGnTp30+++/p3/BAAAAAKBMFq7CwsLUo0cPxcbGKjg4WMOGDdOXX36pKVOmpLjswoULNWvWLPXs2VPz58+Xu7u7evXqpfPnzz+BygEAAAA867JldAEPWr16te7cuaPZs2crT548kqS4uDiNGzdOffv2VcGCBRNdLjo6WvPnz1evXr3Us2dPSVL16tXl7++vRYsWaezYsU9mAwAAAAA8szLVmau9e/fK19fXEqwkqVmzZoqPj9e+ffuSXO7QoUO6ffu2mjVrZmlzdHRU48aNtXfv3vQsGQAAAAAkZbJwderUKXl6elq1ubi4yN3dXadOnUp2OUk2y5YuXVoXL15UVFSU8cUCAAAAwAMy1bDA8PBwubi42LS7uroqLCws2eUcHR2VPXt2q3YXFxeZzWaFhYXJycnpkWqJjY2V2WzWkSNHHmm5tDCZTGpRy11x8fnS/bWAp4W9nZ2OHj0qs9mc0aUYgv0A8Oietv2AxL4AeFRPaj8QGxsrk8mUYr9MFa4yk4Q3LzVvohFccj1a+ANw35P6jj4J7AeAtHma9gMS+wIgLdJ7P2AymbJeuHJxcVFERIRNe1hYmFxdXZNdLiYmRtHR0VZnr8LDw2UymZJdNilVq1Z95GUAAAAAPLsy1TVXnp6eNtdWRURE6Nq1azbXUz28nCSdPn3aqv3UqVMqUqTIIw8JBAAAAIBHlanCVYMGDfTTTz8pPDzc0rZ9+3bZ2dmpbt26SS5XrVo15cqVS9u2bbO0xcbG6ptvvlGDBg3StWYAAAAAkDLZsMCAgAAtX75cAwYMUN++fXXlyhUFBQUpICDA6h5XPXr00MWLF7Vz505JUvbs2dW3b18FBwfLzc1NXl5eWrVqlW7duqXevXtn1OYAAAAAeIZkqnDl6uqqpUuXavz48RowYIBy5sypDh06aNiwYVb94uPjFRcXZ9X2xhtvyGw2a/Hixbpx44bKlSunRYsWqVixYk9yEwAAAAA8o0zmp2n+UgAAAADIIJnqmisAAAAAyKoIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYADCFQAAAAAYIFPdRBhPXnBwsGbPnp3oc2+99Zb69OnzhCtKXI0aNdSjRw8NGjQoXdb/888/q3v37ok+t3//fg0ZMkTOzs6aP3++4a997Ngxffvtt3r99deVI0cOw9cvSRcuXNCLL76omTNnyt/fP11eA0+nPXv2aMWKFTp69KgiIiLk6uqqihUrqnXr1mrevLns7O7/RhcYGKj169dblnN3d1fFihU1bNgweXt7y8/PT6Ghocm+1sCBAxP9jk+dOlV79+7VxYsXZTKZVKpUKfXq1UstWrRIdn0Jf/cPq1y5sr788sskl0tuv5hg2bJlql27drJ9Mpqfn58aNmyoMWPGPPa6lixZolKlSumFF14woDJkVc/i/kCSunXrpl9++cWmvUuXLoZ8vx7Vnj17tHDhQp04cULR0dFyd3dX5cqVNWDAAJUqVUrS/ZqNOm5Zt26dHBwc1KpVq8de17OAcAU5OTlp6dKlNu2FCxfOgGoy1uTJk+Xp6WnV5uLiog8++MDyj4bRjh07ptmzZ6tLly7pFq4KFCigNWvWqGTJkumyfjydPv74Y82fP1+NGzfWmDFj5O7urv/++0/ffvut3n77bbm6uqp+/fqW/sWKFdP06dNlNpt19uxZzZo1S926ddOWLVs0e/ZsxcTEWPoOHDhQ1apVU69evSxthQoVSrSOO3fuqGPHjvL09JTJZNKOHTs0fPhwxcfHp+of++HDh1sFoZw5cybbv2PHjlbblSA2NlbDhg2Tvb29vL29U3zdp8myZcvUsGFDwtUz7FndHySoVq2aRo0aZdWWP3/+VC1rpK1bt2rYsGF6+eWX9frrr8vBwUEnT57Utm3bdPLkSUu4MtL69evl7OxMuEolwhVkZ2enKlWqZHQZT0RUVJScnJySfP65555TpUqVbNrLlCmTnmWlO0dHx2fmM4Yxdu/erfnz5yf663GzZs3Uo0cPZctm/U+Ik5OT5e+satWqKlq0qLp06aJNmzapd+/eVn0dHR2VP3/+VP1dfvjhh1aP69evr3///Vfr169P1T/2JUqUeKS//0KFCiV6YDd58mRdv35dS5cuVZ48eVK9vsSktC8CMpNneX+QwMXFJVP8O7p8+XLVrl1bU6ZMsbTVrVtX3bt3V3x8fAZWhgRcc4UUeXt7a+HChQoODladOnVUu3ZtjR49WpGRkVb9rly5opEjR6pOnTry8fGRv7+/1Rmx+Ph4ffrpp/Lz81PFihXl7++v1atX27zet99+K39/f1WqVEkdOnTQkSNHEq1r9+7d6tixo3x8fPT888/rgw8+sKrp559/lre3t3bv3q3BgwerWrVqGjJkSJreg27duqlv376Wx8HBwapataqOHz+uzp07q3LlymrZsqV++OEHm2XXrVunVq1aqVKlSqpfv75mzJihuLg4y3OjR4+WJPn6+lqGSzz4Gg+rUaOGgoODbWrbvn27mjZtqqpVq6p79+46d+6cpc+FCxfk7e2t7du3W9r8/Pz04YcfauXKlWrUqJGqV6+u/v3768aNG1avd+LECXXp0kWVKlVSkyZNtGnTJvXv31/dunVLy1uJLOLzzz+Xu7u7+vXrl+jzPj4+Kl++fLLrqFixoqT7f39Gy5Mnj2JjYw1fb1L27t2rpUuXql+/fqpVq5bVcydPnlS/fv1UvXp1ValSRX369LH6/kn396MLFizQtGnTVLduXfn6+kqSoqOjNXnyZNWrV0+VKlVSmzZttHPnzlTV9N1336ldu3aqWrWqatSooXbt2mnPnj02/VL6joeGhmrw4MGW+nv37q3jx49bnk8YwrVy5Up5e3vL29tb69atS1WNeDqwP0ie2WzWokWL1LRpU1WsWFEvvviilixZYnn+0qVL8vb21v79+y1t48ePtxyjJPjoo49SHN4YHh4ud3f3RJ9LbIRNcscGknTr1i2NHj1atWvXlo+PjwICAvTrr79ank8YErl7927L9//BYxDY4swVJEn37t2zaXvwV6iVK1eqevXqmjJlis6cOaOgoCDly5dPI0aMkCTdvHlTnTp1kiQNGzZMRYsW1dmzZ62+xEFBQVq2bJn69eunqlWravfu3frggw907949de3aVdL9IXKDBw9WgwYNNHr0aF24cEFDhw61Gj4g3d9ZDBs2TO3atdOgQYN07do1ffTRRwoPD9eMGTOs+r7//vtq3bq15syZk+LQvvj4eKv3ws7OLsllYmNjNWLECHXv3l39+/fXwoULNXjwYO3atUt58+aVdP8fpGnTpqlHjx4KDAzUyZMnLeFqxIgRatiwofr166e5c+fqs88+U+7cueXo6JhsjYk5duyYbty4oREjRiguLk5TpkzR22+/rTVr1iS73K5du3T27FmNGTNGN2/e1OTJkzV+/HjLexgVFaVevXrJxcVF06ZNkyTNmTNH4eHhKl68+CPXiazh3r17OnTokJo2bWrza/SjSDiIKlCgwGPXZDabFRcXp8jISO3atUv79u2z/E2mZOzYsRo2bJjy5MmjF198USNGjHikM0///fefAgMDVa1aNQ0YMMDqufPnzysgIEDPPfecpkyZIpPJpHnz5qlnz57avn271fd52bJlqly5siZOnGjZz4wYMUI//PCDhg4dKk9PT23cuFGDBg3SnDlzEr0+JMG5c+c0ZMgQtWjRQm+99Zbi4+P1zz//KCwszKpfSt/x27dvq1u3brKzs9O4ceOUPXt2zZ07V127dtWmTZtUuHBhzZ49W3369LEatsX3/9nB/uD/XvPhY6WE92PixIn66quv9Oabb6py5co6dOiQpk+fruzZs6tz584qXLiwPDw89Ouvv1p+WPnll1+UPXt2/frrr2rYsKEk6ddff1WNGjWSraNChQratm2bPv/8czVu3FhFixZNsm9KxwZxcXF64403dP78eY0YMUL58+fX8uXL9dprr2n16tWqWLGiPvjgA7399ttycnKyDItMasgm7iNcQZGRkapQoYJN+8qVKy1fcnd3d3300UeSpAYNGujvv//Wjh07LOFqyZIlun79urZt22b5oifsQCTpxo0bWrFihXr37m0ZUlCvXj3dvHlTc+bMUefOnWVvb68FCxaocOHCmjNnjuzt7SVJ2bNn17vvvmtZl9lsVlBQkJo3b66JEyda2t3d3dWnTx/1799fzz33nKXdz89Pb7/9dqrei1deecXqcYcOHaxe40EJ4SrhGoRSpUrpxRdf1N69e9WmTRvdvn1bs2bN0uuvv67hw4dLun/q3sHBQVOmTFHv3r3l5uZmOUipUKGC3NzcUlXnwyIiIrRhwwbL8pGRkRo9erQuX76c7E7QbDZr7ty5lgPA0NBQzZ8/X/Hx8bKzs9PatWt1/fp1rVq1yvK5VqxYUU2aNOHg6il269YtxcTE2Fx3mXBAkyCxHx/u3bsns9msc+fO6YMPPpCDg0OyISG19u/fr9dee03S/QOa999/P8XJWRwdHdW5c2fVq1dPLi4u+uOPPzRv3jz9+eef+uqrr+Tg4JDi65rNZo0aNUr37t3T9OnTLfulBLNnz5arq6s+//xzZc+eXdL9azNefPFFffXVV+rSpYulr6urq2bPni2TySRJ+ueff/TNN99o3LhxCggIkHR//xoaGppiuPr7778VGxur999/X7ly5ZKkRK8TS+k7vm7dOl28eFFbtmxR6dKlJUk1a9ZUo0aNtHTpUgUGBqp8+fKPNGwLTxf2B/ft2bPH5lhpz549iomJ0YoVKzRu3DjLj8x16tRRVFSU5syZo06dOsnOzk41a9a0nBEKCwuzjApJaIuKitKff/5p+bE5KW+99Zb+/fdfTZkyRVOmTJG7u7saNmyorl27qmzZslZ9Uzo22L17t44cOaLPPvvMsv+oV6+emjRpovnz5ys4OFhlypRRrly55OzszPc/lQhXkJOTk1asWGHT/uDEDnXq1LF6rnTp0tqyZYvl8f79+/X8888n+QvKkSNHFBsba7Pza9asmTZv3qwzZ86odOnS+uOPP+Tn52d1AOPv728Vrk6fPq3Q0FC98847Vr8i1apVS3Z2dvrzzz+twlXCL0LS/TNTD45Jfvgfg6lTp1oOMCQlG3bs7OysAmTRokXl5OSkK1euSJIOHz6syMhI+fv7W9WZsNM9ceKEzfCitCpbtqxVrQnXiKUUrmrWrGn1y3rp0qUVGxur69evy93dXX/++ae8vLysPteiRYva7MDxdEoIAQl27NhhNbT24ZmyTpw4YXXwUaBAAU2bNk1eXl6per0Hvycmk8lqP+Dj46Ovv/5at2/f1t69ezVhwgTZ29urY8eOSa6vQIECGjt2rOVxrVq19Nxzz6lv377auXOnmjdvnmJNn3/+uX788UcFBwerSJEiNs/v27dPzZs3l729vaV+FxcXlS9fXn/++adV3wYNGli9p7/99pskJbpfnDx5siIjI+Xs7Jzor+Xe3t6yt7fXiBEj9Morr6hmzZrKnTu3TX0pfccPHjyo5557zmq/lydPHtWpU8dSHyCxP6hevbplGH+CfPnyae3atZKkJk2a2Pxbv3DhQl26dEkeHh6qUaOGJkyYoJiYGB08eFD58+dXx44dtXr1akVGRuqPP/5QbGysatasKcn2eMXe3l4mk0kFCxbU119/rV9//VU//PCDDh48qLVr12rDhg2aM2eO1aQzKR0bHDx4ULly5bL6YcbBwUGNGzfW5s2bk30/kDTCFWRnZ5foJA4PcnFxsXrs4OBgNVTv1q1bVoHmYQlDVR6eWSfh8a1btyRJ165dU758+az65MqVy/KLsHR/CKIkm+E5CS5dumT1+MH1zZkzx2qK5Ycvzi1dunSK70UCJycnmyF8Dg4Oio6Otqrz5ZdfTlWdjyOxz0eSpZbULpewPQnLXb16NdGA6ebmluK6kXXlyZNHjo6Ounz5slW7r6+vvv76a0lK9NqL4sWL6+OPP5bJZJK7u7sKFChgc0CWlIenSfbw8NCuXbssj3PlymX5bvr6+lqGuLRr187mbFJyXnjhBTk7O+uvv/5K8WDqr7/+0scff6yAgAA1adIk0T43b97U0qVLE51x9eFfwh/et4WFhcnBwcFmSFL+/PllNpsVEREhZ2dnm1/Ljx8/rlKlSmnevHmWSQbs7OxUr149jRkzxioEpvQdDw8PT3TGs3z58unEiROJbjOeLewP7sudO3eixwc3b96U2WzW888/n+hyCeGqVq1aioqK0tGjR3Xw4EHVrFlTXl5ecnZ21uHDh3Xo0CEVK1ZMBQsWlCS98847VtPZT548We3atZN0/7itdu3allkP//77b3Xt2lWffPKJVbhK6dggPDzcZr8k3d8HPTzEGKlHuIIh8uTJo6tXryb7vCRdv37dsuOQ7l/L8ODz7u7uun79utWyt2/ftjqQT+g7ZswY+fj42LzWw+O5H9yZv/LKK1ZnsowY+50UV1dXSfeHDSV29ii5cdLS/eGQD1+gGxsbazORSHoqUKCAjh07ZtN+48aNVE9fi6wnW7Zsqlatmvbv36+4uDjLwYqrq6vl4CKxawOzZ8+e6h8nHlagQAHLgVpS639QhQoVtHTpUt24cSPJi7sfR2RkpIYPH66SJUva/Fr9IFdXV73wwgt69dVXbZ57+Dvy8IGlq6urYmNjFRYWZtlfSPf3iyaTyXIm6sH35UENGjRQgwYNLL/eT548WaNHj0406CVX/+nTp23ar1+/blUTnl3sD5Ln6uoqk8mkL774ItGhhQlTo5coUUIFChTQwYMH9euvv+rll1+WnZ2dqlevrl9++UWHDx+2ut5q4MCBVsOKkztmKF++vOrWrZvohDYp1f7wMZd0fx/E9z/tCFcwhK+vrxYvXqyLFy8mOnSmUqVKcnBw0Pbt261mFNq2bZvy5ctnuf+Sj4+Pvv/+e40ePdqyA39whjvp/nDFQoUK6fz581Y7ntQoWLCgVbhLT1WrVlWOHDl0+fJlNW7cOMl+CTvjhyftKFiwoGJjY3Xu3DnL9U0HDhywGuOe3ipWrKgNGzbo/PnzKlasmKT7vyj+888/ql69+hOrA0/ea6+9pr59+2revHlJniU2kqOj4yMdiP3222/KlSuXZfKY1Pr+++8VGRmZ4muNHz9ely9f1ldffZXslOm+vr46ceKEypcv/0i/mEuyfIe2b99uuVYj4XH58uXl7OwsSSnWmitXLjVv3lxHjhx55KE81atX144dO3Tq1CnLUPCwsDD99NNPVjU9eFYez55nfX+QnITLA27dumWZ7TcpNWrU0O7du3Xs2DFNnjxZ0v2hu9u3b9f//vc/tW7d2tK3aNGiiQaq//77z+Zsc3x8vM6ePfvI992qXr26Fi1apB9//FH16tWTdH845rfffmv1bzzf/0dDuILi4+P1+++/27Tny5fPckCdkp49e2rjxo3q2rWr+vXrp2LFiun8+fM6c+aM3n77bbm5ualr165atGiR5Z5Le/bs0ebNm/X+++9bDkr69OmjDh06aMCAAercubMuXLigRYsWWQ0LNJlMCgwM1IgRIxQZGamGDRsqR44cunjxovbs2aNhw4aly030HpWLi4sGDx6sadOm6fLly6pVq5bs7e11/vx5fffddwoODlaOHDks1zqsXLlSL730kpycnOTt7a0GDRrI2dlZ7733nt544w1dvnxZy5Yts3ov0lv79u01b948vfnmm5bhk7Nnz1b+/PlTPbwDWVPDhg3Vp08fzZo1S//884+aNWumAgUKKCIiQgcPHtS1a9eeyNnLf/75R9OnT5e/v788PDwUGRmp3bt366uvvtLw4cOtZi8rX7682rZtq0mTJkmSZfa+KlWqyMXFRUeOHNH8+fNVsWJFvfTSS0m+5tatW7Vu3Tp16tRJkZGRie4fE+6FNXjwYHXo0EG9e/fWK6+8ovz58+u///7TL7/8oho1aqhly5ZJvk7ZsmXVpEkTTZkyRVFRUSpVqpQ2bdqkw4cP69NPP032fVm9erV+//131a9fX+7u7rpw4YI2bdqkunXrpvCOWmvXrp2WLFmivn37aujQoZbZArNly6YePXpY+nl6eurAgQPat2+fXFxcVLRo0Uc+kEXW9SzvD1JSqlQpdenSRSNHjlTv3r1VuXJlxcbG6syZM/r555+tvss1atTQhx9+qLx581quf6pZs6aCgoIs/5+S119/XSVLllSjRo3k4eGhmzdvau3atTp+/LjeeeedR6q9YcOG8vHx0dtvv6233nrLMlvg1atXNWvWLEs/T09PbdiwQbt27bIM8XxSP1RnRYQrKCoqyuoXygTJzZT3sLx582rVqlX66KOPNH36dN29e1ceHh5WQ2VGjhyp3Llz6+uvv9a8efPk4eFhNUuWdH9nOHPmTE2fPl0DBw7Uc889pxkzZtjccLBZs2ZycXHRvHnzFBISIun+mOz69etnyB3Tk9KrVy8VLFhQn3/+uVasWKFs2bKpePHiatiwoeWMVfny5TVo0CB99dVX+uyzz1S4cGHLdO6zZs3S1KlTNWDAAJUrV05BQUFP9P5STk5OWrx4sT744AONGDFCBQsWVP/+/bVhw4ZEL57H0+Wtt95S9erVtXLlSo0bN063b9+Wq6urKlSooEmTJqV4PxYj5M+fXy4uLvr000917do15c6dW56enpo9e7bNAVFcXJzVBeClS5fWqlWr9OWXXyoqKkoFCxZUhw4dNHjw4GSnlN67d68kac2aNUneziDhes0SJUroq6++0ieffKJx48YpMjJS7u7uqlmzpry9vVPcvmnTpunjjz/WwoULdevWLXl6emrWrFkp/gLu7e2t77//XpMnT9atW7fk7u6uFi1aPPK9/HLlyqXly5drypQpev/99xUfH69q1appxYoVVrPDDR8+XGPHjtWgQYN0584dq+s/8Gx4VvcHqfHee++pVKlSWrNmjebMmaOcOXOqVKlSNpPVJExiVb16dcsPlAlnqXPmzKkSJUqk+FpvvPGGtm3bppkzZ1q9B8HBwUleG5qUhFmag4KCNG3aNMvs0YsXL7bclyzhNc+dO6dRo0YpPDw80ZtJ4/+YzGazOaOLAJB13Lp1Sy+99JJ69uypgQMHZnQ5AAAAmQZnrgAka8GCBcqfP788PDx07do1LV68WHFxcWrfvn1GlwYAAJCpEK4AJMvOzk5z587VlStXZG9vr8qVK2vp0qU2N5QEAAB41jEsEAAAAAAMYJfRBQAAAADA04BwBQAAAAAGIFwBAAAAgAEIVwAAAABgAMIVAAAAABiAcAUAAAAABiBcAQAAAIABCFcAAAAAYID/B9alq2MDx2hAAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot Accuracies of the model\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Plot the accuracies\n",
        "# results['eval_accuracy'], average_accuracy, average_accuracy_few_shot\n",
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(10, 6))\n",
        "accuracies = [results['eval_accuracy'], average_accuracy, average_accuracy_few_shot]\n",
        "labels = ['Encoder-Finetuning', 'GPT-3.5 Zero-shot', 'GPT-3.5 Few-Shot']\n",
        "sns.barplot(x=labels, y=accuracies)\n",
        "plt.title('Accuracy of the Models on EJ Indicators Classification Task')\n",
        "plt.ylabel('Accuracy')\n",
        "for i, v in enumerate(accuracies):\n",
        "    plt.text(i, v + 0.01, f'{v:.2f}', ha='center', va='bottom')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "_xbAmsXHBl8E",
        "outputId": "ce4524c7-dc64-4510-8db6-22f280ea3edd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot Accuracies of the model\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "samples_per_sec = results['eval_samples_per_second']\n",
        "# Plot the accuracies\n",
        "# results['eval_accuracy'], average_accuracy, average_accuracy_few_shot\n",
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(10, 6))\n",
        "time_taken_list = [samples_per_sec, len(test_data)/24, len(test_data)/26]\n",
        "labels = ['Encoder-Finetuned', 'GPT-3.5 Zero-shot', 'GPT-3.5 Few-Shot']\n",
        "sns.barplot(x=labels, y=time_taken_list)\n",
        "plt.title('Samples Per Second for inference')\n",
        "plt.ylabel('Samples Per Second')\n",
        "for i, v in enumerate(time_taken_list):\n",
        "    plt.text(i, v + 0.01, f'{v:.2f}', ha='center', va='bottom')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PUW_8VYNBl8E"
      },
      "outputs": [],
      "source": []
    }
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