Implementation_Notebook_CS747_FinalProject_NoahBERT.ipynb

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      "name": "python"
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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Clone git repo"
      ],
      "metadata": {
        "id": "bo_BkcKhLQoJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !apt-get install git-lfs git\n",
        "!git clone https://github.com/PratishMashankar/cs747-noahbert.git\n",
        "!git config --global user.email \"psmashankar@gmail.com\" #change this with your email\n",
        "!git config --global user.name \"Pratish Mashankar\" #change this with your name"
      ],
      "metadata": {
        "id": "4eMbfVrYoGUd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0d44c9c-42ba-45d1-b967-a5bf966c624d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'cs747-noahbert'...\n",
            "remote: Enumerating objects: 23, done.\u001b[K\n",
            "remote: Counting objects: 100% (23/23), done.\u001b[K\n",
            "remote: Compressing objects: 100% (19/19), done.\u001b[K\n",
            "remote: Total 23 (delta 3), reused 18 (delta 1), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (23/23), 1.79 MiB | 18.70 MiB/s, done.\n",
            "Resolving deltas: 100% (3/3), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adding a hardware accelerator\n",
        "\n",
        "Run the following cell to confirm that the GPU is detected."
      ],
      "metadata": {
        "id": "c4V2Xtx5mu-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# Confirm that the GPU is detected\n",
        "\n",
        "assert torch.cuda.is_available()\n",
        "\n",
        "# Get the GPU device name.\n",
        "device_name = torch.cuda.get_device_name()\n",
        "n_gpu = torch.cuda.device_count()\n",
        "print(f\"Found device: {device_name}, n_gpu: {n_gpu}\")\n",
        "device = torch.device(\"cuda\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUWlRPdxmui0",
        "outputId": "ca9d294c-bc8d-49c6-e137-0c3ed6a3b35d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found device: Tesla V100-SXM2-16GB, n_gpu: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting random seed\n",
        "\n",
        "We defined a function named `seed_everything` to enforce result reproducibility in machine learning experiments. In this function, we set the random seed for various libraries crucial to the workflow. Specifically, we configured the seed for Python's built-in `random` module and NumPy using `random.seed(seed)` and `np.random.seed(seed)`, respectively. For PyTorch, a popular deep learning framework, we ensured seed consistency for both CPU and GPU operations using `torch.manual_seed(seed)` and `torch.cuda.manual_seed(seed)`. The function extends this seed configuration to all available GPUs with `torch.cuda.manual_seed_all(seed)`. To enhance reproducibility further, we disabled CuDNN benchmarking and enabled deterministic CuDNN algorithms through `torch.backends.cudnn.benchmark = False` and `torch.backends.cudnn.deterministic = True`. This comprehensive approach ensures that running the code with the specified seed produces identical results, facilitating debugging and result consistency across different executions."
      ],
      "metadata": {
        "id": "s_LnqMvgivXm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "\n",
        "def seed_everything(seed=42):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "seed_everything()"
      ],
      "metadata": {
        "id": "fi0cfW62iv9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing Hugging Face's Transformers library\n",
        "We will use Hugging Face's Transformers (https://github.com/huggingface/transformers), an open-source library that provides general-purpose architectures for natural language understanding and generation with a collection of various pretrained models made by the NLP community. This library will allow us to easily use pretrained models like `BERT` and perform experiments on top of them. We can use these models to solve downstream target tasks, such as text classification, question answering, and sequence labeling.\n",
        "\n",
        "Run the following cell to install Hugging Face's Transformers library"
      ],
      "metadata": {
        "id": "vl_r74tFao0Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install -U -q PyDrive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjeLDXyjaqFT",
        "outputId": "7e96ed14-f6ac-49f5-d4e8-bde38ce926da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Loading"
      ],
      "metadata": {
        "id": "LrWPQi0em_2w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Davidson et al Dataset\n",
        "\n",
        "https://github.com/t-davidson/hate-speech-and-offensive-language/tree/master\n",
        "\n",
        "Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 512-515. https://doi.org/10.1609/icwsm.v11i1.14955"
      ],
      "metadata": {
        "id": "0x60WMfmnI6d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "w-xQoUGkhYzX",
        "outputId": "49056439-2007-45c2-b5cc-8587dbd3e9e1"
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        {
          "output_type": "execute_result",
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              "                                                   tweet  class\n",
              "0      !!! RT @mayasolovely: As a woman you shouldn't...      2\n",
              "1      !!!!! RT @mleew17: boy dats cold...tyga dwn ba...      1\n",
              "2      !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...      1\n",
              "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...      1\n",
              "4      !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...      1\n",
              "...                                                  ...    ...\n",
              "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...      1\n",
              "24779  you've gone and broke the wrong heart baby, an...      2\n",
              "24780  young buck wanna eat!!.. dat nigguh like I ain...      1\n",
              "24781              youu got wild bitches tellin you lies      1\n",
              "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...      2\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-86f4d6a7-b03e-4dd7-acaa-cf01ad2af45e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import pandas as pd\n",
        "davidson_df = pd.read_csv('/content/cs747-noahbert/data/labeled_data.csv')\n",
        "davidson_df = davidson_df[['tweet','class']]\n",
        "davidson_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MLMA Dataset\n",
        "\n",
        "https://huggingface.co/datasets/nedjmaou/MLMA_hate_speech/tree/main \\\n",
        "Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, Yangqiu Song, and Dit-Yan Yeung. 2019. Multilingual and Multi-Aspect Hate Speech Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4675–4684, Hong Kong, China. Association for Computational Linguistics.\n",
        "\n"
      ],
      "metadata": {
        "id": "un11JvtjnWW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "mlma_df = pd.read_csv('/content/cs747-noahbert/data/en_dataset_with_stop_words.csv')\n",
        "mlma_df = mlma_df[['tweet','sentiment']]\n",
        "mlma_df.rename(columns={'sentiment': 'class'}, inplace=True)\n",
        "mlma_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Uyro6XzMNcjv",
        "outputId": "4267fd72-d578-4340-8555-9545d27c95fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  tweet  \\\n",
              "0     If America had another 8 years of Obama's ideo...   \n",
              "1     Most Canadians have never met seen or associat...   \n",
              "2                            Hahaha grow up faggot @URL   \n",
              "3     @user queue is fucking retarded it makes every...   \n",
              "4     @user Que ce ne soit pas des Burundais refugie...   \n",
              "...                                                 ...   \n",
              "5642  @user @user @user Maybe but @user who lied abo...   \n",
              "5643              @user @user You\\u2019re a retard :) x   \n",
              "5644  (2/2) like no you fucking twat I'm doing damag...   \n",
              "5645            If your bf is retarded guess what u r 2   \n",
              "5646             @user whats the ching chong stuff mean   \n",
              "\n",
              "                                             class  \n",
              "0     fearful_abusive_hateful_disrespectful_normal  \n",
              "1                                        offensive  \n",
              "2                                        offensive  \n",
              "3                                offensive_hateful  \n",
              "4                                   hateful_normal  \n",
              "...                                            ...  \n",
              "5642                                       hateful  \n",
              "5643                                       hateful  \n",
              "5644                                       hateful  \n",
              "5645                                        normal  \n",
              "5646                                     offensive  \n",
              "\n",
              "[5647 rows x 2 columns]"
            ],
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "q_u7hupqME9O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Length of Datasets"
      ],
      "metadata": {
        "id": "GOCwtrYHOjE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Length of Davidson Dataset:\", len(davidson_df))\n",
        "print(\"Length of MLMA Dataset:\", len(mlma_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiDr4JtwOhyK",
        "outputId": "45a40c1e-0b71-4099-c654-76a59c24b417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of Davidson Dataset: 24783\n",
            "Length of MLMA Dataset: 5647\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class Distribution"
      ],
      "metadata": {
        "id": "SZ43XXx1OlTi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Davidson et al Dataset\n",
        "\n",
        "We observe three classes \\\n",
        "**0**: Hate speech \\\n",
        "**1**: Offensive language \\\n",
        "**2**: Neither"
      ],
      "metadata": {
        "id": "L241XDhMnmYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Printing a few examples\n",
        "# Examples where sentiment is hateful\n",
        "hateful_examples = davidson_df[davidson_df['class'] == 0]['tweet'].head(2).tolist()\n",
        "\n",
        "# Examples where sentiment is offensive\n",
        "offensive_examples = davidson_df[davidson_df['class'] == 1]['tweet'].head(2).tolist()\n",
        "\n",
        "# Examples where sentiment is normal\n",
        "normal_examples = davidson_df[davidson_df['class'] == 2]['tweet'].head(2).tolist()\n",
        "\n",
        "print(\"-------------- Davidson Examples --------------\\n\")\n",
        "print(\"Hate Speech:\", hateful_examples)\n",
        "print(\"Offensive Language:\", offensive_examples)\n",
        "print(\"Normal\", normal_examples)\n",
        "print(\"\\n---------------------------------------------\\n\")\n",
        "\n",
        "# Assuming df is your DataFrame with 'sentiment' column\n",
        "sentiment_counts = davidson_df.groupby('class').size()\n",
        "\n",
        "# Create a horizontal bar plot\n",
        "ax = sentiment_counts.plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'), figsize=(6, 4))\n",
        "\n",
        "# Remove the top and right spines\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Sentiment Class')\n",
        "plt.title('Sentiment Distribution for Davidson Dataset')\n",
        "\n",
        "# Add count labels at the end of each bar\n",
        "for i, count in enumerate(sentiment_counts):\n",
        "    plt.text(count, i, str(count), ha='left', va='center')\n",
        "\n",
        "# Modify y-axis tick labels\n",
        "tick_labels = ['Hateful', 'Offensive', 'Neither']\n",
        "plt.yticks(range(len(tick_labels)), tick_labels)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "DpdugUFl_icL",
        "outputId": "e7602bbe-97c7-4607-a354-754a45bac58f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------- Davidson Examples --------------\n",
            "\n",
            "Hate Speech: ['\"@Blackman38Tide: @WhaleLookyHere @HowdyDowdy11 queer\" gaywad', '\"@CB_Baby24: @white_thunduh alsarabsss\" hes a beaner smh you can tell hes a mexican']\n",
            "Offensive Language: ['!!!!! RT @mleew17: boy dats cold...tyga dwn bad for cuffin dat hoe in the 1st place!!', '!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby4life: You ever fuck a bitch and she start to cry? You be confused as shit']\n",
            "Normal [\"!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. &amp; as a man you should always take the trash out...\", '\" momma said no pussy cats inside my doghouse \"']\n",
            "\n",
            "---------------------------------------------\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MLMA Dataset"
      ],
      "metadata": {
        "id": "sfQOt6oTnpwY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming df is your DataFrame with 'sentiment' column\n",
        "sentiment_counts = mlma_df.groupby('class').size()\n",
        "\n",
        "# Create a horizontal bar plot\n",
        "ax = sentiment_counts.plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'), figsize=(12, 10))\n",
        "\n",
        "# Remove the top and right spines\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Sentiment Class')\n",
        "plt.title('Sentiment Distribution')\n",
        "\n",
        "# Add count labels at the end of each bar\n",
        "for i, count in enumerate(sentiment_counts):\n",
        "    plt.text(count, i, str(count), ha='left', va='center')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "rXVVI_T4N6_z",
        "outputId": "5e8ebba8-8420-4533-bb6b-8dc3aa78a2ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Engineering"
      ],
      "metadata": {
        "id": "PkWW6qcMYU4C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class reorganization for MLMA dataset\n",
        "\n",
        "Class 0: Hate speech \\\n",
        "Class 1: Offensive language \\\n",
        "Class 2: Neither"
      ],
      "metadata": {
        "id": "Qk5o60OEZDop"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the class to exclude any combination with Normal\n",
        "class_to_exclude = 'normal'\n",
        "\n",
        "# Function to check if a class contains the specified class to exclude\n",
        "def contains_class_to_exclude(class_name):\n",
        "    return class_to_exclude not in class_name\n",
        "\n",
        "# Filtering out samples where the 'class' column includes the specified class to exclude\n",
        "filtered_df = mlma_df[mlma_df['class'].apply(contains_class_to_exclude)]\n",
        "\n",
        "# Define classes that you want to keep and combinations to exclude\n",
        "classes_to_keep = ['offensive', 'hateful']\n",
        "\n",
        "# Function to check if a class is in the specified classes or a combination of them\n",
        "def keep_class(class_name):\n",
        "    return any(class_part in class_name for class_part in classes_to_keep)\n",
        "\n",
        "# Filtering out samples where the 'class' column does not meet the specified conditions\n",
        "filtered_df_combination = filtered_df[filtered_df['class'].apply(keep_class)]\n",
        "\n",
        "# Function to check if a class is a combination of two or more specified classes\n",
        "def is_combination(class_name):\n",
        "    count = sum(class_part in class_name for class_part in classes_to_keep)\n",
        "    return count >= 2\n",
        "\n",
        "# Filtering out samples where the 'class' column is a combination of two or more specified classes\n",
        "filtered_df_final = filtered_df_combination[~filtered_df_combination['class'].apply(is_combination)]\n",
        "\n",
        "#Keep only those colums with normal values\n",
        "normal_df =  mlma_df[mlma_df['class'] == 'normal']\n",
        "\n",
        "# Concatenate the two filtered DataFrames\n",
        "result_df = pd.concat([filtered_df_final, normal_df])\n",
        "\n",
        "# Function to classify the class based on keywords\n",
        "def classify_class(class_name):\n",
        "    if 'offensive' in class_name:\n",
        "        return 'offensive'\n",
        "    elif 'hateful' in class_name:\n",
        "        return 'hateful'\n",
        "    else:\n",
        "        return 'normal'\n",
        "\n",
        "# Create a new column 'classification' based on the 'class' column\n",
        "result_df['class'] = result_df['class'].apply(classify_class)\n",
        "\n",
        "# Reset the index of the resulting DataFrame\n",
        "result_df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# Map categorical class labels to numerical values (0 for 'hateful', 1 for 'offensive', 2 for 'normal')\n",
        "result_df['class'] = result_df['class'].replace({'hateful': 0, 'offensive': 1, 'normal': 2})\n",
        "\n",
        "# Display the DataFrame with the new 'classification' column\n",
        "mlma_reorg_dataset = result_df.copy()\n",
        "mlma_reorg_dataset"
      ],
      "metadata": {
        "id": "XDI7nIq9Y5Tt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f9f89edd-0ce3-408a-ffbf-3b325e2d4781"
      },
      "execution_count": null,
      "outputs": [
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              "                                                  tweet  class\n",
              "0     Most Canadians have never met seen or associat...      1\n",
              "1                            Hahaha grow up faggot @URL      1\n",
              "2              dude i\\u2019m so tired of being retarded      1\n",
              "3     Pretty sure if an employee of Philip Green cal...      1\n",
              "4     This bitch Is retarded \\ud83d\\ude02\\ud83d\\ude0...      1\n",
              "...                                                 ...    ...\n",
              "4472  Some cunt trying to hack into my twitter!!! Fu...      2\n",
              "4473                             intellectual terrorist      2\n",
              "4474  @user You seem to care more about illegal immi...      2\n",
              "4475  @user The helpers are losing the battle and so...      2\n",
              "4476            If your bf is retarded guess what u r 2      2\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#finding the sentiments of the dataframe\n",
        "sentiment_counts = mlma_reorg_dataset.groupby('class').size()\n",
        "\n",
        "# Create a horizontal bar plot\n",
        "ax = sentiment_counts[::-1].plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'), figsize=(6, 4))\n",
        "\n",
        "# Remove the top and right spines\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Sentiment Class')\n",
        "plt.title('Sentiment Distribution for reorganized MLMA')\n",
        "\n",
        "# Add count labels at the end of each bar\n",
        "for i, count in enumerate(sentiment_counts[::-1]):\n",
        "    plt.text(count, i, str(count), ha='left', va='center')\n",
        "\n",
        "# Modify y-axis tick labels to switch the position of 'Hateful' and 'Neither'\n",
        "tick_labels = ['Neither', 'Offensive', 'Hateful'][::-1]\n",
        "plt.yticks(range(len(tick_labels)), tick_labels)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "5OlXawCzGSuH",
        "outputId": "b2254352-2dcc-40bf-8983-1e240c944bc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Combining Data\n",
        "\n",
        "Before forming the final dataset, we ensured that the 'class' column in two separate dataframes, `davidson_df` and `mlma_reorg_dataset`, is of integer type. Subsequently, we created a dataframe named `neither_df` by concatenating rows from both datasets where the 'class' is equal to 2 (neither class). We then sampled 3000 records from this dataframe. Next, we created a dataframe called `hateful_df` by concatenating rows from both datasets where the 'class' is equal to 0 (hateful class), resulting in 1981 records. Following this, we filtered offensive records from both datasets (`davidson_df` and `mlma_reorg_dataset`) based on a condition related to tweet length, sampled 2000 records from each, and created `offensive_davidson` and `offensive_mlma` dataframes. These were then combined into an `offensive_df`. Finally, we concatenated all three dataframes (`offensive_df`, `neither_df`, and `hateful_df`) into the `combined_df`, and we shuffled the rows for randomness using a random seed of 42. The resulting `combined_df` can be used for training and testing.\n",
        "\n"
      ],
      "metadata": {
        "id": "t3YAZvcMI8Q7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import shuffle\n",
        "import numpy as np\n",
        "\n",
        "seed_everything()\n",
        "\n",
        "# Ensure 'class' column is integer\n",
        "davidson_df['class'] = davidson_df['class'].astype(int)\n",
        "mlma_reorg_dataset['class'] = mlma_reorg_dataset['class'].astype(int)\n",
        "\n",
        "# Combine neither classes\n",
        "neither_df = pd.concat([davidson_df[davidson_df['class'] == 2], mlma_reorg_dataset[mlma_reorg_dataset['class'] == 2]])\n",
        "\n",
        "# Sample 3000 records from neither_df\n",
        "neither_df = neither_df.sample(n=3000)\n",
        "print(f\"Number of records labelled Neither: {len(neither_df)}\")\n",
        "\n",
        "# Combine hateful classes\n",
        "hateful_df = pd.concat([davidson_df[davidson_df['class'] == 0], mlma_reorg_dataset[mlma_reorg_dataset['class'] == 0]])\n",
        "\n",
        "# We cannot sample 2000 records from hateful_df as only 1981 example are present\n",
        "# hateful_df = hateful_df.sample(n=2000, replace=True)\n",
        "print(f\"Number of records labelled Hateful: {len(hateful_df)}\")\n",
        "\n",
        "# Filter offensive records with tweet length > 5 from davidson_df\n",
        "offensive_davidson = davidson_df[(davidson_df['class'] == 1) & (davidson_df['tweet'].str.split().apply(len) > 5)]\n",
        "\n",
        "# Sample 2000 records from offensive_davidson\n",
        "offensive_davidson = offensive_davidson.sample(n=2000)\n",
        "\n",
        "# Filter offensive records with tweet length > 5 from mlma_reorg_dataset\n",
        "offensive_mlma = mlma_reorg_dataset[(mlma_reorg_dataset['class'] == 1) & (mlma_reorg_dataset['tweet'].str.split().apply(len) > 5)]\n",
        "\n",
        "# Sample 2000 records from offensive_mlma\n",
        "offensive_mlma = offensive_mlma.sample(n=2000)\n",
        "\n",
        "# Combine offensive records from both dataframes\n",
        "offensive_df = pd.concat([offensive_davidson, offensive_mlma])\n",
        "print(f\"Number of records labelled Offensive: {len(offensive_df)}\")\n",
        "\n",
        "# Combine the dataframes\n",
        "final_df = pd.concat([offensive_df, neither_df, hateful_df])\n",
        "\n",
        "# Shuffle the combined dataframe\n",
        "final_df = shuffle(final_df)\n",
        "\n",
        "# Reset the index of the resulting DataFrame\n",
        "final_df.reset_index(drop=True, inplace=True)\n",
        "print(f\"Length of Final Dataset: {len(final_df)}\")\n",
        "\n",
        "# Rename the 'class' column to 'sentiment'\n",
        "final_df.rename(columns={'class': 'sentiment'}, inplace=True)\n",
        "\n",
        "# Add a new column 'language' with all values set to 'en'\n",
        "final_df['language'] = 'en'\n",
        "\n",
        "# Save the final data\n",
        "final_df.to_csv(\"final_data.csv\")\n",
        "\n",
        "final_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "_lvNYvbMMrTI",
        "outputId": "590e577e-7e6a-4fbc-ce3e-de7053932703"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records labelled Neither: 3000\n",
            "Number of records labelled Hateful: 1981\n",
            "Number of records labelled Offensive: 4000\n",
            "Length of Final Dataset: 8981\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  tweet  sentiment language\n",
              "0                          oh well. he's a fag anyways.          0       en\n",
              "1     @HeilSidious Sion is a faggot. Very meh ganks ...          0       en\n",
              "2         @MrInfernusHD Squad builders gets you bitches          1       en\n",
              "3     RT @TheKingScottyD: Side niggas get pussy and ...          1       en\n",
              "4     RT @WorkoutHumor: These hoes ain't loyal http:...          1       en\n",
              "...                                                 ...        ...      ...\n",
              "8976  I used to get GRs a lot, tho. maybe that's why...          2       en\n",
              "8977  Did the Italian bird just call Janet a calzone...          2       en\n",
              "8978  @GoneWithKalijah I watched the 1st season bc I...          1       en\n",
              "8979  @TechN9ne why did they add your name all ghett...          2       en\n",
              "8980  @JStac825 @ThatAngryNigga at least after nearl...          1       en\n",
              "\n",
              "[8981 rows x 3 columns]"
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing Final Data"
      ],
      "metadata": {
        "id": "W25U8bqXTIjG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Printing a few examples\n",
        "# Examples where sentiment is hateful\n",
        "hateful_examples = final_df[final_df['sentiment'] == 0]['tweet'].head(2).tolist()\n",
        "\n",
        "# Examples where sentiment is offensive\n",
        "offensive_examples = final_df[final_df['sentiment'] == 1]['tweet'].head(2).tolist()\n",
        "\n",
        "# Examples where sentiment is normal\n",
        "normal_examples = final_df[final_df['sentiment'] == 2]['tweet'].head(2).tolist()\n",
        "\n",
        "print(\"-------------- Final Dataset Examples --------------\\n\")\n",
        "print(\"Hate Speech:\", hateful_examples)\n",
        "print(\"Offensive Language:\", offensive_examples)\n",
        "print(\"Neither\", normal_examples)\n",
        "print(\"\\n---------------------------------------------\\n\")\n",
        "\n",
        "# Assuming df is your DataFrame with 'sentiment' column\n",
        "sentiment_counts = final_df.groupby('sentiment').size()\n",
        "\n",
        "# Create a horizontal bar plot\n",
        "ax = sentiment_counts.plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'), figsize=(6, 4))\n",
        "\n",
        "# Remove the top and right spines\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Sentiment Class')\n",
        "plt.title('Sentiment Distribution for Final Dataset')\n",
        "\n",
        "# Add count labels at the end of each bar\n",
        "for i, count in enumerate(sentiment_counts):\n",
        "    plt.text(count, i, str(count), ha='left', va='center')\n",
        "\n",
        "# Modify y-axis tick labels\n",
        "tick_labels = ['Hateful', 'Offensive', 'Neither']\n",
        "plt.yticks(range(len(tick_labels)), tick_labels)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ZdPwSXiMRaHc",
        "outputId": "63e86d82-860f-4f77-ac19-5dab0912cfad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------- Final Dataset Examples --------------\n",
            "\n",
            "Hate Speech: [\"oh well. he's a fag anyways.\", \"@HeilSidious Sion is a faggot. Very meh ganks pre-6 and post-6 his ganks are still pretty meh he's just a tank, nothing special imo Mao +\"]\n",
            "Offensive Language: ['@MrInfernusHD Squad builders gets you bitches', 'RT @TheKingScottyD: Side niggas get pussy and main niggas get arguments']\n",
            "Neither ['RT @michaelpshipley: Martin Short On - CONAN - Oct 3, 2014\\n\\n\"With your yellow skin and red hair you look like a #2 pencil.\"\\n\\nhttps://t.co/U&#8230;', 'When a once relatively respected &amp; trusted country is turned into a total shithole we need a larger more populate\\\\u2026 @URL']\n",
            "\n",
            "---------------------------------------------\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "ZUd8ATFNgyli"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining helper functions\n",
        "\n",
        "The tokenize_and_format function uses bert-base-uncased as the\n",
        "model for the tokenizer. If we are to use a different model for training or for running predictions in a different notebook or python file, we will need to\n",
        "to change the model name as well in the tokenizer, otherwise we will get\n",
        "arbitrarily incorrect results down the line. These functions have been adapted from the pytorch implementation of BERT"
      ],
      "metadata": {
        "id": "ecy7pP_vTp35"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
        "from torch.utils.data import TensorDataset, random_split\n",
        "from transformers import BertTokenizer\n",
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "import sys\n",
        "import numpy as np\n",
        "import time\n",
        "import datetime\n",
        "\n",
        "def tokenize_and_format(sentences):\n",
        "  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\n",
        "\n",
        "  # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
        "  input_ids = []\n",
        "  attention_masks = []\n",
        "\n",
        "  # For every sentence...\n",
        "  for sentence in sentences:\n",
        "      # `encode_plus` will:\n",
        "      #   (1) Tokenize the sentence.\n",
        "      #   (2) Prepend the `[CLS]` token to the start.\n",
        "      #   (3) Append the `[SEP]` token to the end.\n",
        "      #   (4) Map tokens to their IDs.\n",
        "      #   (5) Pad or truncate the sentence to `max_length`\n",
        "      #   (6) Create attention masks for [PAD] tokens.\n",
        "      encoded_dict = tokenizer.encode_plus(\n",
        "                          sentence,                      # Sentence to encode.\n",
        "                          add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
        "                          max_length = 64,           # Pad & truncate all sentences.\n",
        "                          padding = 'max_length',\n",
        "                          truncation = True,\n",
        "                          return_attention_mask = True,   # Construct attn. masks.\n",
        "                          return_tensors = 'pt',     # Return pytorch tensors.\n",
        "                    )\n",
        "\n",
        "      # Add the encoded sentence to the list.\n",
        "      input_ids.append(encoded_dict['input_ids'])\n",
        "\n",
        "      # And its attention mask (simply differentiates padding from non-padding).\n",
        "      attention_masks.append(encoded_dict['attention_mask'])\n",
        "  return input_ids, attention_masks\n",
        "\n",
        "def flat_accuracy(preds, labels):\n",
        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
      ],
      "metadata": {
        "id": "Zn_CDE9JTs-8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing Data\n",
        "\n",
        "We upload our final data to a dataframe `df`. We have a\n",
        "function to tokenize and format the data as BERT requires. Make sure that\n",
        "the csv file, titled final_data.csv, has one column \"tweet\" and another\n",
        "column \"sentiment\" containing integers/float."
      ],
      "metadata": {
        "id": "X3WXHLuVa6kb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from helpers import tokenize_and_format, flat_accuracy\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "seed_everything()\n",
        "\n",
        "# Read the Final Data\n",
        "df = pd.read_csv('/content/cs747-noahbert/data/final_data.csv')\n",
        "\n",
        "texts = df.tweet.values # this assumes that the column containing the text is called \"tweet\"\n",
        "labels = df.sentiment.values # this assumes that the column containing the labels is called \"class\"\n",
        "\n",
        "### tokenize_and_format() is a helper function provided in helpers.py ###\n",
        "### Make sure you use the correct model name for your tokenizer! ###\n",
        "input_ids, attention_masks = tokenize_and_format(texts)\n",
        "\n",
        "label_list = []\n",
        "for l in labels:\n",
        "  label_array = np.zeros(len(set(labels)))\n",
        "  label_array[int(l)-1] = 1\n",
        "  label_list.append(label_array)\n",
        "\n",
        "# Convert the lists into tensors.\n",
        "input_ids = torch.cat(input_ids, dim=0)\n",
        "attention_masks = torch.cat(attention_masks, dim=0)\n",
        "labels = torch.tensor(np.array(label_list))\n",
        "\n",
        "# Print sentence 0, now as a list of IDs.\n",
        "print('Original: ', texts[0])\n",
        "print('Token IDs:', input_ids[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0tAOATca9gx",
        "outputId": "e843ba8c-f035-4e59-8d8e-3b2987205c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:  oh well. he's a fag anyways.\n",
            "Token IDs: tensor([ 101, 2821, 2092, 1012, 2002, 1005, 1055, 1037, 6904, 2290, 4312, 2015,\n",
            "        1012,  102,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performing Train Test Split\n",
        "\n",
        "Here we split the dataset into 3 parts: a training set, a validation set, and a testing set. Each item in the dataset will be a 3-tuple containing an input_id tensor, an attention_mask tensor, and a label tensor."
      ],
      "metadata": {
        "id": "cYSbaU0ccQQE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "total = len(df)\n",
        "\n",
        "num_train = int(total * .8)\n",
        "num_val = int(total * .1)\n",
        "num_test = total - num_train - num_val\n",
        "\n",
        "# make lists of 3-tuples (already shuffled the dataframe in cell above)\n",
        "train_set = [(input_ids[i], attention_masks[i], labels[i]) for i in range(num_train)]\n",
        "val_set = [(input_ids[i], attention_masks[i], labels[i]) for i in range(num_train, num_val+num_train)]\n",
        "test_set = [(input_ids[i], attention_masks[i], labels[i]) for i in range(num_val + num_train, total)]\n",
        "\n",
        "train_text = [texts[i] for i in range(num_train)]\n",
        "val_text = [texts[i] for i in range(num_train, num_val+num_train)]\n",
        "test_text = [texts[i] for i in range(num_val + num_train, total)]\n",
        "\n",
        "print(f\"Length of Training set: {len(train_text)}\")\n",
        "print(f\"Length of Validation set: {len(val_text)}\")\n",
        "print(f\"Length of Testing set: {len(test_text)}\")"
      ],
      "metadata": {
        "id": "-9OSA-1pcgZk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "848171a6-84ef-4eaf-f388-f2c17d1e49f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of Training set: 7184\n",
            "Length of Validation set: 898\n",
            "Length of Testing set: 899\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BERT"
      ],
      "metadata": {
        "id": "jIBdjYS2gdPr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the model\n",
        "\n",
        "Here we choose the model we want to finetune from https://huggingface.co/transformers/pretrained_models.html. Because the task requires us to label sentences, we wil be using BertForSequenceClassification below. We may see a warning that states that `some weights of the model checkpoint at [model name] were not used when initializing. . .` This warning is expected and means that we should fine-tune our pre-trained model before using it on the downstream task. See [here](https://github.com/huggingface/transformers/issues/5421#issuecomment-652582854) for more info."
      ],
      "metadata": {
        "id": "PejdT5o5ekmM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
        "\n",
        "seed_everything()\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\n",
        "    \"bert-base-uncased\", # Use the 12-layer English BERT model, with an uncased vocab.\n",
        "    num_labels = 3, # The number of output labels.\n",
        "    output_attentions = False, # Whether the model returns attentions weights.\n",
        "    output_hidden_states = False, # Whether the model returns all hidden-states.\n",
        ")\n",
        "\n",
        "# Tell pytorch to run this model on the GPU.\n",
        "model.cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQyhdNjedmCR",
        "outputId": "72922bba-5867-47a8-876d-68b7c0eb11da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining Hyperparameters\n",
        "\n",
        "We define our fine-tuning hyperparameters in the cell below"
      ],
      "metadata": {
        "id": "iLEJSk4hd-vw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "batch_size = 45\n",
        "optimizer = AdamW(model.parameters(),lr=0.00001)\n",
        "epochs = 50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLry5Gwhd_7o",
        "outputId": "780fb812-9a3a-42f5-d1e4-bdf470f72a3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetuning the model\n",
        "Here we provide code for fine-tuning the model, monitoring the loss, and checking the validation accuracy."
      ],
      "metadata": {
        "id": "cDpNEMnretn9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Defining function to print validation accuracy"
      ],
      "metadata": {
        "id": "8KKYlIuN_oo_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer\n",
        "import pandas as pd\n",
        "\n",
        "seed_everything()\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# function to get validation accuracy\n",
        "def get_validation_performance(val_set):\n",
        "    # Put the model in evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    # Tracking variables\n",
        "    total_eval_accuracy = 0\n",
        "    total_eval_loss = 0\n",
        "\n",
        "    num_batches = int(len(val_set)/batch_size) + 1\n",
        "\n",
        "    incorrect_predictions = []\n",
        "    total_correct = 0\n",
        "    pred_df = pd.DataFrame(columns=['tweet', 'sentiment', 'language'])\n",
        "\n",
        "    for i in range(num_batches):\n",
        "\n",
        "      end_index = min(batch_size * (i+1), len(val_set))\n",
        "\n",
        "      batch = val_set[i*batch_size:end_index]\n",
        "\n",
        "      if len(batch) == 0: continue\n",
        "\n",
        "      input_id_tensors = torch.stack([data[0] for data in batch])\n",
        "      input_mask_tensors = torch.stack([data[1] for data in batch])\n",
        "      label_tensors = torch.stack([data[2] for data in batch])\n",
        "\n",
        "      # Move tensors to the GPU\n",
        "      b_input_ids = input_id_tensors.to(device)\n",
        "      b_input_mask = input_mask_tensors.to(device)\n",
        "      b_labels = label_tensors.to(device)\n",
        "\n",
        "      # Tell pytorch not to bother with constructing the compute graph during\n",
        "      # the forward pass, since this is only needed for backprop (training).\n",
        "      with torch.no_grad():\n",
        "\n",
        "        # Forward pass, calculate logit predictions.\n",
        "        outputs = model(b_input_ids,\n",
        "                                token_type_ids=None,\n",
        "                                attention_mask=b_input_mask,\n",
        "                                labels=b_labels)\n",
        "        loss = outputs.loss\n",
        "        logits = outputs.logits\n",
        "\n",
        "        # Accumulate the validation loss.\n",
        "        total_eval_loss += loss.item()\n",
        "\n",
        "        # Move logits and labels to CPU\n",
        "        logits = (logits).detach().cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "\n",
        "        # Calculate the number of correctly labeled examples in batch\n",
        "        pred_flat = np.argmax(logits, axis=1).flatten()\n",
        "        labels_flat = np.argmax(label_ids, axis=1).flatten()\n",
        "\n",
        "        num_correct = np.sum(pred_flat == labels_flat)\n",
        "        total_correct += num_correct\n",
        "\n",
        "        for j in range(len(batch)):\n",
        "          text = tokenizer.decode(batch[j][0], skip_special_tokens=True)\n",
        "          predicted_label = pred_flat[j]\n",
        "          actual_label = labels_flat[j]\n",
        "\n",
        "          if predicted_label != actual_label:\n",
        "            incorrect_predictions.append((text, predicted_label, actual_label))\n",
        "\n",
        "          new_row = {'sentence': text, 'predicted_label': predicted_label}\n",
        "          new_row_df = pd.DataFrame([new_row])\n",
        "\n",
        "          # Concatenate the DataFrames\n",
        "          pred_df = pd.concat([pred_df, new_row_df], ignore_index=True)\n",
        "\n",
        "    # Report the final accuracy for this validation run.\n",
        "    print(\"Num of correct predictions during Validation =\", total_correct)\n",
        "    val_accuracy = total_correct / len(val_set)\n",
        "    return val_accuracy, total_eval_loss, incorrect_predictions, pred_df"
      ],
      "metadata": {
        "id": "WUXqeKyWe1-f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training and Validating the model"
      ],
      "metadata": {
        "id": "XImFGkEC_1BE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "seed_everything()\n",
        "\n",
        "# training loop\n",
        "epoch_list = []\n",
        "train_accuracy_list = []\n",
        "train_loss_list = []\n",
        "val_accuracy_list = []\n",
        "val_loss_list = []\n",
        "\n",
        "# For each epoch...\n",
        "for epoch_i in range(0, epochs):\n",
        "    # Perform one full pass over the training set.\n",
        "\n",
        "    print(\"\")\n",
        "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
        "    print('Training...')\n",
        "\n",
        "    # Reset the total loss for this epoch.\n",
        "    total_train_loss = 0\n",
        "\n",
        "    # Array for logits\n",
        "    pred_arr = []\n",
        "    label_arr = []\n",
        "\n",
        "    # Put the model into training mode.\n",
        "    model.train()\n",
        "\n",
        "    # For each batch of training data...\n",
        "    num_batches = int(len(train_set)/batch_size) + 1\n",
        "\n",
        "    for i in range(num_batches):\n",
        "      end_index = min(batch_size * (i+1), len(train_set))\n",
        "\n",
        "      batch = train_set[i*batch_size:end_index]\n",
        "\n",
        "      if len(batch) == 0: continue\n",
        "\n",
        "      input_id_tensors = torch.stack([data[0] for data in batch])\n",
        "      input_mask_tensors = torch.stack([data[1] for data in batch])\n",
        "      label_tensors = torch.stack([data[2] for data in batch])\n",
        "\n",
        "      # Move tensors to the GPU\n",
        "      b_input_ids = input_id_tensors.to(device)\n",
        "      b_input_mask = input_mask_tensors.to(device)\n",
        "      b_labels = label_tensors.to(device)\n",
        "\n",
        "      # Perform a forward pass (evaluate the model on this training batch).\n",
        "      outputs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)\n",
        "      loss = outputs.loss\n",
        "      logits = outputs.logits\n",
        "\n",
        "      total_train_loss += loss.item()\n",
        "\n",
        "      # Clear the previously calculated gradient\n",
        "      model.zero_grad()\n",
        "\n",
        "      # Perform a backward pass to calculate the gradients.\n",
        "      loss.backward()\n",
        "\n",
        "      # Update parameters and take a step using the computed gradient.\n",
        "      optimizer.step()\n",
        "\n",
        "      # ========================================\n",
        "      #       Calculating Train accuracy\n",
        "      # ========================================\n",
        "      # Move logits and labels to CPU\n",
        "      logits = (logits).detach().cpu().numpy()\n",
        "      label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "      # Calculate the number of correctly labeled examples in batch\n",
        "      pred_flat = np.argmax(logits, axis=1).flatten()\n",
        "      labels_flat = np.argmax(label_ids, axis=1).flatten()\n",
        "\n",
        "      pred_arr = np.append(pred_arr, pred_flat)\n",
        "      label_arr = np.append(label_arr, labels_flat)\n",
        "\n",
        "    train_accuracy = np.mean(label_arr == pred_arr)\n",
        "    print(f\"Training Accuracy: {train_accuracy}\")\n",
        "    print(f\"Total Training loss: {total_train_loss}\")\n",
        "\n",
        "    # ========================================\n",
        "    #               Validation\n",
        "    # ========================================\n",
        "    # After the completion of each training epoch, measure our performance on\n",
        "    # our validation set. Implement this function in the cell above.\n",
        "    print(\"Validating...\")\n",
        "    val_accuracy, val_loss, incorrect_predictions, pred_df = get_validation_performance(val_set)\n",
        "    print(f\"Validation accuracy: {val_accuracy}\")\n",
        "    print(f\"Total Validation loss: {val_loss}\")\n",
        "\n",
        "    # Record the training\n",
        "    epoch_list.append(epoch_i)\n",
        "    train_accuracy_list.append(train_accuracy)\n",
        "    train_loss_list.append(total_train_loss)\n",
        "    val_accuracy_list.append(val_accuracy)\n",
        "    val_loss_list.append(val_loss)\n",
        "\n",
        "print(\"\")\n",
        "print(\"Training complete!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YM5DcOQxe9ZU",
        "outputId": "02ef3850-8025-45f1-f894-e9a9fecf1164"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======== Epoch 1 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.6101057906458798\n",
            "Total Training loss: 84.64196386066033\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 681\n",
            "Validation accuracy: 0.7583518930957683\n",
            "Total Validation loss: 7.9222828396206975\n",
            "\n",
            "======== Epoch 2 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.768652561247216\n",
            "Total Training loss: 59.46120228077888\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 697\n",
            "Validation accuracy: 0.7761692650334076\n",
            "Total Validation loss: 6.959744592372194\n",
            "\n",
            "======== Epoch 3 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.8062360801781737\n",
            "Total Training loss: 51.36167039329979\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 709\n",
            "Validation accuracy: 0.7895322939866369\n",
            "Total Validation loss: 6.67834877692946\n",
            "\n",
            "======== Epoch 4 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.8278118040089086\n",
            "Total Training loss: 45.489708988059625\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 707\n",
            "Validation accuracy: 0.7873051224944321\n",
            "Total Validation loss: 6.716390862330091\n",
            "\n",
            "======== Epoch 5 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.862054565701559\n",
            "Total Training loss: 38.91142282386738\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 695\n",
            "Validation accuracy: 0.7739420935412027\n",
            "Total Validation loss: 6.962953498215606\n",
            "\n",
            "======== Epoch 6 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.8854398663697105\n",
            "Total Training loss: 33.26558948811485\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 684\n",
            "Validation accuracy: 0.7616926503340757\n",
            "Total Validation loss: 7.982063388836977\n",
            "\n",
            "======== Epoch 7 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9050668151447662\n",
            "Total Training loss: 28.21233276873904\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 694\n",
            "Validation accuracy: 0.7728285077951003\n",
            "Total Validation loss: 7.730067127127871\n",
            "\n",
            "======== Epoch 8 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9228841870824054\n",
            "Total Training loss: 23.264000796995468\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 688\n",
            "Validation accuracy: 0.7661469933184856\n",
            "Total Validation loss: 8.487024143708888\n",
            "\n",
            "======== Epoch 9 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9376391982182628\n",
            "Total Training loss: 19.192487485812258\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 694\n",
            "Validation accuracy: 0.7728285077951003\n",
            "Total Validation loss: 9.32727426986405\n",
            "\n",
            "======== Epoch 10 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9451559020044543\n",
            "Total Training loss: 17.030318907595454\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 682\n",
            "Validation accuracy: 0.7594654788418709\n",
            "Total Validation loss: 9.912774088863094\n",
            "\n",
            "======== Epoch 11 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9516982182628062\n",
            "Total Training loss: 15.552025522517502\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 672\n",
            "Validation accuracy: 0.7483296213808464\n",
            "Total Validation loss: 9.961003747630043\n",
            "\n",
            "======== Epoch 12 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9562917594654788\n",
            "Total Training loss: 13.931988803584929\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 647\n",
            "Validation accuracy: 0.720489977728285\n",
            "Total Validation loss: 12.45035448558907\n",
            "\n",
            "======== Epoch 13 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9718819599109132\n",
            "Total Training loss: 9.86424883584863\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 683\n",
            "Validation accuracy: 0.7605790645879733\n",
            "Total Validation loss: 10.94579831638133\n",
            "\n",
            "======== Epoch 14 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9775890868596881\n",
            "Total Training loss: 7.849658036969325\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 687\n",
            "Validation accuracy: 0.765033407572383\n",
            "Total Validation loss: 12.049241336623815\n",
            "\n",
            "======== Epoch 15 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9860801781737194\n",
            "Total Training loss: 6.280408525963818\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 675\n",
            "Validation accuracy: 0.7516703786191536\n",
            "Total Validation loss: 13.114722811712682\n",
            "\n",
            "======== Epoch 16 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9871937639198218\n",
            "Total Training loss: 5.258267230197098\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 13.061304131098101\n",
            "\n",
            "======== Epoch 17 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9871937639198218\n",
            "Total Training loss: 4.90111239018761\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 695\n",
            "Validation accuracy: 0.7739420935412027\n",
            "Total Validation loss: 12.851771041238452\n",
            "\n",
            "======== Epoch 18 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9876113585746102\n",
            "Total Training loss: 4.721275460435158\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 698\n",
            "Validation accuracy: 0.77728285077951\n",
            "Total Validation loss: 12.795465515694953\n",
            "\n",
            "======== Epoch 19 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9895601336302895\n",
            "Total Training loss: 3.977302408900938\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 687\n",
            "Validation accuracy: 0.765033407572383\n",
            "Total Validation loss: 13.496113151497\n",
            "\n",
            "======== Epoch 20 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9919265033407573\n",
            "Total Training loss: 3.6127708998655463\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 673\n",
            "Validation accuracy: 0.7494432071269488\n",
            "Total Validation loss: 14.369227018808186\n",
            "\n",
            "======== Epoch 21 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9929008908685969\n",
            "Total Training loss: 3.3410472367286927\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 697\n",
            "Validation accuracy: 0.7761692650334076\n",
            "Total Validation loss: 13.72558591207153\n",
            "\n",
            "======== Epoch 22 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9938752783964365\n",
            "Total Training loss: 2.5688151943928563\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 697\n",
            "Validation accuracy: 0.7761692650334076\n",
            "Total Validation loss: 14.026524730575696\n",
            "\n",
            "======== Epoch 23 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9938752783964365\n",
            "Total Training loss: 2.5831373249293645\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 687\n",
            "Validation accuracy: 0.765033407572383\n",
            "Total Validation loss: 14.495944862970449\n",
            "\n",
            "======== Epoch 24 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9924832962138085\n",
            "Total Training loss: 2.836104866749674\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 697\n",
            "Validation accuracy: 0.7761692650334076\n",
            "Total Validation loss: 15.033807936631643\n",
            "\n",
            "======== Epoch 25 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9933184855233853\n",
            "Total Training loss: 2.5606744935423866\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 704\n",
            "Validation accuracy: 0.7839643652561247\n",
            "Total Validation loss: 14.670595372898978\n",
            "\n",
            "======== Epoch 26 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9951280623608018\n",
            "Total Training loss: 2.138987283004254\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 695\n",
            "Validation accuracy: 0.7739420935412027\n",
            "Total Validation loss: 14.915968397151449\n",
            "\n",
            "======== Epoch 27 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9948496659242761\n",
            "Total Training loss: 2.1222483143049264\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 696\n",
            "Validation accuracy: 0.7750556792873051\n",
            "Total Validation loss: 15.464016008135316\n",
            "\n",
            "======== Epoch 28 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9961024498886414\n",
            "Total Training loss: 1.6324273232168598\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 702\n",
            "Validation accuracy: 0.7817371937639198\n",
            "Total Validation loss: 15.1392581545183\n",
            "\n",
            "======== Epoch 29 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.995684855233853\n",
            "Total Training loss: 1.6924529935773815\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 705\n",
            "Validation accuracy: 0.7850779510022272\n",
            "Total Validation loss: 15.120751193496458\n",
            "\n",
            "======== Epoch 30 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9962416481069042\n",
            "Total Training loss: 1.4900051474132856\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 698\n",
            "Validation accuracy: 0.77728285077951\n",
            "Total Validation loss: 15.173894776147957\n",
            "\n",
            "======== Epoch 31 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9959632516703786\n",
            "Total Training loss: 1.4735009277542739\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 684\n",
            "Validation accuracy: 0.7616926503340757\n",
            "Total Validation loss: 15.387718058921418\n",
            "\n",
            "======== Epoch 32 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9954064587973274\n",
            "Total Training loss: 1.6457736143660313\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 690\n",
            "Validation accuracy: 0.7683741648106904\n",
            "Total Validation loss: 15.616007549300953\n",
            "\n",
            "======== Epoch 33 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9958240534521158\n",
            "Total Training loss: 1.527261114744124\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 672\n",
            "Validation accuracy: 0.7483296213808464\n",
            "Total Validation loss: 16.785355720282286\n",
            "\n",
            "======== Epoch 34 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9954064587973274\n",
            "Total Training loss: 1.6271714428887636\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 16.66260850668391\n",
            "\n",
            "======== Epoch 35 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9973552338530067\n",
            "Total Training loss: 1.0342801135336197\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 682\n",
            "Validation accuracy: 0.7594654788418709\n",
            "Total Validation loss: 16.980097215159102\n",
            "\n",
            "======== Epoch 36 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9966592427616926\n",
            "Total Training loss: 1.6501232125883598\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 650\n",
            "Validation accuracy: 0.7238307349665924\n",
            "Total Validation loss: 19.15511116247872\n",
            "\n",
            "======== Epoch 37 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9967984409799554\n",
            "Total Training loss: 1.191011551249959\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 681\n",
            "Validation accuracy: 0.7583518930957683\n",
            "Total Validation loss: 17.500974569053607\n",
            "\n",
            "======== Epoch 38 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9967984409799554\n",
            "Total Training loss: 0.9889015251234493\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 701\n",
            "Validation accuracy: 0.7806236080178174\n",
            "Total Validation loss: 17.301759521892116\n",
            "\n",
            "======== Epoch 39 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9961024498886414\n",
            "Total Training loss: 1.2989795800616397\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 664\n",
            "Validation accuracy: 0.7394209354120267\n",
            "Total Validation loss: 19.085932973934995\n",
            "\n",
            "======== Epoch 40 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9979120267260579\n",
            "Total Training loss: 1.0183257341887952\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 683\n",
            "Validation accuracy: 0.7605790645879733\n",
            "Total Validation loss: 17.97136254492703\n",
            "\n",
            "======== Epoch 41 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.996380846325167\n",
            "Total Training loss: 1.2825171396664723\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 665\n",
            "Validation accuracy: 0.7405345211581291\n",
            "Total Validation loss: 18.840872613367395\n",
            "\n",
            "======== Epoch 42 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9974944320712695\n",
            "Total Training loss: 0.8781427194625268\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 668\n",
            "Validation accuracy: 0.7438752783964365\n",
            "Total Validation loss: 19.59248430846933\n",
            "\n",
            "======== Epoch 43 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9967984409799554\n",
            "Total Training loss: 0.9913430550554085\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 679\n",
            "Validation accuracy: 0.7561247216035635\n",
            "Total Validation loss: 18.26039602647462\n",
            "\n",
            "======== Epoch 44 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9967984409799554\n",
            "Total Training loss: 1.1866925012895013\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 676\n",
            "Validation accuracy: 0.7527839643652561\n",
            "Total Validation loss: 18.719619734972056\n",
            "\n",
            "======== Epoch 45 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9969376391982183\n",
            "Total Training loss: 1.1354616812081901\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 680\n",
            "Validation accuracy: 0.7572383073496659\n",
            "Total Validation loss: 18.44716085017936\n",
            "\n",
            "======== Epoch 46 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9990256124721604\n",
            "Total Training loss: 0.4875349490162967\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 676\n",
            "Validation accuracy: 0.7527839643652561\n",
            "Total Validation loss: 19.608139899249643\n",
            "\n",
            "======== Epoch 47 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9980512249443207\n",
            "Total Training loss: 1.0084579242417437\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 18.615297381882055\n",
            "\n",
            "======== Epoch 48 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9969376391982183\n",
            "Total Training loss: 1.0411570491296234\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 676\n",
            "Validation accuracy: 0.7527839643652561\n",
            "Total Validation loss: 19.144842053102\n",
            "\n",
            "======== Epoch 49 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9969376391982183\n",
            "Total Training loss: 1.0349612488331892\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 676\n",
            "Validation accuracy: 0.7527839643652561\n",
            "Total Validation loss: 19.026508772670006\n",
            "\n",
            "======== Epoch 50 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9962416481069042\n",
            "Total Training loss: 1.1592646018856767\n",
            "Validating...\n",
            "Num of correct predictions during Validation = 671\n",
            "Validation accuracy: 0.7472160356347439\n",
            "Total Validation loss: 18.502548096031447\n",
            "\n",
            "Training complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot the train and loss plots per epoch"
      ],
      "metadata": {
        "id": "gY_-IjsMDj2Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(16, 6))\n",
        "\n",
        "# Accuracy Plot\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epoch_list, train_accuracy_list, label=\"Train Accuracy\")\n",
        "plt.plot(epoch_list, val_accuracy_list, label=\"Validation Accuracy\")\n",
        "plt.title(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "\n",
        "# Loss Plot\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epoch_list, train_loss_list, label=\"Train Loss\")\n",
        "plt.plot(epoch_list, val_loss_list, label=\"Validation Loss\")\n",
        "plt.title(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "KKPgf52qDolI",
        "outputId": "ac9ea8d9-f447-444b-f5fc-3597e1e85e2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the model"
      ],
      "metadata": {
        "id": "_zhPz94EgVVc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "torch.save(model.state_dict(), \"Bert.pth\")\n",
        "files.download('Bert.pth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "XvMXUn8lgZjo",
        "outputId": "cb63e72b-1471-48ed-f5d0-89619c84c745"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_0be207f5-ef96-4254-99e4-b0b196b9720f\", \"Bert.pth\", 438006393)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating the model\n",
        "\n",
        "After tuning the hyperparameters we evaluate our model on the test set"
      ],
      "metadata": {
        "id": "bZ0QVBWkgla0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "# If notebook disconnects during training, then here, first load the best model we saved , then check validation performance\n",
        "\n",
        "test_accuracy, test_loss, incorrect_predictions, pred_df = get_validation_performance(test_set)\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgorHEkMg0nh",
        "outputId": "10844020-9bdc-401c-e99e-ff7a6bb8a5d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of correct predictions during Validation = 674\n",
            "0.7497219132369299\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Error Analysis\n",
        "\n",
        "We perform an *error analysis* on the model. We print out the text of up to five test set examples that our model gets **wrong**. If our model gets more than five test examples wrong, we randomly choose five of them to analyze. If our model gets fewer than five examples wrong, we design five test examples that fool our model (i.e., *adversarial examples*). We then perform a qualitative analysis of these examples to figure out any reasons for errors that we observe, or if we have any informed guesses (e.g., common linguistic properties of these particular examples). We identify any possible future steps to improve the classifier."
      ],
      "metadata": {
        "id": "43Jzqut0hAwC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "\n",
        "seed_everything()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "## print out up to 5 test set examples (or adversarial examples) that model gets wrong\n",
        "print(\"Number of incorrect predictions:\", len(incorrect_predictions))\n",
        "random.shuffle(incorrect_predictions)\n",
        "\n",
        "if len(incorrect_predictions) == 0:\n",
        "    print(\"All predictions are correct!\")\n",
        "else:\n",
        "    print(\"Below are 5 random incorrect predictions:\")\n",
        "    num_to_print = min(5, len(incorrect_predictions))\n",
        "    random_indices = np.random.choice(len(incorrect_predictions), size=num_to_print, replace=False)\n",
        "    for i in random_indices:\n",
        "        text = incorrect_predictions[i][0]\n",
        "        print(f'Text: {text}')\n",
        "        print(\"Predicted label:\", incorrect_predictions[i][1])\n",
        "        print(\"Correct label:\", incorrect_predictions[i][2])\n",
        "        print(\"------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZJH3mQphkRI",
        "outputId": "8409d79f-2ed5-4f3c-c87f-dc45a7986e9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of incorrect predictions: 207\n",
            "Below are 5 random incorrect predictions:\n",
            "Text: @ hovaa _ ok wat ever u say whitey. u prolly never even seen shaft.\n",
            "Predicted label: 1\n",
            "Correct label: 2\n",
            "------------------------------\n",
            "Text: rt @ alexishope _ 2014 : you got niggas & amp ; i got bitches but i want you... & # 128149 ; & # 127926 ;\n",
            "Predicted label: 0\n",
            "Correct label: 2\n",
            "------------------------------\n",
            "Text: mike pence out here following the girls? the faggot jumped out!!\n",
            "Predicted label: 2\n",
            "Correct label: 0\n",
            "------------------------------\n",
            "Text: whenever i hit 1000 followers i feel so self - destructive. like a nigger near a cop car.\n",
            "Predicted label: 2\n",
            "Correct label: 1\n",
            "------------------------------\n",
            "Text: @ user you can \\ u2019t honestly be such an asshole can you? the shithole countries of the past were italy and \\ u2026 @ url\n",
            "Predicted label: 0\n",
            "Correct label: 2\n",
            "------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NoahBERT"
      ],
      "metadata": {
        "id": "P_in4IBzkquG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load BERT and modify"
      ],
      "metadata": {
        "id": "Yv3opR-9nW2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from transformers import BertModel, BertTokenizer, BertConfig\n",
        "from transformers.modeling_outputs import TokenClassifierOutput\n",
        "from transformers import AdamW, BertConfig\n",
        "\n",
        "# Load pretrained BERT model and tokenizer\n",
        "checkpoint = 'bert-base-uncased'\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "class BERTWithNoahClassifier(nn.Module):\n",
        "    \"\"\"\n",
        "    Our custom Noah transformer model. This model loads a pre-trained BERT transformer model and adds a new feed-forward, dropout\n",
        "    and linear layer at the end for fine-tuning and prediction on specific tasks.\n",
        "    \"\"\"\n",
        "    def __init__(self, checkpoint, num_labels ):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            checkpoint (str): The name of the pre-trained model or path to the model weights.\n",
        "            num_labels (int): The number of output labels in the final classification layer.\n",
        "        \"\"\"\n",
        "        super(BERTWithNoahClassifier, self).__init__()\n",
        "        self.num_labels = num_labels\n",
        "\n",
        "        self.model = model = BertModel.from_pretrained(checkpoint, config = BertConfig.from_pretrained(checkpoint,\n",
        "                                                                                                       output_attention = True,\n",
        "                                                                                                       output_hidden_state = True ) )\n",
        "        #\n",
        "        # New Layer\n",
        "        self.feedforward = nn.Sequential(\n",
        "            nn.Linear(768, 256),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "        self.dropout = nn.Dropout(0.1)\n",
        "        self.classifier = nn.Linear(256, num_labels )\n",
        "\n",
        "    def forward(self, input_ids = None, attention_mask=None, labels = None ):\n",
        "        \"\"\"\n",
        "        Forward pass for the model.\n",
        "\n",
        "        Args:\n",
        "            input_ids (torch.Tensor, optional): Tensor of input IDs. Defaults to None.\n",
        "            attention_mask (torch.Tensor, optional): Tensor for attention masks. Defaults to None.\n",
        "            labels (torch.Tensor, optional): Tensor for labels. Defaults to None.\n",
        "\n",
        "        Returns:\n",
        "            TokenClassifierOutput: A named tuple with the following fields:\n",
        "            - loss (torch.FloatTensor of shape (1,), optional, returned when label_ids is provided) – Classification loss.\n",
        "            - logits (torch.FloatTensor of shape (batch_size, num_labels)) – Classification scores before SoftMax.\n",
        "            - hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).\n",
        "            - attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).\n",
        "        \"\"\"\n",
        "        outputs = self.model(input_ids = input_ids, attention_mask = attention_mask)\n",
        "\n",
        "        # Extract the last hidden state\n",
        "        last_hidden_state = outputs.last_hidden_state\n",
        "\n",
        "        # Apply the feedforward layer\n",
        "        features = self.feedforward(last_hidden_state[:, 0, :])\n",
        "\n",
        "        # Apply dropout\n",
        "        features = self.dropout(features)\n",
        "\n",
        "        # Apply the classifier layer\n",
        "        logits = self.classifier(features)\n",
        "\n",
        "        if labels is not None:\n",
        "            # Compute the loss if labels are provided\n",
        "            loss_fn = nn.CrossEntropyLoss()\n",
        "            loss = loss_fn(logits, labels)\n",
        "            return logits, loss\n",
        "        else:\n",
        "            # Return the logits if no labels are provided\n",
        "            return logits\n",
        "\n",
        "# Initialize Noah classifier and combine with BERT model\n",
        "# noah_classifier = BERTWithNoahClassifier(input_size=768, hidden_size=256, output_size=3)\n",
        "model = BERTWithNoahClassifier(checkpoint=checkpoint, num_labels=3)\n",
        "\n",
        "# Tell pytorch to run this model on the GPU.\n",
        "model.cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TwnYbH21CDt",
        "outputId": "f168a59c-1a98-4a9c-e607-5074da82f83f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BERTWithNoahClassifier(\n",
              "  (model): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (feedforward): Sequential(\n",
              "    (0): Linear(in_features=768, out_features=256, bias=True)\n",
              "    (1): ReLU()\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=256, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining Hyperparameters"
      ],
      "metadata": {
        "id": "MV0RCJwFqxoz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 45\n",
        "optimizer = AdamW(model.parameters(),lr=0.00001)\n",
        "epochs = 3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jhx3yHjXq2by",
        "outputId": "69d4a647-a5f2-4eef-df03-52a3873342e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetuning NoahBERT"
      ],
      "metadata": {
        "id": "yxd2nvfVq4q8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Defining function to print validation accuracy"
      ],
      "metadata": {
        "id": "NJjWV8B8pqft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer\n",
        "import pandas as pd\n",
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "seed_everything()\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# function to get validation accuracy\n",
        "def get_validation_performance(val_set, model, batch_size, device):\n",
        "    # Put the model in evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    # Tracking variables\n",
        "    total_eval_accuracy = 0\n",
        "    total_eval_loss = 0\n",
        "\n",
        "    num_batches = int(len(val_set) / batch_size) + 1\n",
        "\n",
        "    incorrect_predictions = []\n",
        "    total_correct = 0\n",
        "    pred_df = pd.DataFrame(columns=['sentence', 'predicted_label'])\n",
        "\n",
        "    for i in range(num_batches):\n",
        "        end_index = min(batch_size * (i + 1), len(val_set))\n",
        "\n",
        "        batch = val_set[i * batch_size:end_index]\n",
        "\n",
        "        if len(batch) == 0:\n",
        "            continue\n",
        "\n",
        "        input_id_tensors = torch.stack([data[0] for data in batch])\n",
        "        input_mask_tensors = torch.stack([data[1] for data in batch])\n",
        "        label_tensors = torch.stack([data[2] for data in batch])\n",
        "\n",
        "\n",
        "        # Move tensors to the GPU\n",
        "        b_input_ids = input_id_tensors.to(device)\n",
        "        b_input_mask = input_mask_tensors.to(device)\n",
        "        b_labels = label_tensors.to(device)\n",
        "\n",
        "        # Tell pytorch not to bother with constructing the compute graph during\n",
        "        # the forward pass, since this is only needed for backprop (training).\n",
        "        with torch.no_grad():\n",
        "            # Forward pass, calculate logit predictions.\n",
        "            outputs = model(b_input_ids,\n",
        "                            attention_mask=b_input_mask,\n",
        "                            labels=b_labels)\n",
        "            loss = outputs[1]\n",
        "            logits = outputs[0]\n",
        "\n",
        "            # Accumulate the validation loss.\n",
        "            total_eval_loss += loss.item()\n",
        "\n",
        "            # Move logits and labels to CPU\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "            label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "            # Calculate the number of correctly labeled examples in batch\n",
        "            pred_flat = np.argmax(logits, axis=1).flatten()\n",
        "            labels_flat = np.argmax(label_ids, axis=1).flatten()\n",
        "\n",
        "            num_correct = np.sum(pred_flat == labels_flat)\n",
        "            total_correct += num_correct\n",
        "\n",
        "            for j in range(len(batch)):\n",
        "                text = tokenizer.decode(batch[j][0], skip_special_tokens=True)\n",
        "                predicted_label = pred_flat[j]\n",
        "\n",
        "                if predicted_label != labels_flat[j]:\n",
        "                    incorrect_predictions.append((text, predicted_label, labels_flat[j]))\n",
        "\n",
        "                new_row = {'sentence': text, 'predicted_label': predicted_label}\n",
        "                new_row_df = pd.DataFrame([new_row])\n",
        "\n",
        "                # Concatenate the DataFrames\n",
        "                pred_df = pd.concat([pred_df, new_row_df], ignore_index=True)\n",
        "    # Report the final accuracy for this validation run.\n",
        "    print(\"Num of correct predictions =\", total_correct)\n",
        "    avg_val_accuracy = total_correct / len(val_set)\n",
        "    return avg_val_accuracy, total_eval_loss, incorrect_predictions, pred_df\n"
      ],
      "metadata": {
        "id": "Lv47zgBAq7gt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Training and Validating the model"
      ],
      "metadata": {
        "id": "JmE_BUBw4b1k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "seed_everything()\n",
        "\n",
        "# training loop\n",
        "epoch_list = []\n",
        "train_accuracy_list = []\n",
        "train_loss_list = []\n",
        "val_accuracy_list = []\n",
        "val_loss_list = []\n",
        "\n",
        "# For each epoch...\n",
        "for epoch_i in range(0, epochs):\n",
        "    # Perform one full pass over the training set.\n",
        "\n",
        "    print(\"\")\n",
        "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
        "    print('Training...')\n",
        "\n",
        "    # Reset the total loss for this epoch.\n",
        "    total_train_loss = 0\n",
        "\n",
        "    # Array for logits\n",
        "    pred_arr = []\n",
        "    label_arr = []\n",
        "\n",
        "    # Put the model into training mode.\n",
        "    model.train()\n",
        "\n",
        "    # For each batch of training data...\n",
        "    num_batches = int(len(train_set) / batch_size) + 1\n",
        "\n",
        "    for i in range(num_batches):\n",
        "        end_index = min(batch_size * (i + 1), len(train_set))\n",
        "\n",
        "        batch = train_set[i * batch_size:end_index]\n",
        "\n",
        "        if len(batch) == 0:\n",
        "            continue\n",
        "\n",
        "        input_id_tensors = torch.stack([data[0] for data in batch])\n",
        "        input_mask_tensors = torch.stack([data[1] for data in batch])\n",
        "        label_tensors = torch.stack([data[2] for data in batch])\n",
        "\n",
        "        # Move tensors to the GPU\n",
        "        b_input_ids = input_id_tensors.to(device)\n",
        "        b_input_mask = input_mask_tensors.to(device)\n",
        "        b_labels = label_tensors.to(device)\n",
        "\n",
        "        # Perform a forward pass (evaluate the model on this training batch).\n",
        "        outputs = model(b_input_ids, attention_mask=b_input_mask, labels=b_labels)\n",
        "        logits = outputs[0]\n",
        "        loss = outputs[1]\n",
        "\n",
        "        total_train_loss += loss.item()\n",
        "\n",
        "        # Clear the previously calculated gradient\n",
        "        model.zero_grad()\n",
        "\n",
        "        # Perform a backward pass to calculate the gradients.\n",
        "        loss.backward()\n",
        "\n",
        "        # Update parameters and take a step using the computed gradient.\n",
        "        optimizer.step()\n",
        "\n",
        "       # ========================================\n",
        "       #       Calculating Train accuracy\n",
        "       # ========================================\n",
        "       # Move logits and labels to CPU\n",
        "        logits = (logits).detach().cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "       # Calculate the number of correctly labeled examples in batch\n",
        "        pred_flat = np.argmax(logits, axis=1).flatten()\n",
        "        labels_flat = np.argmax(label_ids, axis=1).flatten()\n",
        "\n",
        "        pred_arr = np.append(pred_arr, pred_flat)\n",
        "        label_arr = np.append(label_arr, labels_flat)\n",
        "\n",
        "    train_accuracy = np.mean(label_arr == pred_arr)\n",
        "    print(f\"Training Accuracy: {train_accuracy}\")\n",
        "    print(f\"Total Training loss: {total_train_loss}\")\n",
        "\n",
        "\n",
        "    # ========================================\n",
        "    #               Validation\n",
        "    # ========================================\n",
        "    # After the completion of each training epoch, measure our performance on\n",
        "    # our validation set. Implement this function in the cell above.\n",
        "    print(f\"Total loss: {total_train_loss}\")\n",
        "    val_acc, val_loss, incorrect_predictions, pred_df = get_validation_performance(val_set, model, batch_size, device)\n",
        "    print(f\"Validation accuracy: {val_acc}\")\n",
        "    print(f\"Total Validation loss: {val_loss}\")\n",
        "\n",
        "    # Record the training\n",
        "    epoch_list.append(epoch_i)\n",
        "    train_accuracy_list.append(train_accuracy)\n",
        "    train_loss_list.append(total_train_loss)\n",
        "    val_accuracy_list.append(val_acc)\n",
        "    val_loss_list.append(val_loss)\n",
        "\n",
        "print(\"\")\n",
        "print(\"Noah Training complete!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMAsf_h4q_uv",
        "outputId": "861d2ea4-3aa5-4989-e8a6-11597c3d3bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======== Epoch 1 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.6378062360801782\n",
            "Total Training loss: 132.74744189669923\n",
            "Total loss: 132.74744189669923\n",
            "Num of correct predictions = 679\n",
            "Validation accuracy: 0.7561247216035635\n",
            "Total Validation loss: 12.249198343102321\n",
            "\n",
            "======== Epoch 2 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.7742204899777283\n",
            "Total Training loss: 94.27722238740701\n",
            "Total loss: 94.27722238740701\n",
            "Num of correct predictions = 701\n",
            "Validation accuracy: 0.7806236080178174\n",
            "Total Validation loss: 11.286748596613876\n",
            "\n",
            "======== Epoch 3 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.8035913140311804\n",
            "Total Training loss: 82.46431384956551\n",
            "Total loss: 82.46431384956551\n",
            "Num of correct predictions = 703\n",
            "Validation accuracy: 0.7828507795100222\n",
            "Total Validation loss: 11.068852847262987\n",
            "\n",
            "======== Epoch 4 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.8335189309576837\n",
            "Total Training loss: 72.32299621028402\n",
            "Total loss: 72.32299621028402\n",
            "Num of correct predictions = 701\n",
            "Validation accuracy: 0.7806236080178174\n",
            "Total Validation loss: 11.375645979500115\n",
            "\n",
            "======== Epoch 5 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.862054565701559\n",
            "Total Training loss: 60.755642411875705\n",
            "Total loss: 60.755642411875705\n",
            "Num of correct predictions = 694\n",
            "Validation accuracy: 0.7728285077951003\n",
            "Total Validation loss: 12.526025131306053\n",
            "\n",
            "======== Epoch 6 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.8883630289532294\n",
            "Total Training loss: 51.93970236817725\n",
            "Total loss: 51.93970236817725\n",
            "Num of correct predictions = 671\n",
            "Validation accuracy: 0.7472160356347439\n",
            "Total Validation loss: 14.542020351746787\n",
            "\n",
            "======== Epoch 7 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9088251670378619\n",
            "Total Training loss: 42.558707703080145\n",
            "Total loss: 42.558707703080145\n",
            "Num of correct predictions = 669\n",
            "Validation accuracy: 0.744988864142539\n",
            "Total Validation loss: 15.730941404786577\n",
            "\n",
            "======== Epoch 8 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9235801781737194\n",
            "Total Training loss: 35.53019131516119\n",
            "Total loss: 35.53019131516119\n",
            "Num of correct predictions = 689\n",
            "Validation accuracy: 0.767260579064588\n",
            "Total Validation loss: 15.042211512469349\n",
            "\n",
            "======== Epoch 9 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9329064587973274\n",
            "Total Training loss: 31.596240236646743\n",
            "Total loss: 31.596240236646743\n",
            "Num of correct predictions = 674\n",
            "Validation accuracy: 0.7505567928730512\n",
            "Total Validation loss: 16.19962173030009\n",
            "\n",
            "======== Epoch 10 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9476614699331849\n",
            "Total Training loss: 24.742239984993795\n",
            "Total loss: 24.742239984993795\n",
            "Num of correct predictions = 672\n",
            "Validation accuracy: 0.7483296213808464\n",
            "Total Validation loss: 17.87158774217562\n",
            "\n",
            "======== Epoch 11 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9501670378619154\n",
            "Total Training loss: 23.191766827883846\n",
            "Total loss: 23.191766827883846\n",
            "Num of correct predictions = 648\n",
            "Validation accuracy: 0.7216035634743875\n",
            "Total Validation loss: 19.758224290793038\n",
            "\n",
            "======== Epoch 12 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9521158129175946\n",
            "Total Training loss: 21.351795135178662\n",
            "Total loss: 21.351795135178662\n",
            "Num of correct predictions = 662\n",
            "Validation accuracy: 0.7371937639198218\n",
            "Total Validation loss: 17.969769192445305\n",
            "\n",
            "======== Epoch 13 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9699331848552338\n",
            "Total Training loss: 14.772850295528189\n",
            "Total loss: 14.772850295528189\n",
            "Num of correct predictions = 667\n",
            "Validation accuracy: 0.7427616926503341\n",
            "Total Validation loss: 19.744232549664\n",
            "\n",
            "======== Epoch 14 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9728563474387528\n",
            "Total Training loss: 12.254644334279055\n",
            "Total loss: 12.254644334279055\n",
            "Num of correct predictions = 658\n",
            "Validation accuracy: 0.732739420935412\n",
            "Total Validation loss: 20.399443958749398\n",
            "\n",
            "======== Epoch 15 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9819042316258352\n",
            "Total Training loss: 9.276927187439576\n",
            "Total loss: 9.276927187439576\n",
            "Num of correct predictions = 659\n",
            "Validation accuracy: 0.7338530066815144\n",
            "Total Validation loss: 21.42053510069613\n",
            "\n",
            "======== Epoch 16 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.981347438752784\n",
            "Total Training loss: 8.953913739299438\n",
            "Total loss: 8.953913739299438\n",
            "Num of correct predictions = 654\n",
            "Validation accuracy: 0.7282850779510023\n",
            "Total Validation loss: 21.842503095132866\n",
            "\n",
            "======== Epoch 17 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.987750556792873\n",
            "Total Training loss: 6.273048249839791\n",
            "Total loss: 6.273048249839791\n",
            "Num of correct predictions = 647\n",
            "Validation accuracy: 0.720489977728285\n",
            "Total Validation loss: 24.408829890883652\n",
            "\n",
            "======== Epoch 18 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.987054565701559\n",
            "Total Training loss: 6.523755337263081\n",
            "Total loss: 6.523755337263081\n",
            "Num of correct predictions = 669\n",
            "Validation accuracy: 0.744988864142539\n",
            "Total Validation loss: 24.95998995925432\n",
            "\n",
            "======== Epoch 19 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.987750556792873\n",
            "Total Training loss: 5.970249637429713\n",
            "Total loss: 5.970249637429713\n",
            "Num of correct predictions = 688\n",
            "Validation accuracy: 0.7661469933184856\n",
            "Total Validation loss: 23.43099000640039\n",
            "\n",
            "======== Epoch 20 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9910913140311804\n",
            "Total Training loss: 4.340113287678204\n",
            "Total loss: 4.340113287678204\n",
            "Num of correct predictions = 684\n",
            "Validation accuracy: 0.7616926503340757\n",
            "Total Validation loss: 24.556738696281563\n",
            "\n",
            "======== Epoch 21 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9941536748329621\n",
            "Total Training loss: 3.402899610362886\n",
            "Total loss: 3.402899610362886\n",
            "Num of correct predictions = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 24.885921656605447\n",
            "\n",
            "======== Epoch 22 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9933184855233853\n",
            "Total Training loss: 3.4982210908249254\n",
            "Total loss: 3.4982210908249254\n",
            "Num of correct predictions = 695\n",
            "Validation accuracy: 0.7739420935412027\n",
            "Total Validation loss: 25.954678103799786\n",
            "\n",
            "======== Epoch 23 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9947104677060133\n",
            "Total Training loss: 2.678175236107478\n",
            "Total loss: 2.678175236107478\n",
            "Num of correct predictions = 680\n",
            "Validation accuracy: 0.7572383073496659\n",
            "Total Validation loss: 26.723212953180653\n",
            "\n",
            "======== Epoch 24 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9948496659242761\n",
            "Total Training loss: 2.817834402338573\n",
            "Total loss: 2.817834402338573\n",
            "Num of correct predictions = 694\n",
            "Validation accuracy: 0.7728285077951003\n",
            "Total Validation loss: 26.90920515778861\n",
            "\n",
            "======== Epoch 25 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9955456570155902\n",
            "Total Training loss: 2.362833514624348\n",
            "Total loss: 2.362833514624348\n",
            "Num of correct predictions = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 26.83515198164741\n",
            "\n",
            "======== Epoch 26 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9958240534521158\n",
            "Total Training loss: 2.1273639689873693\n",
            "Total loss: 2.1273639689873693\n",
            "Num of correct predictions = 671\n",
            "Validation accuracy: 0.7472160356347439\n",
            "Total Validation loss: 26.74874857282494\n",
            "\n",
            "======== Epoch 27 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9924832962138085\n",
            "Total Training loss: 3.615608992976006\n",
            "Total loss: 3.615608992976006\n",
            "Num of correct predictions = 697\n",
            "Validation accuracy: 0.7761692650334076\n",
            "Total Validation loss: 25.908033301704847\n",
            "\n",
            "======== Epoch 28 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9942928730512249\n",
            "Total Training loss: 2.9170266817596846\n",
            "Total loss: 2.9170266817596846\n",
            "Num of correct predictions = 688\n",
            "Validation accuracy: 0.7661469933184856\n",
            "Total Validation loss: 27.157954604609603\n",
            "\n",
            "======== Epoch 29 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9966592427616926\n",
            "Total Training loss: 1.6543000933475471\n",
            "Total loss: 1.6543000933475471\n",
            "Num of correct predictions = 681\n",
            "Validation accuracy: 0.7583518930957683\n",
            "Total Validation loss: 27.27472690880714\n",
            "\n",
            "======== Epoch 30 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9965200445434298\n",
            "Total Training loss: 1.7950200413690502\n",
            "Total loss: 1.7950200413690502\n",
            "Num of correct predictions = 692\n",
            "Validation accuracy: 0.7706013363028953\n",
            "Total Validation loss: 28.032756692132825\n",
            "\n",
            "======== Epoch 31 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9959632516703786\n",
            "Total Training loss: 2.2701081594294816\n",
            "Total loss: 2.2701081594294816\n",
            "Num of correct predictions = 669\n",
            "Validation accuracy: 0.744988864142539\n",
            "Total Validation loss: 28.941793372971798\n",
            "\n",
            "======== Epoch 32 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9966592427616926\n",
            "Total Training loss: 1.8338336858357298\n",
            "Total loss: 1.8338336858357298\n",
            "Num of correct predictions = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 28.59772192524691\n",
            "\n",
            "======== Epoch 33 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9952672605790646\n",
            "Total Training loss: 2.017153274297312\n",
            "Total loss: 2.017153274297312\n",
            "Num of correct predictions = 672\n",
            "Validation accuracy: 0.7483296213808464\n",
            "Total Validation loss: 30.001040847341773\n",
            "\n",
            "======== Epoch 34 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9958240534521158\n",
            "Total Training loss: 1.709530710345852\n",
            "Total loss: 1.709530710345852\n",
            "Num of correct predictions = 672\n",
            "Validation accuracy: 0.7483296213808464\n",
            "Total Validation loss: 29.31036497345907\n",
            "\n",
            "======== Epoch 35 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9958240534521158\n",
            "Total Training loss: 2.697147780167991\n",
            "Total loss: 2.697147780167991\n",
            "Num of correct predictions = 691\n",
            "Validation accuracy: 0.7694877505567929\n",
            "Total Validation loss: 27.62836326132632\n",
            "\n",
            "======== Epoch 36 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9951280623608018\n",
            "Total Training loss: 2.1442127557465986\n",
            "Total loss: 2.1442127557465986\n",
            "Num of correct predictions = 690\n",
            "Validation accuracy: 0.7683741648106904\n",
            "Total Validation loss: 28.86429020509348\n",
            "\n",
            "======== Epoch 37 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9961024498886414\n",
            "Total Training loss: 1.7981586756866663\n",
            "Total loss: 1.7981586756866663\n",
            "Num of correct predictions = 665\n",
            "Validation accuracy: 0.7405345211581291\n",
            "Total Validation loss: 31.739701960753855\n",
            "\n",
            "======== Epoch 38 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9958240534521158\n",
            "Total Training loss: 1.8554587806929328\n",
            "Total loss: 1.8554587806929328\n",
            "Num of correct predictions = 672\n",
            "Validation accuracy: 0.7483296213808464\n",
            "Total Validation loss: 30.532745387619563\n",
            "\n",
            "======== Epoch 39 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9976336302895323\n",
            "Total Training loss: 1.6676681959165893\n",
            "Total loss: 1.6676681959165893\n",
            "Num of correct predictions = 689\n",
            "Validation accuracy: 0.767260579064588\n",
            "Total Validation loss: 30.06148273056974\n",
            "\n",
            "======== Epoch 40 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9962416481069042\n",
            "Total Training loss: 1.6080311128980551\n",
            "Total loss: 1.6080311128980551\n",
            "Num of correct predictions = 681\n",
            "Validation accuracy: 0.7583518930957683\n",
            "Total Validation loss: 31.385519919814318\n",
            "\n",
            "======== Epoch 41 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9954064587973274\n",
            "Total Training loss: 2.3487519512307884\n",
            "Total loss: 2.3487519512307884\n",
            "Num of correct predictions = 693\n",
            "Validation accuracy: 0.7717149220489977\n",
            "Total Validation loss: 28.711328606046532\n",
            "\n",
            "======== Epoch 42 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9965200445434298\n",
            "Total Training loss: 1.8660135610508655\n",
            "Total loss: 1.8660135610508655\n",
            "Num of correct predictions = 685\n",
            "Validation accuracy: 0.7628062360801782\n",
            "Total Validation loss: 29.507733983889274\n",
            "\n",
            "======== Epoch 43 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9979120267260579\n",
            "Total Training loss: 1.0285626269475967\n",
            "Total loss: 1.0285626269475967\n",
            "Num of correct predictions = 690\n",
            "Validation accuracy: 0.7683741648106904\n",
            "Total Validation loss: 31.54948427622422\n",
            "\n",
            "======== Epoch 44 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9974944320712695\n",
            "Total Training loss: 1.3024593405627665\n",
            "Total loss: 1.3024593405627665\n",
            "Num of correct predictions = 682\n",
            "Validation accuracy: 0.7594654788418709\n",
            "Total Validation loss: 30.76113687144629\n",
            "\n",
            "======== Epoch 45 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9969376391982183\n",
            "Total Training loss: 1.422020660137945\n",
            "Total loss: 1.422020660137945\n",
            "Num of correct predictions = 695\n",
            "Validation accuracy: 0.7739420935412027\n",
            "Total Validation loss: 29.97266403185053\n",
            "\n",
            "======== Epoch 46 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9980512249443207\n",
            "Total Training loss: 0.841618562028731\n",
            "Total loss: 0.841618562028731\n",
            "Num of correct predictions = 679\n",
            "Validation accuracy: 0.7561247216035635\n",
            "Total Validation loss: 31.20116645119557\n",
            "\n",
            "======== Epoch 47 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9981904231625836\n",
            "Total Training loss: 0.8470930591408985\n",
            "Total loss: 0.8470930591408985\n",
            "Num of correct predictions = 667\n",
            "Validation accuracy: 0.7427616926503341\n",
            "Total Validation loss: 33.7217340485048\n",
            "\n",
            "======== Epoch 48 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.995684855233853\n",
            "Total Training loss: 2.302412309356727\n",
            "Total loss: 2.302412309356727\n",
            "Num of correct predictions = 671\n",
            "Validation accuracy: 0.7472160356347439\n",
            "Total Validation loss: 31.048856443694486\n",
            "\n",
            "======== Epoch 49 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9979120267260579\n",
            "Total Training loss: 1.2035242181203913\n",
            "Total loss: 1.2035242181203913\n",
            "Num of correct predictions = 674\n",
            "Validation accuracy: 0.7505567928730512\n",
            "Total Validation loss: 31.93345387748046\n",
            "\n",
            "======== Epoch 50 / 50 ========\n",
            "Training...\n",
            "Training Accuracy: 0.9981904231625836\n",
            "Total Training loss: 1.1004824076141324\n",
            "Total loss: 1.1004824076141324\n",
            "Num of correct predictions = 652\n",
            "Validation accuracy: 0.7260579064587973\n",
            "Total Validation loss: 34.37325478305014\n",
            "\n",
            "Noah Training complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Plot the train and loss plots per epoch"
      ],
      "metadata": {
        "id": "eN2ToRAl4-dn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(16, 6))\n",
        "\n",
        "# Accuracy Plot\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epoch_list, train_accuracy_list, label=\"Train Accuracy\")\n",
        "plt.plot(epoch_list, val_accuracy_list, label=\"Validation Accuracy\")\n",
        "plt.title(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "\n",
        "# Loss Plot\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epoch_list, train_loss_list, label=\"Train Loss\")\n",
        "plt.plot(epoch_list, val_loss_list, label=\"Validation Loss\")\n",
        "plt.title(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_lChVijY5Ars",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "f83a5c0b-8b06-4daf-f336-3393c89a4c63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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m1X0dnRytI6rIx+3vfzcJaZnadixFW4+d1rZjKdp2PFlpmbn/jCp/Txc1r+4rfw8Xx/eei8Us5/w/XZzy/pvg4mSWs8Vc6HvUxWJWl/qBshhUBLvUz8+VWVn4unR89SfFJJ/VwkeuU+uaVQzJAAAAyg/Wg1DSLvU9dqU/PxvaqbFp06ZCcxIKtoC69957NWfOHMXGxhaaOF+rVi0tXbpUTz75pN555x1Vr15dH374oaOgIUkDBw5UYmKixo0bp7i4OLVo0ULLly+/4oIGUFxSUlK0Y8cOzZs3r9wVNIDSYLfbdSY7b+E9NbPwontGVm7+tip5tyyrTTm59ktuv2K3S20j/BXVJERNq/mU+hYs6Vm52hqdrM1HT2tLdN7tQouWJc1syiuGXN2icdoFj/q4OTk6B/IW2P8ueHi4WpSemauMLKvSsnKUkWVVelaO0rOs+cdzlZ6Vq7RzPj6bc3UL/B4uFvm6O1/05lPwsYezvF3/XujPyrXqZHq2EtOyCt1Opud/nP73sYtlM5kkZ4tZrucuIp+zsOxkMen46bNKyrjwUDlni0l1Ar3UIMRb9YO91SDYWw1CvOXl6qRjp//ueji3A+JEcqaycm06kJCuAwnpF/26WMwmVfFwkb+ns/w9Xf6+ebioSv7HVTxcdCa7oHCRpbiUs3kFjNQsxaVkXtHfiZPZpGrnFFfOLXyFVyla8SvHanMUOjKzbTqTk5v/vZP/fZKZ971S8Hla/rGCjwu+l2z520IF+7opxMdNofmFi5D8z4PyCxjF5dGuktVm1964VP1xOEl/HDmtjUeSlJiWpU1HT2vT0dOaoYMymaSGIT6qXsVdu2JSdCIl87xruTqZ1ayar1qE+yky3E8twv1UvYo720eh2AX5uCom+awS087/PgQAAADKI0M7NcoqOjVQHLp27aqNGzfq//7v//T2228bHafI+F6HlNdtkJielb/AelY5VrusNpusNslqt8tqtclql+OYzW5XrrXwHvUZWbkX7A5Izcy55q1WLqaan7tuahKsHk1C1CbCv9jfuWy32xWddEab87swtkQna19cqmz/eDnuzhZFhvuqdc0qiqzuJ09XJ1nz9+e35n+drLa827lfu4JjVptdmTnWwou62YULBBlZVqVl5uQv8BZ+frNJfy/0/3Ph/x83T1cnJaZlnbewnph2ddsKGa3gtdtseV0IReFsMeX/nRT9eU0mKaKqp+oHe6lBsLfqh+QVMCICPOVcxBkMOVabYpMzz9nmKe/v5vjpM0o6k63TGXl/78XFz8NZIT55RYGC4kCor5ujoBXi41bscyQqArvdrqOnzmjjkaT8QkeSjpw6U+gck0mqF+TlKGBEVvdTgxDvIn9PlGVloSOhLCoLX5eHP92s5bvi9OKtTXTvdRGGZAAAAOUH60EoaeW+UwOoyFavXm10BFRgaZk5WnvgpH47cFI2e972Knnv1P77VsXTRVU9XS67z3tGVm7eO8X/sVf+sdNndSzpjLKKeU/6f3K2mM5bbPd0dcrbYsXy97Yq574z3vXcbVny7zubY9XPexO0el+iYpLPavbaI5q99oiqerroxsbBimoSouvqVi3yu7Yzc6w6kJCuvfnbCe2LS9OuEyk6mX7+O/KrV3FXqxpV1Lpm3q1hSPEPE74Yu92uzByb0rLyikXebk7ycnG65nkMZ7Jzdfz0Wcf3R3T+onrBx1m5Nnm5/D3nwOuc23kzEdyc5OVqkbuzk4r6ZnS7XRctkP3zlpVrk80uJZ/JcTze2WJSoJerAr3zbgHnfFxwvOCYZ/6shlxrXkdQTq5dWed0CeWcM7g565xjob5uqhPoJXeX4ukMcLaY8woKVS++B35mjlXJZ3KUlJGddzuTrdMZ2TqVkfdn0plsJaVn6/SZbLm7WAoVLQq2YwrJ72yoCAPLjWAymRQR4KmIAE8NaBMuSUpIzdTGI0mKS8lU4zAfNa/uJy9XfuyGMYJ88mYHJtCpAQAAgAqC364AoByw2/OGyv68N0E/7U3QH0eSrrjLwd3ZUqjQ4e/hLJtdOnY6r4BxocX5c1nMJoX5uSnM112uzhY5mfP2mnfKnw1R6GYyyWLJ/zP/PC9Xy/ndAR5/f+zubCm27VYGtAlXZo5Va/5K1Ipd8fpxT7xOZWRr/h/HNP+PY/JyddK/GwYpqkmIujYIdCxeS3nvij96KiOveBGXP9A5Pl1HTmXoQj2NLhazmlbzcRQxWtWsomAf497FYjKZ5O5iKbYF9QIeLk6qH5y3ZdI/2e122e0qE4Osz5WZY3UUPCQpyNvtquZOOFnMeUUpF0kqm3NF3JwtCvG1KMSXd1CVJUE+brqleZjRMQBJcvy/KT61fHbeAQAAAP9EUQMAyqjMHKs2HE5yFDKikwpvZ1IrwFNdGwTK193Z8S7t02eydSr/XdlJGdnKseYN641JPquY5LMXfS5fd+e/98b/x175oX5u5WqLFDdni25qEqKbmoQox2rTxsNJWr4zTit2xSkhLUtLtp3Qkm0n5OJkVpd6AfJ0ddK+uDQdSsy46ByKKh7OapC/jVD9EG81DPFRkzCfSv/OdpPJVOSOi9Lg5myRm7NFQQYWmQCgrAjyLujUoKgBAACAioGiBgCUISeSz+rnfQn6eW+C1h44VWh4r4vFrPa1/XVDgyDd0DBItQI8L3ktu92u9Kxcnc7I0amMrPxCR46SMrJkt6vQ0OeiDPgtT5wtZnWsG6COdQP04q1NtPV4slbkFziOnDqjH/ckFDrf08WiesHnFi/yOhQCvFwY3gsAKJcKCrwJqWw/BQAAgIqBogYAGMhut2vXiVSt2BWnlbvjtTcurdD9wT6u+nfDIHVtEKROdQMKbZd0OSaTSd5uzvJ2c77knvyVhdlsUqsaVdSqRhU917Oh9sWn6ae9CbLb5SheVPNzL3NbKQEAcC2CfejUAAAAQMVCUQMASpnVZtfmo6e1Yldex8Dx039vC2U2SS1rVMkvZASqcagPHQIlwGQyqWGIjxqG+BgdBQCAEhXkndepkZSRraxcq1ydKvfWiQAAACj/KGoAQCnIzrVp3cGTjo6Mc4dzuzmbdX39QEU1CdENDYJUxdPFwKQAAKAiqeLhLGeLSTlWuxLTslS9Ct2bAAAAKN/Kz+RXGK5r164aMWKE4/OIiAhNnTr1ko8xmUxavHjxNT93cV0HKE0ZWbn6fkes/jP/T7WetFJDZ/+hzzce08n0bPm4Oen2ltU08+7W+nPsTfrvPW10e6vqFDQAAECxMplMjm4NtqACAAC4ONY+yw86NSqB3r17KycnR8uXLz/vvl9//VVdunTRtm3b1Lx58yJd948//pCn56UHFRfVhAkTtHjxYm3durXQ8djYWFWpUqVYn+tizp49q2rVqslsNismJkaurq6l8rwoWfvj0/TjngS5OZvl6+583s3H3Vluzle+HYPNZldaZq5SzuYo+Wy2Us7mFLptOZqsX/cnKivX5nhMoLeropoEK6pJiP5Vu6qcLdSVAQBAyQvycVVM8lmGhQMAgAqJtc8rM2fOHI0YMULJyckl+jylgaJGJXD//ferX79+On78uKpXr17ovtmzZ6tNmzZF/kctSYGBgcUV8bJCQkJK7bkWLlyoJk2ayG63a/HixRo4cGCpPfc/2e12Wa1WOTnxT/VqZWTl6p1V+/XRb4dltdkvea6r0/kFDx93Z2XlWgsXLc7kKC0rV/ZLX06SVMPfQz2ahiiqSYhahvsxhBoAAJS6YDo1AABABcbaZ+XD24QrgVtuuUWBgYGaM2dOoePp6en68ssvdf/99+vUqVMaPHiwqlWrJg8PDzVr1kyff/75Ja/7zxas/fv3q0uXLnJzc1Pjxo21cuXK8x7z7LPPqn79+vLw8FDt2rU1duxY5eTkSMqrFr744ovatm2bTCaTTCaTI/M/W7B27Nihf//733J3d1fVqlX10EMPKT093XH/0KFDddttt+nNN99UaGioqlatqscee8zxXJfy0Ucf6e6779bdd9+tjz766Lz7d+3apVtuuUU+Pj7y9vZW586ddfDgQcf9s2bNUpMmTeTq6qrQ0FANHz5cknTkyBGZTKZCldjk5GSZTCatXr1akrR69WqZTCYtW7ZMrVu3lqurq3777TcdPHhQffr0UXBwsLy8vNS2bVv9+OOPhXJlZWXp2WefVXh4uFxdXVW3bl199NFHstvtqlu3rt58881C52/dulUmk0kHDhy47NekPLLb7Vq2I1bdp/yiD9YcktVmV6e6Abq5Wag61Q1Qs2q+quHvIV93ZxXM4c7KtSkhLUv7E9K16ehprdqboK//jNH3O+K09sAp7YxJ1bGks0rN/Lug4e5sUYiPmxoEe6tdhL+6NwpWv1bVNfLG+lr2n8765emuer5XI7WuWYWCBgAAMESQT17ncTydGgAAoAJi7bNoa58XEx0drT59+sjLy0s+Pj4aMGCA4uPjHfdv27ZNN9xwg7y9veXj46PWrVtr06ZNkqSjR4+qd+/eqlKlijw9PdWkSRN9//33V53lcnj7d3Gw26WcM6X/vM4ecqzGXoKTk5OGDBmiOXPm6IUXXpAp/zFffvmlrFarBg8erPT0dLVu3VrPPvusfHx8tHTpUt1zzz2qU6eO2rVrd9nnsNlsuv322xUcHKwNGzYoJSWl0B50Bby9vTVnzhyFhYVpx44devDBB+Xt7a1nnnlGAwcO1M6dO7V8+XLHgr2vr+9518jIyFBUVJQ6dOigP/74QwkJCXrggQc0fPjwQv/x+vnnnxUaGqqff/5ZBw4c0MCBA9WiRQs9+OCDF30dBw8e1Pr167Vo0SLZ7XY9+eSTOnr0qGrWrClJiomJUZcuXdS1a1f99NNP8vHx0dq1a5WbmytJmjFjhkaOHKlXX31VPXv2VEpKitauXXvZr98/Pffcc3rzzTdVu3ZtValSRceOHVOvXr308ssvy9XVVZ988ol69+6tffv2qUaNGpKkIUOGaP369Zo2bZoiIyN1+PBhnTx5UiaTSffdd59mz56tUaNGOZ5j9uzZ6tKli+rWrVvkfGXd0VMZGr9kl1bvS5Qkhfu768Vbm+jfDYMveL7NZldaVq5Sz+nGKPg4NTNHrk6WQp0b53ZyuDhRGwYAAGVbsE9+p0YqnRoAAKCIjFr3lFj7LIG1z0u9voKCxi+//KLc3Fw99thjGjhwoOPN2HfddZdatmypGTNmyGKxaOvWrXJ2dpYkPfbYY8rOztaaNWvk6emp3bt3y8vLq8g5rhRFjeKQc0Z6Jaz0n/f5E5LLle3rdt999+mNN97QL7/8oq5du0rKW9Tu16+ffH195evrW2jB+/HHH9eKFSv0xRdfXNE/7B9//FF79+7VihUrFBaW97V45ZVX1LNnz0LnjRkzxvFxRESERo0apfnz5+uZZ56Ru7u7vLy85OTkdMmWq3nz5ikzM1OffPKJY1+7d999V71799Zrr72m4OC8hesqVaro3XfflcViUcOGDXXzzTdr1apVl/yHPWvWLPXs2dOxh11UVJRmz56tCRMmSJLee+89+fr6av78+Y5/tPXr13c8/qWXXtJTTz2l//znP45jbdu2vezX758mTpyoG2+80fG5v7+/IiMjHZ9PmjRJX3/9tZYsWaLhw4frr7/+0hdffKGVK1eqe/fukqTatWs7zh86dKjGjRunjRs3ql27dsrJydG8efPO694o7zJzrPrvL4f03uoDys61ydli0sPX19GjXevK3eXi8zLMZpOjSBFeinkBAABKQ6B3fqcG208BAICiMmrdU2LtswTWPi9m1apV2rFjhw4fPqzw8LzVsU8++URNmjTRH3/8obZt2yo6OlpPP/20GjZsKEmqV6+e4/HR0dHq16+fmjVrJqnwumRJ4C3GlUTDhg113XXXadasWZKkAwcO6Ndff9X9998vSbJarZo0aZKaNWsmf39/eXl5acWKFYqOjr6i6+/Zs0fh4eGOf9SS1KFDh/POW7BggTp27KiQkBB5eXlpzJgxV/wc5z5XZGRkoUE9HTt2lM1m0759+xzHmjRpIovl74Xs0NBQJSQkXPS6VqtVH3/8se6++27Hsbvvvltz5syRzZY37Hnr1q3q3Lmzo6BxroSEBJ04cULdunUr0uu5kDZt2hT6PD09XaNGjVKjRo3k5+cnLy8v7dmzx/G127p1qywWi66//voLXi8sLEw333yz4+//22+/VVZWlu64445rzlpW/Lo/UT3f+VVv//iXsnNt6li3qpaP6KKnbmpwyYIGAABARfd3pwbbTwEAgIqJtc/Lr31e7jnDw8MdBQ1Jaty4sfz8/LRnzx5J0siRI/XAAw+oe/fuevXVVwttx//EE0/opZdeUseOHTV+/Hht3779qnJcKTo1ioOzR17l0IjnLYL7779fjz/+uN577z3Nnj1bderUcSyCv/HGG3rnnXc0depUNWvWTJ6enhoxYoSys7OLLe769et111136cUXX1RUVJSj4+Gtt94qtuc41z8LDyaTyVGcuJAVK1YoJibmvMHgVqtVq1at0o033ih3d/eLPv5S90mS2ZxXQ7SfM136YvvcnfsfLUkaNWqUVq5cqTfffFN169aVu7u7+vfv7/j7udxzS9IDDzyge+65R2+//bZmz56tgQMHysOjaN9DZVFcSqYmLd2tpdtjJUlB3q4ae0tj3dI81NFuCAAAUJkF5XdqMCgcAAAUmVHrngXPXQSsfV567fNaTZgwQXfeeaeWLl2qZcuWafz48Zo/f7769u2rBx54QFFRUVq6dKl++OEHTZ48WW+99ZYef/zxEslCp0ZxMJnyWqFK+1bEBdsBAwbIbDZr3rx5+uSTT3Tfffc5Fn3Xrl2rPn366O6771ZkZKRq166tv/7664qv3ahRIx07dkyxsbGOY7///nuhc9atW6eaNWvqhRdeUJs2bVSvXj0dPXq00DkuLi6yWq2Xfa5t27YpIyPDcWzt2rUym81q0KDBFWf+p48++kiDBg3S1q1bC90GDRrkGBjevHlz/frrrxcsRnh7eysiIkKrVq264PUDAwMlqdDX6Nyh4Zeydu1aDR06VH379lWzZs0UEhKiI0eOOO5v1qyZbDabfvnll4teo1evXvL09NSMGTO0fPly3XfffVf03CUtO9emHGvR/4Oba7Xpw18Pqdtbq7V0e6zMJum+jrW06qnr1TsyjIIGAABAvoJOjaSMbGXnltwvugAAoAIyat2Ttc9iX/u83HMeO3ZMx44dcxzbvXu3kpOT1bhxY8ex+vXr68knn9QPP/yg22+/XbNnz3bcFx4erocffliLFi3SU089pf/9738lklWiU6NS8fLy0sCBAzV69GilpqZq6NChjvvq1aunr776SuvWrVOVKlU0ZcoUxcfHF/qmvZTu3burfv36uvfee/XGG28oNTVVL7zwQqFz6tWrp+joaM2fP19t27bV0qVL9fXXXxc6JyIiQocPH9bWrVtVvXp1eXt7y9XVtdA5d911l8aPH697771XEyZMUGJioh5//HHdc889jj3liioxMVHffvutlixZoqZNmxa6b8iQIerbt6+SkpI0fPhwTZ8+XYMGDdLo0aPl6+ur33//Xe3atVODBg00YcIEPfzwwwoKClLPnj2VlpamtWvX6vHHH5e7u7v+9a9/6dVXX1WtWrWUkJBQaJ+9S6lXr54WLVqk3r17y2QyaezYsYUqrxEREbr33nt13333OQaFHz16VAkJCRowYIAkyWKxaOjQoRo9erTq1at3wRa50vb5xmhN+m63zmRb5epklperk7zcnOTpkvenl6uT41jBx56uTnJ1Muuz349qb1yaJKllDT+9dFtTNQk7f7gSAABAZVfFw1nOFpNyrHYlpmepmt/lu3wBAADKG9Y+L89qtZ73JmtXV1d1795dzZo101133aWpU6cqNzdXjz76qK6//nq1adNGZ8+e1dNPP63+/furVq1aOn78uP744w/169dPkjRixAj17NlT9evX1+nTp/Xzzz+rUaNG15T1UujUqGTuv/9+nT59WlFRUYX2gBszZoxatWqlqKgode3aVSEhIbrtttuu+Lpms1lff/21zp49q3bt2umBBx7Qyy+/XOicW2+9VU8++aSGDx+uFi1aaN26dRo7dmyhc/r166cePXrohhtuUGBgoD7//PPznsvDw0MrVqxQUlKS2rZtq/79+6tbt2569913i/bFOEfB4J0LzcPo1q2b3N3d9dlnn6lq1ar66aeflJ6eruuvv16tW7fW//73P0e717333qupU6fq/fffV5MmTXTLLbdo//79jmvNmjVLubm5at26tUaMGKGXXnrpivJNmTJFVapU0XXXXafevXsrKipKrVq1KnTOjBkz1L9/fz366KNq2LChHnzwwUIVXSnv7z87O1vDhg0r6peoWNlsdk1etkejF+3Qmey86nRWrk2nMrJ19NQZ7Y5N1cbDSfppb4KWbDuheRui9cGaQ5qy8i9N+m63xizeqb1xafLzcNartzfTwoevo6ABAABwESaTSUHeed0a8czVAAAAFRhrn5eWnp6uli1bFroVvIn6m2++UZUqVdSlSxd1795dtWvX1oIFCyTlvVn61KlTGjJkiOrXr68BAwaoZ8+eevHFFyXlFUsee+wxNWrUSD169FD9+vX1/vvvX3PeizHZz93gH5Kk1NRU+fr6KiUlRT4+PoXuy8zM1OHDh1WrVi25ubkZlBC4Or/++qu6deumY8eOXbayW1Lf65k5Vj31xTYt3ZHXrjeiez0NvS5C6Vm5ysiyKj0rR2mZf3+cnmVVembu3x9n5So9M0e1Arw0/N915e/pUmzZAADA1bnUz8+VWVn6uvR9f63+jE7WzLtbq0fTEEOzAACAsou1T5S0S32PXenPz2w/BVQCWVlZSkxM1IQJE3THHXdcc6va1TqVnqUHP9mkLdHJcraY9Hr/5urbsrokyc+D4gQAAEBJ+XtYOJ0aAAAAKN/YfgqoBD7//HPVrFlTycnJev311w3JcCAhXX3fX6ct0cnydXfWp/e3dxQ0AAAAULIKhoUnpGYZnAQAAAC4NnRqAJXA0KFDCw1HKm2/Hzql//t0s1LO5qiGv4dmD2urOoFehuUBAACobAo6NZipAQAAgPKOogaAErVoy3E9u3C7cqx2tazhpw+HtFFVL1ejYwEAAFQqQfmdGvFpdGoAAACgfKOoAaBE2O12vbNqv6b+uF+SdHOzUL01IFJuzhaDkwEAAFQ+jpkadGoAAACgnKOocZVsNpvREYASdS3f49m5Nj23aLsWbYmRJD18fR09E9VAZrOpuOIBAACgCBwzNejUAAAAV8ButxsdARVUcayrU9QoIhcXF5nNZp04cUKBgYFycXGRycRCLSoOu92u7OxsJSYmymw2y8XFpUiPTzmTo//7bJN+P5Qki9mkl25rqsHtapRQWgAAAFyJgqJGUka2snNtcnEyG5wIAACURc7OzjKZTEpMTFRgYCDrnig217rmeC6KGkVkNptVq1YtxcbG6sSJE0bHAUqMh4eHatSoIbP5yn/hjT51RkPnbNShxAx5uTrp/btaqUv9wBJMCQAAgCtRxcNZzhaTcqx2JaZnqZqfu9GRAABAGWSxWFS9enUdP35cR44cMToOKqCrWXP8J4oaV8HFxUU1atRQbm6urFar0XGAYmexWOTk5HTF1fjEtCyt3pegV5ft1amMbIX5umnWsLZqGOJTwkkBAABwJUwmk4K83RSTfFYJqZkUNQAAwEV5eXmpXr16ysnJMToKKpiirjleDEWNq2QymeTs7CxnZ2ejowClLjvXps1HT2vN/kSt+StRu06kOu5rWs1HH93b1rHFAQAAAMqGQG9XxSSfVXwqczUAAMClWSwWWSwWo2MAF0RRA8AVOXoqQ2v+StQvf53U+oMnlZFduEupaTUf/bthsP6vS215uvKfFgAAgLIm2MdVkpSYlmlwEgAAAODqsfII4IIysnK1/uApRzfGkVNnCt0f4OWizvUCdX39QHWqF6AAL1eDkgIAAOBKBHnnddLSqQEAAIDyjKIGgEJyrTY9u3CHlmyLUY7V7jjuZDapdc0q6lI/r5DRONRHZvO17X8HAACA0lPQqRGfSqcGAAAAyi+KGgAKmbshWgu3HJckhfu76/r6gepSL1Ad6lSVtxszZAAAAMqroPyZZwlpdGoAAACg/KKoAcDhVHqW3vphnyRpfO/GGnpdhEwmujEAAAAqgiBvOjUAAABQ/pmNDgCg7Hjzh7+UmpmrRqE+GtKBggYAAEBFEpzfqZFIpwYAAADKMYoaACRJO46naP4f0ZKkF29tIgvzMgAAACqUgk6NUxnZys61GZwGAAAAuDoUNQDIZrNr/JKdstulPi3C1K6Wv9GRAAAArsmaNWvUu3dvhYWFyWQyafHixY77cnJy9Oyzz6pZs2by9PRUWFiYhgwZohMnThS6RlJSku666y75+PjIz89P999/v9LT00v5lRSfKh4ucrbkvXHlZDrdGgAAACifKGoA0Nd/xmhLdLI8XCwa3bOR0XEAAACuWUZGhiIjI/Xee++dd9+ZM2e0ZcsWjR07Vlu2bNGiRYu0b98+3XrrrYXOu+uuu7Rr1y6tXLlS3333ndasWaOHHnqotF5CsTObTQr0Yq4GAAAAyjcGhQOVXFpmjl5dvleSNPzfdRXi62ZwIgAAgGvXs2dP9ezZ84L3+fr6auXKlYWOvfvuu2rXrp2io6NVo0YN7dmzR8uXL9cff/yhNm3aSJKmT5+uXr166c0331RYWFiJv4aSEOTjphMpmYpPpVMDAAAA5ROdGkAlN/2nA0pMy1KtAE/d36mW0XEAAAAMkZKSIpPJJD8/P0nS+vXr5efn5yhoSFL37t1lNpu1YcMGg1Jeu4K5GolpdGoAAACgfKJTA6jEDiSka9ZvhyVJ425pLFcni8GJAAAASl9mZqaeffZZDR48WD4+PpKkuLg4BQUFFTrPyclJ/v7+iouLu+B1srKylJX1dwdEampqyYW+SsE+eV25dGoAAACgvKJTA6ik7Ha7Xvx2l3JtdnVrGKQbGgZd/kEAAAAVTE5OjgYMGCC73a4ZM2Zc07UmT54sX19fxy08PLyYUhafYJ+8To0EOjUAAABQTlHUACqplbvj9ev+k3KxmDX2lsZGxwEAACh1BQWNo0ePauXKlY4uDUkKCQlRQkJCofNzc3OVlJSkkJCQC15v9OjRSklJcdyOHTtWovmvRpA3nRoAAAAo39h+CqiEMnOsmrR0tyTpgc61FBHgaXAiAACA0lVQ0Ni/f79+/vlnVa1atdD9HTp0UHJysjZv3qzWrVtLkn766SfZbDa1b9/+gtd0dXWVq6triWe/FkGOTg2KGgAAACifKGoAldAHaw7pWNJZhfi46bEb6hodBwAAoNilp6frwIEDjs8PHz6srVu3yt/fX6Ghoerfv7+2bNmi7777Tlar1TEnw9/fXy4uLmrUqJF69OihBx98UDNnzlROTo6GDx+uQYMGKSwszKiXdc0KOjUSUtl+CgAAAOUTRQ2gkjl++ozeX533C/7zNzeSpyv/GQAAABXPpk2bdMMNNzg+HzlypCTp3nvv1YQJE7RkyRJJUosWLQo97ueff1bXrl0lSXPnztXw4cPVrVs3mc1m9evXT9OmTSuV/CWlYKbGqYxs5VhtcrawIzEAAADKF1YzgUpm8vd7lZljU7ta/urdPNToOAAAACWia9eustvtF73/UvcV8Pf317x584ozluGqeLjIyWxSrs2uxLQshfm5Gx0JAAAAKBLelgNUIusOnNTSHbEym6QJvZvIZDIZHQkAAAClyGw2Kcg7r1sjni2oAAAAUA5R1AAqiRyrTRO+3SVJuvtfNdU4zMfgRAAAADBCkE/+XA2GhQMAAKAcoqgBVBKfrj+qv+LTVcXDWSNvrG90HAAAABikoFODYeEAAAAojyhqAJXAyfQsvf3jX5Kkp6Mays/DxeBEAAAAMEownRoAAAAoxyhqAJXAG8v3KS0zV02r+Whg23Cj4wAAAMBAzNQAAABAeUZRA6jgth1L1hebj0mSXry1iSxmhoMDAABUZnRqAAAAoDwzvKjx3nvvKSIiQm5ubmrfvr02btx40XNzcnI0ceJE1alTR25uboqMjNTy5csLnTNhwgSZTKZCt4YNG5b0ywDKpFyrTeOX7JLdLt3esppa1/Q3OhIAAAAMFuhT0KlBUQMAAADlj5ORT75gwQKNHDlSM2fOVPv27TV16lRFRUVp3759CgoKOu/8MWPG6LPPPtP//vc/NWzYUCtWrFDfvn21bt06tWzZ0nFekyZN9OOPPzo+d3Iy9GUCpSI9K1d7Y1O1OzZVe2JTtftEqvbFpykzxyYvVyc915PiHgAAAKRg7/xODbafAgAAQDlk6Gr/lClT9OCDD2rYsGGSpJkzZ2rp0qWaNWuWnnvuufPO//TTT/XCCy+oV69ekqRHHnlEP/74o9566y199tlnjvOcnJwUEhJSOi8CKGV2u10nUjK158Q5BYzYVB09deaC53u5Ounlvk0VlL/NAAAAACq3oPxOjVMZ2cqx2uRsMbyBHwAAALhihhU1srOztXnzZo0ePdpxzGw2q3v37lq/fv0FH5OVlSU3t8ILs+7u7vrtt98KHdu/f7/CwsLk5uamDh06aPLkyapRo0bxvwigFKSczdHmo0naePi0th47rT2xaUo5m3PBc0N83NQ4zEeNQr3VONRXjcN8VNPfQ2bmaAAAACCfv4eLnMwm5drsSkzLUpifu9GRAAAAgCtmWFHj5MmTslqtCg4OLnQ8ODhYe/fuveBjoqKiNGXKFHXp0kV16tTRqlWrtGjRIlmtVsc57du315w5c9SgQQPFxsbqxRdfVOfOnbVz5055e3tf8LpZWVnKyvp7P9nU1NRieIXA1UlIzdTGI0n643CSNhxO0r74NNnthc9xMptUN8hLjUN91CjUJ7+Q4SN/TxdjQgMAAKDcMJtNCvJ21YmUTCVQ1AAAAEA5U66GTbzzzjt68MEH1bBhQ5lMJtWpU0fDhg3TrFmzHOf07NnT8XHz5s3Vvn171axZU1988YXuv//+C1538uTJevHFF0s8P/BPdrtdR06d0R+Hk/IKGUeSLriNVK0AT7WNqKI2Nf3VOMxH9YK95OpkMSAxAAAAKoJAHzedSMlUPHM1AAAAUM4YVtQICAiQxWJRfHx8oePx8fEXnYcRGBioxYsXKzMzU6dOnVJYWJiee+451a5d+6LP4+fnp/r16+vAgQMXPWf06NEaOXKk4/PU1FSFh4cX8RUBV+77HbFauj1WG48kKTEtq9B9JpPUKMRH7Wr5q22Ev9rWqqIgb+ZhAAAAoPgEe+fN1Uj4x8+iAAAAQFlnWFHDxcVFrVu31qpVq3TbbbdJkmw2m1atWqXhw4df8rFubm6qVq2acnJytHDhQg0YMOCi56anp+vgwYO65557LnqOq6urXF1dr+p1AEW1ZNsJPfH5n47PXSxmRYb75hcw/NW6ZhX5uDkbmBAAAAAVXcGw8AQ6NQAAAFDOGLr91MiRI3XvvfeqTZs2ateunaZOnaqMjAwNGzZMkjRkyBBVq1ZNkydPliRt2LBBMTExatGihWJiYjRhwgTZbDY988wzjmuOGjVKvXv3Vs2aNXXixAmNHz9eFotFgwcPNuQ1AueKST6rF77eIUm6vVU1DWpbQ82r+8rNma2kAAAAUHqC8zuBE1Lp1AAAAED5YmhRY+DAgUpMTNS4ceMUFxenFi1aaPny5Y7h4dHR0TKbzY7zMzMzNWbMGB06dEheXl7q1auXPv30U/n5+TnOOX78uAYPHqxTp04pMDBQnTp10u+//67AwMDSfnlAIVabXU/O36q0zFy1CPfTa/2ay9livvwDAQAAgGJW0KkRn0anBgAAAMoXwweFDx8+/KLbTa1evbrQ59dff7127959yevNnz+/uKIBxWrmLwe18UiSPF0semdQCwoaAAAAMEyQT16nRjydGgAAAChnWFUFSsHWY8l6e+VfkqQX+zRVzaqeBicCAABAZVaw/VQinRoAAAAoZyhqACUsIytXI+b/qVybXTc3D1W/VtWMjgQAAIBKrmD7qZPp2cqx2gxOAwAAAFw5ihpACZv47W4dOXVGob5ueuW2ZjKZTEZHAgAAQCXn7+EiJ3Pez6Un09mCCgAAAOUHRQ2gBC3bEasFm47JZJKmDGghXw9noyMBAAAAMptNCvTOHxbOXA0AAACUIxQ1gBISm3JWzy3aIUl6+Po66lCnqsGJAAAAgL8VDAtPSGWuBgAAAMoPihpACbDZ7Hrqi21KOZujZtV89WT3+kZHAgAAAAoJKujUSKNTAwAAAOUHRQ2gBHz42yGtO3hK7s4WTR3UQi5O/FMDAABA2RKcPyycTg0AAACUJ6y0AsVsZ0yK3lixT5I0rndj1Qn0MjgRAAAAcL4g74Ltp+jUAAAAQPlBUQMoRmezrfrP/D+VY7XrpsbBGtQ23OhIAAAAwAUVdGrEp9GpAQAAgPKDogZQjF7+frcOJmYoyNtVr/ZrLpPJZHQkAAAA4IL+HhROpwYAAADKD4oaQDH5cXe8Pvs9WpL01oBI+Xu6GJwIAAAAuLiCQeEJdGoAAACgHKGoARSDhLRMPbNwuyTpgU611LleoMGJAAAAgEsLzu/UOJWRrRyrzeA0AAAAwJWhqAFcI5vNrlFfbldSRrYahnjr6R4NjI4EAAAAXJa/h4uczCbZ7dLJdLagAgAAQPlAUQO4Rh+vP6I1fyXK1cmsaYNbytXJYnQkAAAA4LLMZpMCC7agYq4GAAAAygmKGsA12H48WZOX7ZUkvXBzI9UP9jY4EQAAAHDlCuZqxKcyVwMAAADlA0UN4Cr9uj9Rgz/4Xdm5Nt3QIFD3/Kum0ZEAAACAIgnKn6sRn0anBgAAAMoHJ6MDAOXRoi3H9cxX25Vrs6tD7ap6Z3BLmUwmo2MBAAAARRLsk9epkUinBgAAAMoJihpAEdjtdr2/+qDeWLFPknRrZJjeuKM5czQAAABQLgV553dqMFMDAAAA5QRFDeAKWW12TViyS5/+flSS9H9dauvZHg1lNtOhAQAAgPKpoFMjIY1ODQAAAJQPFDWAK5CZY9UTn/+pH3bHy2SSxt7cWPd1qmV0LAAAAOCa0KkBAACA8oaiBnAZpzOy9cAnm7T56Gm5OJk1dWAL9WoWanQsAAAA4JoFOTo1KGoAAACgfKCoAVzCsaQzunf2Rh1KzJCPm5M+vLet2tXyNzoWAAAAUCwKOjVOZWQp12qTk8VscCIAAADg0ihqABexMyZFw+b8ocS0LIX5uunj+9qpXrC30bEAAACAYlPV00UWs0lWm10n07MV4utmdCQAAADgkngbDnABv+5P1MD/rldiWpYahnhr0aMdKWgAAACgwjGbTQr0ytuCKj6VYeEAAAAo+yhqAP+waMtxDZv9hzKyrbquTlV98XAH3rEGAACACivYh6IGAAAAyg+2nwLy2e12vb/6oN5YsU+S1KdFmN7oHykXJ2p/AAAAqLiCfNwkpTAsHAAAAOUCRQ0g36vL9uq/aw5Jkv6vS20926OhzGaTwakAAACAkhXkndepkUCnBgAAAMoBihqApJW74x0FjfG9G2tYx1oGJwIAAABKR7BP3lardGoAAACgPGBfHVR6CamZenbhdknSg51rUdAAAABApVLQqcFMDQAAAJQHFDVQqdlsdj315TYlZWSrUaiPRkU1MDoSAAAAUKro1AAAAEB5QlEDldqcdUf06/6TcnUya9qgFnJ1shgdCQAAAChVgY5ODYoaAAAAKPsoaqDS2hObqleX7ZUkjbmlseoFexucCAAAACh9BZ0apzKylGu1GZwGAAAAuDSKGqiUMnOsGjF/q7KtNnVrGKS729cwOhIAAABgiKqeLrKYTbLbpZPp2UbHAQAAAC6JogYqpVeX7dW++DQFeLnqtf7NZTKZjI4EAAAAGMJsNinQi2HhAAAAKB8oaqDS+XlfguasOyJJevOO5grI/wUOAAAAqKyCffJ+JmZYOAAAAMo6ihqoVE6mZ+npL7dLkoZeF6GuDYIMTgQAAAAYL9A7b64GnRoAAAAo6yhqoNKw2+169qvtOpmepQbB3nquZ0OjIwEAAABlAp0aAAAAKC8oaqDS+Oz3o1q1N0EuTma9M7iF3JwtRkcCAAAAyoSg/E6NBDo1AAAAUMZR1EClsD8+TS8t3SNJeq5HQzUM8TE4EQAAAFB20KkBAACA8oKiBiq8rFyrnpi/VVm5NnWpH6ih10UYHQkAAAAoU4LyixrM1AAAAEBZR1EDFd6bK/ZpT2yq/D1d9Gb/5jKbTUZHAgAAAMqUIMegcDo1AAAAULZR1ECF9tv+k/rfr4clSa/1a64gHzeDEwEAAABlT3D+z8mnMrKUa7UZnAYAAAC4OIoaqLBOZ2Rr5BdbJUl3ta+hGxsHGxsIAAAApWbNmjXq3bu3wsLCZDKZtHjx4kL32+12jRs3TqGhoXJ3d1f37t21f//+QuckJSXprrvuko+Pj/z8/HT//fcrPT29FF9F6anq6SKL2SS7XTqZnm10HAAAAOCiKGqgQrLb7Xpu0XYlpGWpdqCnxtzc2OhIAAAAKEUZGRmKjIzUe++9d8H7X3/9dU2bNk0zZ87Uhg0b5OnpqaioKGVm/j1T4q677tKuXbu0cuVKfffdd1qzZo0eeuih0noJpcpsNinQq2BYOHM1AAAAUHY5GR0AKAkL/jimFbvi5WwxadqglnJ3sRgdCQAAAKWoZ8+e6tmz5wXvs9vtmjp1qsaMGaM+ffpIkj755BMFBwdr8eLFGjRokPbs2aPly5frjz/+UJs2bSRJ06dPV69evfTmm28qLCys1F5LaQnycVVcaiZzNQAAAFCm0amBCudE8lm9+O1uSdKomxqoaTVfgxMBAACgLDl8+LDi4uLUvXt3xzFfX1+1b99e69evlyStX79efn5+joKGJHXv3l1ms1kbNmy44HWzsrKUmppa6FaeFAwLp1MDAAAAZRlFDVQ403/ar7M5VrWpWUUPdq5tdBwAAACUMXFxcZKk4ODCM9eCg4Md98XFxSkoKKjQ/U5OTvL393ec80+TJ0+Wr6+v4xYeHl4C6UtOkE/e9lN0agAAAKAso6iBCiX61Bl9uem4JOnZng1lNpsMTgQAAIDKYvTo0UpJSXHcjh07ZnSkIgku6NRIpVMDAAAAZRdFDVQo76zar1ybXV3qB6pthL/RcQAAAFAGhYSESJLi4+MLHY+Pj3fcFxISooSEhEL35+bmKikpyXHOP7m6usrHx6fQrTwJ9ikYFE6nBgAAAMouihqoMA4kpOvrP/O6NEbeWN/gNAAAACiratWqpZCQEK1atcpxLDU1VRs2bFCHDh0kSR06dFBycrI2b97sOOenn36SzWZT+/btSz1zafh7+yk6NQAAAFB2ORkdACgu76zaL5td6t4oSC3C/YyOAwAAAAOlp6frwIEDjs8PHz6srVu3yt/fXzVq1NCIESP00ksvqV69eqpVq5bGjh2rsLAw3XbbbZKkRo0aqUePHnrwwQc1c+ZM5eTkaPjw4Ro0aJDCwsIMelUl6+9B4XRqAAAAoOyiqIEKYV9cmr7bfkKS9CRdGgAAAJXepk2bdMMNNzg+HzlypCTp3nvv1Zw5c/TMM88oIyNDDz30kJKTk9WpUyctX75cbm5ujsfMnTtXw4cPV7du3WQ2m9WvXz9Nmzat1F9LaSno1DiZnqVcq01OFhr7AQAAUPZQ1ECF8PbKv2S3S72ahahJmK/RcQAAAGCwrl27ym63X/R+k8mkiRMnauLEiRc9x9/fX/PmzSuJeGVSVU9XWcwmWW12ncrIVrCP2+UfBAAAAJQyw99689577ykiIkJubm5q3769Nm7ceNFzc3JyNHHiRNWpU0dubm6KjIzU8uXLr+maKP92xqRo+a44mUzSiO50aQAAAABXw2I2KcDLRRJzNQAAAFB2GVrUWLBggUaOHKnx48dry5YtioyMVFRUlBISEi54/pgxY/Tf//5X06dP1+7du/Xwww+rb9+++vPPP6/6mij/pqz8S5J0a2SY6gd7G5wGAAAAKL8KujMSUpmrAQAAgLLJ0KLGlClT9OCDD2rYsGFq3LixZs6cKQ8PD82aNeuC53/66ad6/vnn1atXL9WuXVuPPPKIevXqpbfeeuuqr4nybUv0af20N0EWs0n/6VbP6DgAAABAuRbknTdXIz6NTg0AAACUTYYVNbKzs7V582Z179797zBms7p3767169df8DFZWVmFBvdJkru7u3777bervmbBdVNTUwvdUD5M+SGvS+P2ltVUO9DL4DQAAABA+RaU36kRT6cGAAAAyijDihonT56U1WpVcHBwoePBwcGKi4u74GOioqI0ZcoU7d+/XzabTStXrtSiRYsUGxt71deUpMmTJ8vX19dxCw8Pv8ZXh9Kw4dAp/XbgpJzMJj1BlwYAAABwzYK984oaiXRqAAAAoIwyfFB4UbzzzjuqV6+eGjZsKBcXFw0fPlzDhg2T2XxtL2P06NFKSUlx3I4dO1ZMiVFS7Ha73sqfpTGgbbjC/T0MTgQAAACUf0E++dtP0akBAACAMsqwokZAQIAsFovi4+MLHY+Pj1dISMgFHxMYGKjFixcrIyNDR48e1d69e+Xl5aXatWtf9TUlydXVVT4+PoVuKNvWHjiljYeT5OJk1uP/rmt0HAAAAKBCCM4vaiTQqQEAAIAyyrCihouLi1q3bq1Vq1Y5jtlsNq1atUodOnS45GPd3NxUrVo15ebmauHCherTp881XxPlh91u15s/7JMk3dmuhkJ93Q1OBAAAAFQMQd7M1AAAAEDZ5mTkk48cOVL33nuv2rRpo3bt2mnq1KnKyMjQsGHDJElDhgxRtWrVNHnyZEnShg0bFBMToxYtWigmJkYTJkyQzWbTM888c8XXRPn3874EbT2WLDdnsx69oY7RcQAAAIAKo2D7qVPpWcq12uRkKVc7FgMAAKASMLSoMXDgQCUmJmrcuHGKi4tTixYttHz5cseg7+jo6ELzMjIzMzVmzBgdOnRIXl5e6tWrlz799FP5+fld8TVRvtntdr31Q94sjXs7RDjeSQYAAADg2lX1dJXZJNns0qmMbAX78PM2AAAAyhaT3W63Gx2irElNTZWvr69SUlKYr1HGLN8Zq4c/2yJPF4t+ffbf8vd0MToSAABApcfPzxdWXr8u7V/5UfGpWVoyvKOaV/czOg4AAAAqiSv9+ZleYpQbNptdb6/cL0ka1rEWBQ0AAACgBBR0ZyQwVwMAAABlEEUNlBvf7YjVvvg0ebs56cHOtY2OAwAAAFRIQd55czViUzMNTgIAAACcj6IGyoVcq01Tf8ybpfFg59ry9XA2OBEAAABQMTUI8ZYk/X7wlMFJAAAAgPNR1EC5sHjrCR1KzJCfh7OGdYwwOg4AAABQYUU1CZEk/bwvQZk5VoPTAAAAAIVR1ECZl2O1adqqvFka/9eljrzd6NIAAAAASkqzar6q5ueuM9lW/fJXotFxAAAAgEIoaqDM+2rzcUUnnVGAl4vuva6m0XEAAACACs1kMqlH07xujWU7Yg1OAwAAABRGUQNlWlauVdPzuzQe6VpXHi5OBicCAAAAKr5ezfKKGqv2JCgrly2oAAAAUHZQ1ECZ9sWm4zqRkqlgH1fd1b6G0XEAAACASqFleBUF+7gqLStX6w4wMBwAAABlB0UNlFl2u12frT8qKW+WhpuzxeBEAAAAQOVgNpscA8OX7WQLKgAAAJQdFDVQZm2JTta++DS5OZvVr3V1o+MAAAAAlUrBXI0fdscrx2ozOA0AAACQh6IGyqzPN0ZLkm5pHiZfd2eD0wAAAACVS7sIf1X1dFHymRxtOJRkdBwAAABAEkUNlFEpZ3P03fYTkqTB7ZilAQAAAJQ2J4tZNzUJlsQWVAAAACg7KGqgTPpma4wyc2xqEOytVjX8jI4DAAAAVEo9moZKklbsipfVZjc4DQAAAEBRA2WQ3W7XvA15W08Nbhcuk8lkcCIAAACgcupQu6p83Jx0Mj1Lm46wBRUAAACMR1EDZc6fx5K1Ny5Nrk5m9W3JgHAAAADAKC5OZt3YOG9g+LKdcQanAQAAAChqoAz6PL9L4+bmofL1YEA4AAAAYKSeTfOKGit2xcnGFlQAAAAwGEUNlCmpmTn6Nn9A+F3tGRAOAAAAGK1TvQB5ulgUm5KpbceTjY4DAACASo6iBsqUb/7MGxBeP9hLrWpUMToOAAAAUOm5OVv070bBktiCCgAAAMajqIEyw263a65jQHgNBoQDAAAAZUTBFlTLdsbKbmcLKgAAABiHogbKjG3HU84ZEF7N6DgAAAAA8nVtECg3Z7OOJZ3VrhOpRscBAABAJUZRA2WGY0B4s1D5ebgYnAYAAABAAQ8XJ3WtHyRJWs4WVAAAADAQRQ2UCWmZOVqyLW9A+GAGhAMAAABlTs9meVtQfc8WVAAAADAQRQ2UCd9sPaGzOVbVDfJSm5oMCAcAAADKmn83DJKLxaxDiRnan5BudBwAAABUUhQ1YDi73a55DAgHAAAAyjRvN2d1rhcgSVq2gy2oAAAAYAyKGjDc9uMp2h2bKhcns/q1YkA4AAAAUFb1aJq3BdWynbEGJwEAAEBlRVEDhvt8IwPCAQAAgPLgxsbBcjKbtDcuTYdPZhgdBwAAAJUQRQ0YqtCA8HYMCAcAAADKMj8PF3WoU1US3RoAAAAwBkUNGGrJthM6k21VnUBPtY1gQDgAAABQ1vVsGipJWr6TuRoAAAAofRQ1YKiCracYEA4AAACUDzc1CZbZlDcb7/jpM0bHAQAAQCVDUQOG2XE8RTtjUuViMatfq+pGxwEAAABwBQK8XNU2wl8S3RoAAAAofRQ1YJh5+V0aPZuFqIonA8IBAACA8qJn0xBJ0jKKGgAAAChlFDVgiPSsXC3ZGiOJAeEAAABAedMjf67G5qOnFZ+aaXAaAAAAVCYUNWCIb7edUEa2VbUDPNW+lr/RcQAAAAAUQYivm1rV8JMkrdhFtwYAAABKD0UNGIIB4QAAAED51jO/W2PZDooaAAAAKD0UNVDqdsakaPvxlLwB4a0ZEA4AAACURz3y52psOHxKp9KzDE4DAACAyoKiBkpdwYDwHk1D5M+AcAAAAKBcCvf3UNNqPrLZpR92xxsdBwAAAJUERQ2UqoysXH3zJwPCAQAAgIrAsQXVTragAgAAQOmgqIFSVTAgvFaAp/5VmwHhAAAAQHnWM38LqnUHTirlTI7BaQAAAFAZUNRAqfp7QHg4A8IBAACAcq52oJcaBHsr12bXj3vYggoAAAAlj6IGSs3OmBRtO54iZ4tJ/VoxIBwAAACoCAoGhi/bGWtwEgAAAFQGFDVQaub/kdelEdUkRFW9XA1OAwAAAKA49GqWN1djzf6TSs/KNTgNAAAAKjqKGigVmTlWffPnCUkMCAcAAAAqkvrBXqod4KnsXJt+2ptgdBwAAABUcBQ1UCpW7o5XWlauqvm5q0PtqkbHAQAAAFBMTCbT31tQ7WALKgAAAJQsihooFYu2HJck3d6qmsxmBoQDAAAAFcnNzfO2oFq1J0EpZ3IMTgMAAICKjKIGSlxCWqbW7D8pSbqdAeEAAABAhdM41EcNQ7yVbbVpybYYo+MAAACgAqOogRK3ZOsJWW12tarhp1oBnkbHAQAAAFDMTCaT7mgTLkn6cvNxg9MAAACgIqOogRK3cEveO7Xo0gAAAAAqrttahMnJbNL24ynaF5dmdBwAAABUUBQ1UKJ2n0jVnthUuVjMuiV/n10AAADAaFarVWPHjlWtWrXk7u6uOnXqaNKkSbLb7Y5z7Ha7xo0bp9DQULm7u6t79+7av3+/ganLtqperurWKEiS9OWmYwanAQAAQEVFUQMl6us/81rPuzUKkp+Hi8FpAAAAgDyvvfaaZsyYoXfffVd79uzRa6+9ptdff13Tp093nPP6669r2rRpmjlzpjZs2CBPT09FRUUpMzPTwORl2x2t87agWrw1RjlWm8FpAAAAUBFR1ECJybXatHjrCUlsPQUAAICyZd26derTp49uvvlmRUREqH///rrpppu0ceNGSXldGlOnTtWYMWPUp08fNW/eXJ988olOnDihxYsXGxu+DOvaIFABXq46mZ6tn/cmGB0HAAAAFRBFDZSY3w6cVGJalvw9XXR9/UCj4wAAAAAO1113nVatWqW//vpLkrRt2zb99ttv6tmzpyTp8OHDiouLU/fu3R2P8fX1Vfv27bV+/XpDMpcHThazbm9VTRIDwwEAAFAynIwOgIprUf6A8Fsjw+TiRP0MAAAAZcdzzz2n1NRUNWzYUBaLRVarVS+//LLuuusuSVJcXJwkKTg4uNDjgoODHff9U1ZWlrKyshyfp6amllD6su2O1tX1wZpD+nlvgk6mZynAy9XoSAAAAKhADF9pfu+99xQRESE3Nze1b9/e0e59MVOnTlWDBg3k7u6u8PBwPfnkk4X2tJ0wYYJMJlOhW8OGDUv6ZeAfUjNztGJX3i97/dh6CgAAAGXMF198oblz52revHnasmWLPv74Y7355pv6+OOPr/qakydPlq+vr+MWHh5ejInLj3rB3ooM91Ouza7Ff8YYHQcAAAAVjKFFjQULFmjkyJEaP368tmzZosjISEVFRSkh4cJ7r86bN0/PPfecxo8frz179uijjz7SggUL9Pzzzxc6r0mTJoqNjXXcfvvtt9J4OTjHsh2xysq1qV6Ql5pW8zE6DgAAAFDI008/reeee06DBg1Ss2bNdM899+jJJ5/U5MmTJUkhISGSpPj4+EKPi4+Pd9z3T6NHj1ZKSorjduzYsZJ9EWXYHa3z3tj05abjstvtBqcBAABARWJoUWPKlCl68MEHNWzYMDVu3FgzZ86Uh4eHZs2adcHz161bp44dO+rOO+9URESEbrrpJg0ePPi87g4nJyeFhIQ4bgEBAaXxcnCOhflbT93eqrpMJpPBaQAAAIDCzpw5I7O58K9DFotFNptNklSrVi2FhIRo1apVjvtTU1O1YcMGdejQ4YLXdHV1lY+PT6FbZdU7MkyuTmbti0/TjpgUo+MAAACgAjGsqJGdna3NmzcXGrxnNpvVvXv3iw7eu+6667R582ZHEePQoUP6/vvv1atXr0Ln7d+/X2FhYapdu7buuusuRUdHl9wLwXmOJZ3RxsNJMpmk21qGGR0HAAAAOE/v3r318ssva+nSpTpy5Ii+/vprTZkyRX379pUkmUwmjRgxQi+99JKWLFmiHTt2aMiQIQoLC9Ntt91mbPhywNfdWVFN8jpavtzEwHAAAAAUH8MGhZ88eVJWq/WCg/f27t17wcfceeedOnnypDp16iS73a7c3Fw9/PDDhbafat++vebMmaMGDRooNjZWL774ojp37qydO3fK29v7gtdloF/x+jp/39yOdQIU6utucBoAAADgfNOnT9fYsWP16KOPKiEhQWFhYfq///s/jRs3znHOM888o4yMDD300ENKTk5Wp06dtHz5crm5uRmYvPy4o011Ldl2Qt9sjdELNzeSm7PF6EgAAACoAAwfFF4Uq1ev1iuvvKL3339fW7Zs0aJFi7R06VJNmjTJcU7Pnj11xx13qHnz5oqKitL333+v5ORkffHFFxe9LgP9io/dbteiLXnvxOrXuprBaQAAAIAL8/b21tSpU3X06FGdPXtWBw8e1EsvvSQXFxfHOSaTSRMnTlRcXJwyMzP1448/qn79+gamLl+uqxOgMF83pWbmauXu+Ms/AAAAALgChhU1AgICZLFYijR4b+zYsbrnnnv0wAMPqFmzZurbt69eeeUVTZ482bH37T/5+fmpfv36OnDgwEWzMNCv+GyJTtaRU2fk4WJxtJsDAAAAqHwsZpP6FQwM38wWVAAAACgehhU1XFxc1Lp160KD92w2m1atWnXRwXsXG+Yn5XUIXEh6eroOHjyo0NDQi2ZhoF/xWZjfpdGzaag8XAzb3QwAAABAGdA/v6jx6/5ExaacNTgNAAAAKgJDt58aOXKk/ve//+njjz/Wnj179MgjjygjI0PDhg2TJA0ZMkSjR492nN+7d2/NmDFD8+fP1+HDh7Vy5UqNHTtWvXv3dhQ3Ro0apV9++UVHjhzRunXr1LdvX1ksFg0ePNiQ11iZZOZY9d22E5Kkfq3YegoAAACo7GpW9VS7Wv6y26VFW2KMjgMAAIAKwNC30g8cOFCJiYkaN26c4uLi1KJFCy1fvtwxPDw6OrpQZ8aYMWNkMpk0ZswYxcTEKDAwUL1799bLL7/sOOf48eMaPHiwTp06pcDAQHXq1Em///67AgMDS/31VTY/7U1Qamauwnzd9K/aVY2OAwAAAKAM6N+6ujYeTtKXm47p0a51ZDKZjI4EAACAcsxkv9i+TZVYamqqfH19lZKSwlZURfDAx3/oxz0JerRrHT3To6HRcQAAAFBK+Pn5wvi65MnIylXbl3/UmWyrvny4g9pG+BsdCQAAAGXQlf78bOj2U6g4TqZnafW+REnS7a2qG5wGAAAAQFnh6eqkXs3yZhx+uemYwWkAAABQ3lHUQLH4dtsJ5drsigz3U90gL6PjAAAAAChD7sgfGL50e6zOZOcanAYAAADlGUUNFIuCoX8MCAcAAADwT+1q+atmVQ9lZFv1/Y44o+MAAACgHKOogWv2V3yadsSkyNli0i3Nw4yOAwAAAKCMMZlM6p+/TS1bUAEAAOBaUNTANVu45bgk6YYGQfL3dDE4DQAAAICyqF/r6jKZpA2HkxR96ozRcQAAAFBOFbmoERERoYkTJyo6Orok8qCcsdrsWvxn3tZTDAgHAAAAcDFhfu7qVDdAkvTVZro1AAAAcHWKXNQYMWKEFi1apNq1a+vGG2/U/PnzlZWVVRLZUA6sO3hS8alZ8vNw1r8bBhkdBwAAAEAZ1j9/YPjCLTGy2ewGpwEAAEB5dFVFja1bt2rjxo1q1KiRHn/8cYWGhmr48OHasmVLSWREGVYwIPzWyDC5OLGbGQAAAICLi2oSIm83J8Ukn9W6g6eMjgMAAIBy6KpXoVu1aqVp06bpxIkTGj9+vD788EO1bdtWLVq00KxZs2S3866bii49K1fLd8ZJYuspAAAAAJfn5mzRrZFhkqQv2YIKAAAAV+Gqixo5OTn64osvdOutt+qpp55SmzZt9OGHH6pfv356/vnndddddxVnTpRBy3fG6WyOVbUDPRVZ3dfoOAAAAADKgTvahEvK+30iNTPH4DQAAAAob5yK+oAtW7Zo9uzZ+vzzz2U2mzVkyBC9/fbbatiwoeOcvn37qm3btsUaFGXPws3HJUn9WlWXyWQyOA0AAACA8iCyuq/qBXlpf0K6vtsWqzvb1zA6EgAAAMqRIndqtG3bVvv379eMGTMUExOjN998s1BBQ5Jq1aqlQYMGFVtIlD3HT5/R+kOnZDJJt7WsZnQcAAAAAOWEyWTSHW3ytq9lCyoAAAAUVZE7NQ4dOqSaNWte8hxPT0/Nnj37qkOh7Ptm6wlJUofaVVXNz93gNAAAAADKk9taVtNry/fpz+hkHUhIU90gb6MjAQAAoJwocqdGQkKCNmzYcN7xDRs2aNOmTcUSCmWb3W7Xwi15W08xIBwAAABAUQV5u+mGBoGSpC/zt7UFAAAArkSRixqPPfaYjh07v0U4JiZGjz32WLGEQtm27XiKDiVmyN3Zoh5NQ4yOAwAAAKAc6t86b2D4oi0xyrXaDE4DAACA8qLIRY3du3erVatW5x1v2bKldu/eXSyhULZ9szVGknRTk2B5uRZ5BzMAAAAA0L8bBqmKh7MS07K0/tApo+MAAACgnChyUcPV1VXx8fHnHY+NjZWTEwvcFZ3VZtd322MlSX1ahBmcBgAAAEB55eJkdnR+f78j1uA0AAAAKC+KXNS46aabNHr0aKWkpDiOJScn6/nnn9eNN95YrOFQ9vx+6JQS07JUxcNZnesFGh0HAAAAQDnWq1moJGnFrni2oAIAAMAVKXJrxZtvvqkuXbqoZs2aatmypSRp69atCg4O1qefflrsAVG2FGw91atZqJwtRa6JAQAAAIBDh9pVVcXDWUkZ2fr9UJI61QswOhIAAADKuCKvSlerVk3bt2/X66+/rsaNG6t169Z65513tGPHDoWHh5dERpQRmTlWLdsZJ0m6NZKtpwAAAABcGyeLWVFN8ragWsoWVAAAALgCVzUEw9PTUw899FBxZ0EZt3pfotIycxXq66a2Ef5GxwEAAABQAfRqFqr5fxzTil1xmtSniZzoCAcAAMAlXPVk7927dys6OlrZ2dmFjt96663XHApl07fbTkjK69Iwm00GpwEAAABQEXSoU1V++VtQbTicpI512YIKAAAAF1fkosahQ4fUt29f7dixQyaTSXa7XZJkMuUtclut1uJNiDIhLTNHP+6JlyTd2oKtpwAAAAAUD2eLWVGNQ7Rg0zEt3RFLUQMAAACXVOS+3v/85z+qVauWEhIS5OHhoV27dmnNmjVq06aNVq9eXQIRURb8sCteWbk21Qn0VONQH6PjAAAAoII6duyYjh8/7vh848aNGjFihD744AMDU6Gk9WoeKklasTNOuVabwWkAAABQlhW5qLF+/XpNnDhRAQEBMpvNMpvN6tSpkyZPnqwnnniiJDKiDPgmf+upPi2qObpyAAAAgOJ255136ueff5YkxcXF6cYbb9TGjRv1wgsvaOLEiQanQ0m5Ln8LqlMZ2dp4OMnoOAAAACjDilzUsFqt8vb2liQFBAToxIm8xe6aNWtq3759xZsOZcLJ9CytPXBSUt48DQAAAKCk7Ny5U+3atZMkffHFF2ratKnWrVunuXPnas6cOcaGQ4lxtph1U+NgSdLSHbEGpwEAAEBZVuSiRtOmTbVt2zZJUvv27fX6669r7dq1mjhxomrXrl3sAWG873fEymqzKzLcTxEBnkbHAQAAQAWWk5MjV1dXSdKPP/6oW2+9VZLUsGFDxcay2F2R9WqWvwXVrjhZbXaD0wAAAKCsKnJRY8yYMbLZ8vY4nThxog4fPqzOnTvr+++/17Rp04o9IIz3zda8bhy6NAAAAFDSmjRpopkzZ+rXX3/VypUr1aNHD0nSiRMnVLVqVYPToSR1rBsgX3dnnUzP1obDp4yOAwAAgDLKqagPiIqKcnxct25d7d27V0lJSapSpQqzFiqgY0lntPnoaZlMUu/84X0AAABASXnttdfUt29fvfHGG7r33nsVGRkpSVqyZIljWypUTAVbUH25+bi+3xGr6+oEGB0JAAAAZVCROjVycnLk5OSknTt3Fjru7+9PQaOCWpI/IPy6OlUV5ONmcBoAAABUdF27dtXJkyd18uRJzZo1y3H8oYce0syZMw1MhtLQK/+NVMt3xrMFFQAAAC6oSEUNZ2dn1ahRQ1artaTyoIz5Nr+o0SeymsFJAAAAUBmcPXtWWVlZqlKliiTp6NGjmjp1qvbt26egoCCD06GkdawTIB83J51Mz9LGw0lGxwEAAEAZVOSZGi+88IKef/55JSXxA2ZFtzcuVXvj0uRiMSuqaYjRcQAAAFAJ9OnTR5988okkKTk5We3bt9dbb72l2267TTNmzDA4HUqai5NZNzXJ+93j+x0MhgcAAMD5ilzUePfdd7VmzRqFhYWpQYMGatWqVaEbKo4l+QPCuzYIlK+7s8FpAAAAUBls2bJFnTt3liR99dVXCg4O1tGjR/XJJ59o2rRpBqdDabi5Wd4WVMt2xrEFFQAAAM5T5EHht912WwnEQFljt9v1TX5Ro08Ltp4CAABA6Thz5oy8vb0lST/88INuv/12mc1m/etf/9LRo0cNTofS0LHu31tQ/XEkSf+qXdXoSAAAAChDilzUGD9+fEnkQBmzJfq0YpLPytPFom6N2LsYAAAApaNu3bpavHix+vbtqxUrVujJJ5+UJCUkJMjHx8fgdCgNLk5m3dg4RAu3HNf3O2IpagAAAKCQIm8/hcqhYOupqCYhcnO2GJwGAAAAlcW4ceM0atQoRUREqF27durQoYOkvK6Nli1bGpwOpeXm5nlzNdiCCgAAAP9U5E4Ns9ksk8l00futVus1BYLxcq02fbc9byjfrS3CDE4DAACAyqR///7q1KmTYmNjFRkZ6TjerVs39e3b18BkKE2d6gbK281JiWlZ2nQkSe3p1gAAAEC+Ihc1vv7660Kf5+Tk6M8//9THH3+sF198sdiCwThrD57SqYxsVfV0Uce6AUbHAQAAQCUTEhKikJAQHT9+XJJUvXp1tWvXzuBUKE15W1AFa9GWGH2/I5aiBgAAAByKXNTo06fPecf69++vJk2aaMGCBbr//vuLJRiM883WGElSr2ahcrawQxkAAABKj81m00svvaS33npL6enpkiRvb2899dRTeuGFF2Q28/NpZXFzs1At2hKjZTvjNL53E5nNF98xAAAAAJVHkYsaF/Ovf/1LDz30UHFdDgbJzLHqh13xkqQ+bD0FAACAUvbCCy/oo48+0quvvqqOHTtKkn777TdNmDBBmZmZevnllw1OiNLSqV6AvF2dlJCWpU1HT6tdLX+jIwEAAKAMKJaixtmzZzVt2jRVq1atOC4HA/20N0HpWbmq5ueuVjWqGB0HAAAAlczHH3+sDz/8ULfeeqvjWPPmzVWtWjU9+uijFDUqEVcnS94WVH/mbUFFUQMAAADSVRQ1qlSpUmhQuN1uV1pamjw8PPTZZ58VaziUvoKtp25tEUZ7NwAAAEpdUlKSGjZseN7xhg0bKikpyYBEMFKvZqFa9GeMlu2M1bhbGvM7CgAAAIpe1Hj77bcLFTXMZrMCAwPVvn17VanCO/vLs5SzOfp5b6Ik6dZItp4CAABA6YuMjNS7776radOmFTr+7rvvqnnz5galglE618/bgio+NUubo0+rbQTdGgAAAJVdkYsaQ4cOLYEYKAtW7IxTttWm+sFeahjibXQcAAAAVEKvv/66br75Zv3444/q0KGDJGn9+vU6duyYvv/+e4PTobS5OlnUvXGwvv4zRku3x1LUAAAAgMxFfcDs2bP15Zdfnnf8yy+/1Mcff1wsoWCMJdtOSJL6tKhWqBsHAAAAKC3XX3+9/vrrL/Xt21fJyclKTk7W7bffrl27dunTTz81Oh4M0KtZqCRp2c5Y2Wx2g9MAAADAaEUuakyePFkBAQHnHQ8KCtIrr7xSLKFQ+hJSM7Xu4ElJbD0FAAAAY4WFhenll1/WwoULtXDhQr300ks6ffq0PvroI6OjwQCd6wXIK38Lqi3Rp42OAwAAAIMVuagRHR2tWrVqnXe8Zs2aio6OLpZQKH3fbY+VzS61rOGncH8Po+MAAAAAgCTJzdmi7o2CJElLd8QanAYAAABGK3JRIygoSNu3bz/v+LZt21S1atViCYXS903B1lN0aQAAAAAoYxxbUO2IYwsqAACASq7IRY3BgwfriSee0M8//yyr1Sqr1aqffvpJ//nPfzRo0KCSyIgSdvRUhrYdS5bZJN3cnKIGAAAAgLKlS/1Aebk6KS41U38eYwsqAACAysypqA+YNGmSjhw5om7dusnJKe/hNptNQ4YMYaZGObVka16XRse6AQr0djU4DQAAACqj22+//ZL3Jycnl04QlEluzhZ1axSkb7ae0NLtcWpd09/oSAAAADBIkYsaLi4uWrBggV566SVt3bpV7u7uatasmWrWrFkS+VDC7Ha7Fm+NkcSAcAAAABjH19f3svcPGTKklNKgLOrVLFTfbD2hZTtjNebmRjKbTUZHAgAAgAGKXNQoUK9ePdWrV684s8AAu2NTdTAxQy5OZkU1DTE6DgAAACqp2bNnGx0BZdz19QPl6WJRbEqm/jyWrNY1qxgdCQAAAAYo8kyNfv366bXXXjvv+Ouvv6477rijWEKh9BRsPdWtYZB83JwNTgMAAAAAF5a3BVWwJOn7HbEGpwEAAIBRilzUWLNmjXr16nXe8Z49e2rNmjVFDvDee+8pIiJCbm5uat++vTZu3HjJ86dOnaoGDRrI3d1d4eHhevLJJ5WZmXlN16ysbDa7vskvavRpwdZTAAAAAMq2Xs1CJUnLdsTKZrMbnAYAAABGKHJRIz09XS4uLucdd3Z2VmpqapGutWDBAo0cOVLjx4/Xli1bFBkZqaioKCUkJFzw/Hnz5um5557T+PHjtWfPHn300UdasGCBnn/++au+ZmX2++FTikvNlLebk7o2CDI6DgAAAFCqYmJidPfdd6tq1aqOWYGbNm1y3G+32zVu3DiFhobK3d1d3bt31/79+w1MjK4N8ragOpGSqa3Hk42OAwAAAAMUuajRrFkzLViw4Lzj8+fPV+PGjYt0rSlTpujBBx/UsGHD1LhxY82cOVMeHh6aNWvWBc9ft26dOnbsqDvvvFMRERG66aabNHjw4EKdGEW9ZmX2zZ95XRo3NwuVm7PF4DQAAABA6Tl9+rQ6duwoZ2dnLVu2TLt379Zbb72lKlX+ntPw+uuva9q0aZo5c6Y2bNggT09PRUVFndcpjtJz7hZUCzcfNzgNAAAAjFDkQeFjx47V7bffroMHD+rf//63JGnVqlWaN2+evvrqqyu+TnZ2tjZv3qzRo0c7jpnNZnXv3l3r16+/4GOuu+46ffbZZ9q4caPatWunQ4cO6fvvv9c999xz1deUpKysLGVlZTk+L2rHSXmUmWN17EPbp0U1g9MAAAAApeu1115TeHh4oQHltWrVcnxst9s1depUjRkzRn369JEkffLJJwoODtbixYs1aNCgUs+MPIPahWvJthNatCVGz0Q1lK8HswEBAAAqkyJ3avTu3VuLFy/WgQMH9Oijj+qpp55STEyMfvrpJ9WtW/eKr3Py5ElZrVYFBwcXOh4cHKy4uLgLPubOO+/UxIkT1alTJzk7O6tOnTrq2rWrY/upq7mmJE2ePFm+vr6OW3h4+BW/jvLq570JSsvKVaivm9rX8jc6DgAAAFCqlixZojZt2uiOO+5QUFCQWrZsqf/973+O+w8fPqy4uDh1797dcczX11ft27e/6BumsrKylJqaWuiG4tehdlU1DPHW2Ryr5v8RbXQcAAAAlLIiFzUk6eabb9batWuVkZGhQ4cOacCAARo1apQiIyOLO18hq1ev1iuvvKL3339fW7Zs0aJFi7R06VJNmjTpmq47evRopaSkOG7Hjh0rpsRl19d/xkiSbm0RJrPZZHAaAAAAoHQdOnRIM2bMUL169bRixQo98sgjeuKJJ/Txxx9LkuNNUUV5w1RlfLOUEUwmk+7rmNdV8/G6I8q12gxOBAAAgNJ0VUUNSVqzZo3uvfdehYWF6a233tK///1v/f7771f8+ICAAFksFsXHxxc6Hh8fr5CQkAs+ZuzYsbrnnnv0wAMPqFmzZurbt69eeeUVTZ48WTab7aquKUmurq7y8fEpdKvIUs7kaPW+RElS35ZsPQUAAIDKx2azqVWrVnrllVfUsmVLPfTQQ3rwwQc1c+bMq75mZXyzlFFubREmf08XnUjJ1A+74y//AAAAAFQYRSpqxMXF6dVXX1W9evV0xx13yMfHR1lZWVq8eLFeffVVtW3b9oqv5eLiotatW2vVqlWOYzabTatWrVKHDh0u+JgzZ87IbC4c2WLJG3Btt9uv6pqV0fc7Y5VttalhiLcahlTsAg4AAABwIaGhoWrcuHGhY40aNVJ0dN52RgVviirKG6Yq25uljOTmbNFd7WtIkmb9dtjgNAAAAChNV1zU6N27txo0aKDt27dr6tSpOnHihKZPn35NTz5y5Ej973//08cff6w9e/bokUceUUZGhoYNGyZJGjJkSKGh371799aMGTM0f/58HT58WCtXrtTYsWPVu3dvR3HjctfE31tP3UaXBgAAACqpjh07at++fYWO/fXXX6pZs6akvKHhISEhhd4wlZqaqg0bNvCGqTLi7n/VlLPFpE1HT2v78WSj4wAAAKCUOF3picuWLdMTTzyhRx55RPXq1SuWJx84cKASExM1btw4xcXFqUWLFlq+fLlj39ro6OhCnRljxoyRyWTSmDFjFBMTo8DAQPXu3Vsvv/zyFV+zsotJPquNh5NkMkm3RoYZHQcAAAAwxJNPPqnrrrtOr7zyigYMGKCNGzfqgw8+0AcffCApb27DiBEj9NJLL6levXqqVauWxo4dq7CwMN12223GhockKdjHTTc3C9XirSc0e+0RvT2whdGRAAAAUApMdrvdfiUn/v777/roo4+0YMECNWrUSPfcc48GDRqk0NBQbdu27bzW7fIsNTVVvr6+SklJqXAt4++vPqDXl+/Tv2r7a/5DvMMMAAAA1668/vz83XffafTo0dq/f79q1aqlkSNH6sEHH3Tcb7fbNX78eH3wwQdKTk5Wp06d9P7776t+/fpXdP3y+nUpT7YfT9at766Vs8Wktc/+W0E+bkZHAgAAwFW60p+fr7ioUeD/2bvv8Kjq7I/j75lJ741UQi+hd5AioiLFrliwi90fuq641t21rK64uioWrIu9YW80FQWl9957TQjpvc7vj28KkQAJmeRmwuf1PPPMnTt37j0zpHHPPefk5OQwdepU3nnnHZYsWUJJSQkvvPACN910E4GBgXUOvDFoqv/5cDqdjJz0O1uSsnnm0m6M7d/C6pBEREREpAloqn8/15U+l4Yx5vUFLN+dxl/OaseEER2tDkdERERETlJN/36u1aBwAH9/f2666SbmzZvH2rVrue+++3jmmWeIjIzkwgsvrFPQUr82HsxiS1I2Xg47o7vFWB2OiIiIiIhInd00uDUAHy/eQ35RicXRiIiIiEh9q3VS40gdO3bk2WefZd++fXz66aeuiknqyXerzIDwsxIiCfb1tDgaERERERGRuhvZJYrYYB9Scgr5fvUBq8MRERERkXpWp6RGOYfDwcUXX8z333/vit1JPSgpdfLdKvMH/sW9NCBcRERERESaBg+HnesHtQLg3fm7qGWHZRERERFxMy5Jakjjt3hnComZ+QT5eDCsY6TV4YiIiIiIiLjM2H7x+Ho62Hgwk0U7Uq0OR0RERETqkZIap4hvV5rWU+d2i8HH02FxNCIiIiIiIq4T4ufFpb3jAHh3/k6LoxERERGR+qSkxikgv6iEGWsTAbi4V5zF0YiIiIiIiLjeuMGtAPh5YxJ7UnKtDUZERERE6o2SGqeAXzcdIqugmNhgH/q3CrM6HBEREREREZdrFxnI0A7NcDrh/YW7rA5HREREROqJkhqngPLWUxf2jMNut1kcjYiIiIiISP24qaxa4/Ole8kuKLY2GBERERGpF0pqNHHpuYX8tvkQABf3irU4GhERERERkfoztH0z2jTzJ6ugmC+X7bU6HBERERGpB0pqNHHT1yZSVOIkITqQhOggq8MRERERERGpN3a7jXGDWwPw3oJdlJY6LY5IRERERFxNSY0mrrz1lAaEi4iIiIjIqWBM7ziCfDzYlZJbUbUuIiIiIk2HkhpN2L60XJbsSsVmgwt7qPWUiIiIiIg0fX5eHlzVvwUA78zfaXE0IiIiIuJqSmo0Yd+vPgDAgNZhxIb4WhyNiIiIiIhIw7h+UCscdhvzt6WwOTHL6nBERERExIU8rA5A6ofT6axsPdXzT62nSoogaT3sXw77V0DaTojoAHF9oHlfs2x3WBC1iIiIiIhI3cWF+DKySxTT1yby7vydPDOmu9UhiYiIiIiLKKnRRG08mMWWpGy8HDbOjS+AtV+WJTGWw8HVUJxf9QW758Pyd82yVwDE9jJJjvJbsGZyiIiIiIiI+7hpcGumr03km5X7eWBUAmH+XlaHJCIiIiIuoKRGU5OTAgdWcHjOTN71XEo/z50EvJl59HY+wZUJi7C2kLwR9i2HAyuhMBt2/WFu5QJjyrbvbe5je4NPUMO9LxERERERkVro0zKUbnHBrN2fwadL9jD+zHZWhyQiIiIiLqCkRlPhdMIXN8CG7wAYCuAASgGHF0R3g7i+lYmM8LZgsx29n9ISSN4M+5dVVnYkbYCsg7DpR3MDwAadL4TzJ4FfWIO8RRERERERkZqy2WzcNKQV905dzQcLd3Hb0DZ4OjRWUkRERMTdKanRVOxbWpHQyAtszfT0ODY7OvC3m67GK7Y7eHjXbD92B0R1Nrfe15t1hTlwcE3VREf6HnO8/Svgsncgvn89vTEREREREZGTc163WJ6evomkzAKmrz3IRX+eNygiIiIibkeXqTQVi98w9z2v5bGW73Nf0f+R1X0cXi361TyhcSxe/tByIAy6Gy5/D/66Fm6bA2FtIGMvvDMK5r0IpaV1fRciIiIiIiIu4+Vh57rTWgLwzvxd1gYjIiIiIi6hpEZTkHmwokqjoM8tzFibCFC/VyHF9oLb5kLXMeAsgV8eh48vg+zk+jumiIiIiIhILV09oAVeDjur96azYk+a1eGIiIiISB0pqdEULHsHSouhxSBmp0eTVVBMbLAP/VvV86wLnyAYMwUueBk8fGD7bHhjCOyaV7/HFRERERERqaGIAG8u6hkLwDvzdlocjYiIiIjUlZIa7q64wCQ1AAbcxjcr9wNwYc847PZqBoG7ms0GfW6AW3+DiI6QnQjvXwBz/mOGjouINGaHt0L2IaujEBERkXo2bnBrAGauS+RQVr7F0YiIiIhIXSip4e7WfQ25hyEojvQWI5iz2Zycu6RXAw/Ai+oMt/0GPa8FZynMeRo+uAiyEhs2DhGRmkrdCa8Pglf7wf7lVkcjIiIi9ahzbBB9WoZSXOrk86V7rQ5HREREROpASQ135nRWDgjvdzPTNhymqMRJQnQgHaMDGz4eL3+4eDJc8iZ4+sOuP+D1wbBtdsPHIiJyImumQkkh5KfD+xfB7gVWRyQiIiL16JoBLQD4dMleSkqdFkcjIiIiIidLSQ13tm8pHFwFDm/ofSPfrTwAwMUNXaXxZz3Gwm1zIKqrqSL5aAzM/heUFFsbl4gYSRtgxxyro7CW0wmrPzPLgbFQmAUfXgrbf7U2LhGpueQtsP5b8/0sIlID53aLIdTPk/3pefy2Se0nRURERNyVkhrurLxKo9vlOP3CWLU3HYARnaOsi6lcsw5wyy/Q9ybACX88D++dBxn7rI5M5NS2/Td46wzTHm7T9Po/XmM92bhvKaTtNFVld8yDdudAcR58cmXDfC4iUjdZifDuKPjiBlj1idXRiIib8PF0cHnfeAA+Wrzb4mhERERE5GQpqeGuMg/Chu/M8oDbyCksobCkFICYYF8LAzuCpy+c/yJc9i54B8HeRfDWMMhOtjoykVPT3iXw2dWm5RLA9L9BQVb9HS91B7zUAz66DPIz6u84J6O8SqPTBeAfDmM/NsslhTD1Wlj7pbXxicixlZbCN7dDbop5/Mtjje9njIg0Wlf3Ny2o5m5JZm9qrsXRiIiIiMjJUFLDXS17B0qLocVAiOlBWo45SenjacfXy2FxcH/S9VK4fS6Et4OcZFjxvtURiZx6EtfCx5dBUS60ORNCW0Hmfvj13/VzvNJS+Pb/IH03bPvZVIbkptbPsWqruBDWf22We1xp7j284bL3oPuV4CyBr26BFR9aFqKISxUXmorJTdOsjsQ1FrxsWuh5+kFIS/O3xZz/WB2ViLiJVhH+nN4+AqcTPlmyx+pwREREROQkKKnhjooLYPm7ZnnA7QCkliU1Qv28rIrq+MLawOl/M8vL34PSEkvDETmlHN4GH15irmSOP81UJZz3gnluyZuwf4Xrj7n4DdizELwCwC8cDqw0Leiyklx/rNra9jPkpUFANLQ+o3K9wwMufgP6jAOc8P1dsPgty8IEICcF8jOtjUHc36yHzWyrz66G2U+apKO72r8cfn3SLI/+D5x/xM+yQ5usi0tE3Mo1A1oC8PnSvRQU6/8lIiIiIu5GSQ13tP4bc1ViUBwknA9AWm4jT2oAdLkEfEMhYy9s/dnqaERODel7TZVETjJEd4Orp4KXP7Q7G7pdDs5S+OEeKCl23TEPb4XZT5jlEU/CjdNNAuHQBnjvXOtn65S3nup2Gdj/VNlmt5u2eaeNN49n3A/zXmzY+ModWGXad715urnSXuRkrPoUlv6v8vEf/4Uvx0FRnnUxnayCLPjyZlOp2uUS6HUdtBsOHc8z62Y+2Hjn+IhIozK8UyTRQT6k5BQyc12i1eGIiIiISC0pqeFunE5Y9LpZ7nsTODyByqRGmH8jTmp4+kDPa8zysnesjUXkVJCVBB9cCJn7IKIDXPct+IZUPj/yafAJhsQ1prLCFUpL4Ns7oTjftLnqMw4iE2DcdAiOh5Rt8M5oM2/DCnlpsGWmWe4xtvptbDYY+W8Y+oB5/Mvj8OtTDXuyNGOfGVpemAVpu2DTDw137MYo+xB8fAV8NAa+uwt+exqWvQtbfjKt1XJSdDK7OgdXw49/NcvDHoaLXwe7J2z4tvFUTtXGtL9B2k4IbgHnTzLfq2C+Xx3epiXVxlP8e0VEasTDYWdsfzMw/ONFakElIiIi4m48rA5AamnfUji4yvznvc+NFatTc4oACG3MSQ0wiZiFr8LWnyBtN4S2tDoikaYpN9W0nErdYU4AXvct+EdU3SYgEs55En74C/z2b+h8IYS0qNtxF75qfk55B8FFr1aedAxvC+NmmCRL6g5491y4/jto1rFux6ut9d+aYeCRXUzlyrHYbHDW38HLzyQ1fn8OCnPNydPy91RfCrJMQiM70ZyALi2Cpe9A1zH1e9zGbM5E2Drr+Ns4vCEw2lQxBsVAYAwExZqWa837NEycjUluKky9ziQY2480STq73XyPT73WtHH639mmeiuqi+uOm58BuxdA66GmKsxVVn8Gaz4DmwPG/K9qgjasNQz+i/k+nfV3U73h5ee6Y4tIkzS2Xwte+XUbS3alsjkxi47RgVaHJCIiIiI1pEoNd7P4TXPf7fIqJyjTK9pPeVoRVc2Ft4U2wwCnma0hIq5XkAUfXw6H1kNAFFz/LQTHVb9tr+ugxSAzQHza3+p2tfuhTZWDx0c+DcHNqz4fEm8SG806QdZBk9g4uObkj3cy1nxu7ssHhJ/IkHth9HNmedFkc9V7fc4jKCmGL2+CpHXgHwk3/gg2O+yed+rOC8jYVzm0/cx/wJl/NxVA7UeaxJRf2e/CkgIzmH7PAlj3lUmwzXoE3hlpfcuzhlZaAl/faj6P0FZw6ZsmoQHQagjcMhvC25l2kFNGuqYlZFE+LHjFtEz7dKzZr6s+95TtMO0+szzsYWgx4OhthkyAoOaQsccMEhcROYHoYB/O6RQFwCeLd1scjYiIiIjUhpIa7iTzoGkZATDgtipPNfpB4Ufqe7O5X/lh4+oTP+vv8PrgU+/klzQtRflmGPD+ZWaGzXXfmmTisdjtcMEkUxGwdRZs+O7kjltSDN/eYU4stx8Bva6tfrvAaLhxGsT0gNzD8P75sG/ZyR2zttLKTnhjg66X1fx1A26Diyab5MLy98repwtnkJRzOmHmQ6aSzcMXrv4MWpwGHUab50/Vtn3zXjTVKq1OhzPuhzMeMF+z13wOd8yDB7bDPw7BPWtg3Ey47B0Y8W8YeBeEtjavLZ+j0pg4nfDtePjfOa5PWM15Brb9Yr6OrvzY/Cw4UnhbuPln85kWZsEnV1ReNFFbJcWw4gN4pTf89A/T4g0bJK2Ft8+C/Svq9l6KC+GrW6AwG1oOgdMnVL+dlx+MfMosz3vRfL+LiJzANaeZCtWvV+wnp6AefreLiIiISL1QUsOdLHvHDMJsMdCcEDyCW8zUKNfxXNMWJCcZNn5vdTRG8mZzVW/SOph+v3qzS91s/w2WTik7udeASorgixth5+/gFQjXfgVRnU/8umYdK08UznjQtI+prfmT4MBKM6PjgpeO36LJPxxu+AHiB5hjfXAR7JpX+2PWVnmVRuuhx65cOZZe15qWN3YPWDMVvrzRtKNypcVvwNK3ARtc+hbElbVM6neTuV/9KRTmuPaYjV3GfnPCHOCMB4+9nYe3aWfYcqBp0zXoLtMq7IyyuSirPm58P9c3/QirPoJ9S2DKObD9V9fsd/MM+P1Zs3zhyxDdtfrt/MLg2q/N17azFGY8YH7/1TRh53SaJOhrp8H3d0PmflMpceGr8JeVpsVbdpKpyNpQh9/1vz0FB1aYxMylb4HdcextO19sEjXF+fDT30/+mCJyyhjcNoJW4X5kFRTz/eoDVocjIiIiIjWkpIa7KC6A5e+a5QG3H/V0eaVGSGNvPwXg8IDeN5jlxnLl8fyXKpc3T9egUTl56XtM66dpE+D5TvD9XyBpff0ft7TUDOjeMgM8fMxV/nG1mCMwZIJpR5OdCL88UbtjJ64zV4YDjH7WzDE4EZ9gc0K19VBzBfZHY2DrL7U7bm04naYfPxx7QPiJdB0DV3wIDi/zM+J/Z5uEqCtsngEzHzbL5zxh5puUa3OWqTgoyIS1X7rmeO5i/iQzA6XlYGh9eu1f3+lC8PQ3c1z2LnZ5eCetuBB+ftQs+4aZf9uPLjPJ0LpI2Q5fl/2N0P926H7F8bf38DJJiOGPm8dL3jKto/Izj/+67b/B22fC59dDylbzHkY+DXcvh97XmRkXN82EdudAcR58fh388ULtE0vbf638/XzhqydORtps5meQzWG+R7f/Vrvjicgpx263cc0AM+Pvo0W7cTa2BLiIiIiIVEtJDXex/htT2RAYCwnnH/V0eq4ZFO4WlRoAfW4wJx12z4dDG62NJX2vufIaTBUJmCtWT3RSR6Q65a1yHN7mZN6K9+H1QfDueWVDquupbdG0CbD2C1NJcMWHpm9+bXj6wPkvmuVl78DeJTV7XUmRacdUWmS+f7rXcFYFgHcAXP25mY1QnG9OptZXQnH/CkjZZtrxdLrg5PeTcK5JxvhHwqEN8NYwWPVJ3WI7sMrM0cAJfW6EQX+p+rzdDn3HmeVlUxpfxUF9yTwIy983y8er0jge7wDocolZXvmRa+JyheXvmkSLfzO4axl0HwvOEvN9PPMRMxOjtgpzzADwggwzHH3EUzV7nc1mZsdc8aH5/tj2s5lDkr7n6G33L4f3L4QPLzaVWZ7+5t/mntUwcLz5OVLOJwiu+swkVwBmPwHf3VXztpM5h+GbO8xy35uh09F/+1QrqjP0v9Usz3jQ/IwSETmOy/o0x8vDzvoDmazam251OCIiIiJSA0pquAOn07QlAeh3MziOrsZwq5kaYK7k7thI+sQvnGzaerU63fRiD2tjhhj/+qS1cYn7OXKg8XXfmKHYnS8uS+DNgy9ugJe6w+//hexk1xzT6TRXfC9/18x8uPRt6DDi5PbVeij0vAZwwg/31Oxk4B/PQ+Ja0xrm/EnHbztVHU9fuPIj6HyRSYx8fgOs+eJkoj++8sRlp/PBO7Bu+2p9Otw5H9oMMwPWv73TXB1fkF37fWXsN8mcolxocyac+9/qP8Oe15gKkYOr6z6jwF3Mn2RmtLQYaL42T1bPq839+m8bR/uuvPTKyqYzHzHt2C55A876h1m3aDJ8dk3tvp6cTlMVdmgDBETBFe+bKoza6HwhjJtuXn9oA7x9duW8m+QtMPU6MyNj51wzg2fAHSaZceYjJoFRHYcHnPssjH7O/Hxa9RF8dCnkpp74/Xx7p2lf1ayTaSVWG8MeNgPkD2821SciIscR6u/F+d1jAPhoUTUJXRERERFpdJTUcAf7lpkrIh3e5ireP3E6nRUzNULdpVIDTIIGzADXkzkZ6Ao5KeZKejAzBTx9K69WX/J2ww0wlqbhyIHGrQZDy0Hm5OJf18LQ+81Jtsz9JmH2YmdzFfL+5bU/jtNpZlEc2gS/PgULXjbrL3gJul5at/cw4inwCzcnNcv3eywHV8Pvz5nlc/8LgVEnd0wPLxjzDvS4ylyt/vWtlVfou0JJEaz7yizXppLkeAIi4dpv4Kx/mpO1az4zVRuJ62q+j4Is+ORKk0Rt1sl8rVSTtAbAP8IkyKD+EsEHVppB841BVqIZyg6mEqC2ybIjtRwEoa3MQOzG0Fpw3guQlwoRHaHX9WadzWZ+Rlz2rmkft2UGvDvKJL1qYvEbsO5LU6l1+fsQGH1yscX1hlt/hahukHMI3jvPJDNeG1A2A8sGPa42baZG/wcCmtVsvwNuM1VZXoGw6w/433DTKut472frT+azuOwd87u5NnxDYPhjZnnOM5B9qHavF5FTzrWnmRZUP645QHpuDSvKRERERMQySmq4g/IqjW6XmRNbf5JTWEJRiWlHEuYulRoArYdV9olfZ1Gf+MVvmCukY3qYq6TBXH3d4ypqdbW6yPEGGgfHmauwJ2yAS96E2N5mTsDqT82Vz2+fbYZYFxeYtjOZB02yY+OPsPgt+OVxUwnw/gXwSl+Y2ByeaWFONP7xX3OMkU9D7+vr/j78ynrjA8x91rTIqU5xAXxzp6ly6nShmTdRFw4PuOg16FvWhumHv8C22XXbZ7ltsyH3sGkZVf597gp2Owz9G9w4zbQGTNlq/j2XvXPiFlElxfDlzZC01rQgunqqmTNyPOWJ4HVfuX4I/dIpJinzyRUn1/rI1ea/ZFqSxQ8wP5PrwmYrq0DCDAy3UtpuWFT2O33Ek+br/khdLzVfT/7NTAXU22eZZNPx7F4AP5VVeYz4txmWXhfBzeGmGdBhlPk32Pi9GSTe8Ty4cwFc8roZyl5b7c+Bm3+C4HhI3W5m0uyad/R2B1dXzhsZ+W/TTupk9LwWYnuZvzF+efzk9iEip4xe8SF0igmioLiUL5fvszocERERETkBJTUau8yDsOFbs9z/tmo3SStrPeXjacfXy9FAgbmA3V52AhNzQq2h+8QXZFW2pRgyoeqVwCP+bQafJq0z7alETqQmA409vM2Q6tt+g1t+NVUDdk/Yv8xUJ/ynNTzZDF5IMCczp14DM+43FSBrPoOdv5sT54VllU0+weYK/9HPmX72rtL9Smh9hjmh+eOE6r835z4Lh9ab6pPzX6zblfTl7HY474XK5Mz3d5uKlLoqHxDe7bKjTyK7QstBcMc8MxukpAB+vBe+HHf8uTyzHoGts8yV6FdNrdlJ4vgBENnFzGpZ9anr4s9JMfMOwLQWmj/Jdfs+GVlJldUoda3SKNfjKsBmvofSdtd9fyfr1yfN10jrodD+GG3imveFW2ab7+3sRHhn9LErTDIPmpZtpcXQ7XIYcLtr4vQOhLGfmM8/4Xy4+We46pOTTzCUi+ps3ltcX5OY++BiWHlEoqkwxyT7SgrNcfvefPLHsttNBRmYZNbepXUKXUSaNpvNxrWntQDgk8V7NDBcREREpJFTUqOxW/6uOVnRYiDE9qx2E7ebp3GkXteatlqJaxq+T/zy9yA/HcLbHT042D+8csjqnGcgbVfDxibu5WQGGjfvA5e+Zao3zvwHBMZAUY5pv2RzQFCcOfHX6QIzaHf443DJW3DDD3DXcnjkADy0B8YvMq1dXMlmM4kKhzfs+M0MID/S/uUm0QJw/gvVVpDV6dijnjFVXJn7zcn/usjPgE3TzbKrWk9Vxz/cDEUe8ZRpAbT+G3hzaPVX2S96A5a8aZYvfct8LdSEzQb9yhLBNakGqalf/2U+J9+wssf/tvYE8IKXTUKteT9oe5Zr9hkSXzmXY/Vnrtlnbe1fXva9ZDNfJ8dL1oS2NFUN7YabJNbU62DepKr/5sWFZk5PziGT7LrgJdckgMrZHWZextiPIb6/6/YbGAU3/mgGuJcWwXf/ZyopSkvNYO+Uraby6cJX6v5+mvetrNKZcb85hojIMVzcM44Abw92HM5hwfYUq8MRERERkeNQUqMxKy6ovFr1GFUaQOU8DXdMaviFmRMbAMumNNxxiwsqKzAG32NO3vxZz6vNbITivGNfrS4CdRtoHBAJZ9xv5m7cuRAmbIJ/Jptkx62zzRDtc5+FIfdCjyvN/iPagZd/vbyVCuFtTVwAMx+uHOxblG/aTjlLTMupzhe5/the/nDxa4ANVn4EW2ad/L42fG/+bZolmDZz9cluh0F3w02zILgFpO2E/51jkhjlPz82z4RZD5vl4U/U/vPrfiV4BZgTvzt/r3vMB1ZWJuTGfmz+TZ0l8NXNrqmSqa3sQ6ZyD+CMh1x7kr7XteZ+1ccNf3Lb6YSf/mmWe4yt2deiT5Cp4ul3K+CEXx4z1UvFZb3ef/o77F0M3sFw5Yf1/zPBlTx9zRydoWU/Y+a9CFPOgZUfAjaT7PMLc82xhj8O3kHma33VR67Zp4g0Sf7eHlzSKw6AjxZZWNUnIiIiIiekpEZjtv5byEk2Vyz+uZLgCOVJjTB3GhJ+pCP7xJefOK1vqz8zw3kDY4999bbNBudPMlerb59dOWhY5EiuGmjs8DStWYJiqk+yWWHQPaYFTu5h+LnshOycp+HwZjOfory1S31oOQhO+z+z/P1fTn6GxJqp5r77Fa49QX48zfvCHb+bn9ulRTDzQfjsGtgxF768ycwn6H2DSajWlnegeS9Q90RwaSlMfwBwmtZFLQeZCp2QFpC+25pk7oKXTSI5rg+0O9u1+04435zcTt8Nexa4dt8nsnkG7J5v2o2d9Y+av87hAef9F0Y/awbSr/wQPrrUzNopb5946VsmCelu7HbzWVz8RmUbPjBzao7Vwu9kBETCsIfM8i9PQF666/YtIk3ONWUtqH7akERSZr7F0YiIiIjIsSip0Vg5nbD4dbPc72ZzwvMYUnPMIOtQd01qNO8HUd1Mu5HVLuwTfyylJWYILZg5BB7ex942op05wQIw8yHXD+cV9+fKgcaNjYcXXDDJLK/8yLS/WfCKeXzBJNddSX0sZ//TtIfLToQZD9X+9el7YdcfZrnbFa6N7UR8Q+GKD828E4cXbJ4GH1xoWoy1ORPOe/7kkyzlcwY2TTNJtZO1ZirsWwKe/nDOv8w6n2AYM8W0QFv3ZcP8TC6XnVx/VRoAXn6VlYFHznGobyVFlYOvB443g7hra8DtpmrDK8B8Tc8oq3A440HoOMp1sVqh51Vww/dmgHiHUebf3tX63wYRHU2Cds5E1+9fRJqMhOgg+rYMpaTUydSle60OR0RERESOQUmNxmrfMtMqweENfW487qbpFe2njp34aNTqq0/8sWz8HlK3g0/ICT9bAAb/1ZwMyUmGnx+r39jEvdTHQOPGpsVp0GecWf7lMVNl0H0sJJxX/8f29DVXcdvsZtj3pmm1e/3az819q9PNTIWGZrOZeSc3/wxhbcy6ZglwxfvHTVSfUHRXk0QrLYYVH5zcPvIzzb8nmMRtUGzlc/H94cyyFlnT/gaHt518rLWx8BUoyoXYXtD+nPo5Rvl8hQ3fQUF2/Rzjz5a/Z9qF+UWY3ycnq8MI09osqCwp0u6c+kkAWKHlINOC76rPTHWKqzk8YfR/zPKStyFpg+uPISJNxrWntQTg0yV7KC7RLB4RERGRxkhJjcaqfIhst8tOOITXrQeFl+t2BXgFQso22Dm3/o7jdMIfL5jlAbeDd8CJX+PhZQawAqx4H3Y3cNsSabzqY6BxYzT8cQiIMssB0TD6mYY7dnw/M6cC4Id7IKeGgzudTlhd3nqqHgeE10RsT7htLlzyJtw4zVRD1FV5tcby96CkuPav//1ZyE6CsLameuDPhkwwyaCiHPjqpso5DvUlJwWW/M8s10eVRrn4/qb6pygHNnxbP8c4Un5GZWXAsIfMnIy6iO4Kt8+FS982yTF7E/ozzmar38Rw2zNNSzhnCcx4QHOyROSYRneLJszfi4MZ+fy66ZDV4YiIiIhINZrQ/4abkMyDsP4bs3ycAeHl3H6mBpjkQo+yE49L69gn/ni2z4bENeDpBwPuqPnrWg40PfABfvirGTQup7b6HGjc2PiGwMWvm+HGl00xrZUa0rBHTIVDTnJl250TObjazP7w8IHOF9ZvfDXhE2QGRJ8gSV1jnS8C3zDI3A9bf6rda5O3wKKy9oajnqm+BZ/dYWY1+Iaaz3L2E3WP+XgWvmISDTE9ocPI+juOzQY9rzbLqz6pv+OUmzcJclMgvH3NKgNrwj/CzFVxp8HgjcWIf5ufCbv+0JwsETkmbw8Hl/c1VXEfLd5jcTQiIiIiUh0lNRqjdV+atiLxp5krfE+gvFIjxF3bT5XrW9aCatM0k9ipD/Mmmfs+N9Z+HsA5T4B/M3OidP7Lro5M3E19DjRujNqdDbf/Dq2GNPyxPX3g4tfK5jx8Beu/PfFrygeEdxztmsqIxsbTB3qVtVKqzcBwp9MMLi8tNvMLOow49rZBsXDRZLO88FXY9svJx3s8uammJRA0TBu37mNNS7Pd8yF1R/0dJ30vLHrNLJ/zr7q1HBPXCG1pqpAAfry34VqriYjbuaa/aUH1+5ZkdqfkWByNiIiIiPyZkhqN0Wnj4Zov4exHa7R5eq4ZFO7WlRoAUV1MIsdZcvJ94o9n71Jzdabds/p2KyfiG2quagb4/TmdDDmV1fdAYzlaXB8Ycq9ZnjbB/BscS0kxrP3SLHcfW/+xWaV81sm22ZC6s2av2Twdtv9qhpePfPrE2yecB/1uNcvf3GEqlFxt4WQozIbo7iYJVd+C48ywdoBV9TgI/denTHu6lkMa5n1JzZw+AVoMgoJMmHptw81WERG30iLcj6EdmgHwiao1RERERBodJTUaI7vdDEltNbhGmzeJmRrl+pX1iV/x/sn1iT+eeS+a++5XQnDzk9tH1zHQ9mwoKYAf/6qe3KeqhhhoLEc74wGI7GLa+UybcOzvvx2/Qc4hM5i5KVfRhLctm+XihOXvnnj7ojyYWTYAfOBd5vU1MeJJiOxs2n99cweUunBoam4qLC6bIdUQVRrlyltQrf7Ute+n3IFVZrg9mM9Pic/Gw+EJl79n5gMlb4Tv79bvchGp1rUDWgDw+bK9FBSXWByNiIiIiBxJSQ0353Q6K2ZqhLp7pQaYPvF+4WV94me5br+HNsLmaYANBt9z8vux2eD8F8DD11R9rK7Hq3ylcWqogcZyNA9vuOR1sHvAxu+P3RO/vPVU1zFNv+VP+cDwlR+deNbPglcgfTcExsLp99X8GJ6+cNk7ZhbB9tmVLZVcYdHrUJgFUV2h47mu2++JJJwP3sGQsRd2/e7afTud8NM/zHK3KyCut2v3L3UXGAVXfGB+lqz/2rVf0yLSZJyVEElMsA9puUXMWJtodTgiIiIicoRGkdSYPHkyrVq1wsfHhwEDBrBkyZJjbjts2DBsNttRt/POO69imxtvvPGo50eNGtUQb6XB5RSWUFRirjAMawqVGh7e0Otas+zKgeHlszQ6nQ/NOtRtX6GtYNhDZnnW381Jbjl1NNRAY6leTA8YWjYsfPrfICup6vMFWbDxR7Pc48qGjc0KHUZBUJypXtnw3bG3S98Df7xglkc8Cd4BtTtOZCcYNdEs//I4HFh5UuFWkZcGi98wy2c8YKoUG4qnD3QbY5ZXfuzafW+ZZZLeDm84+5+u3be4TosBMLLsa/qnf8KuedbGIyKNjofDzlX9TbXGR4t2WxyNiIiIiBzJ8qTG1KlTmTBhAo899hgrVqygR48ejBw5kkOHqu/b/fXXX3Pw4MGK27p163A4HFx++eVVths1alSV7T79tGleUZ9W1nrKx9OOr5fD4mhcpM84wGauCHbFENf0PbD2C7Nc3pO/rgaON1cW56XCT393zT6l8WvogcZSvdPvM/MX8tLgh3uqto7Z+IMZ4B7eDmJPgSvkHR7Q+wazfLxE8E//MJ9Ly8GmguVk9BkHnS6A0iL48ua6zyJY9IaZaxDZGRIuqNu+TkbPsgT6xh8gP8M1+ywphp/L5mGddieEtHDNfqV+9L/VtKR0lsAXN0LmAasjEpFGZmy/eBx2G8t2p7F6b7rV4YiIiIhIGcuTGi+88AK33nor48aNo3Pnzrzxxhv4+fnxzjvvVLt9WFgY0dHRFbeff/4ZPz+/o5Ia3t7eVbYLDQ1tiLfT4JrUPI1yYa0r++Avq0Gf+BNZ8Io5YdH6DDNs2BUcnnDBS4DNtKCa+Yg5MaYTIk3bwlcbdqCxVM/hCZe8AXZP2DKjst0UwOqyOQbdx546Safe14PNAXsXQdL6o5/fMddUcdjsMPo/J/+52GxwwcumMiR1O8x44ORjzks3raeg4as0ysX1hoiOJtmz/hvX7HPlB3B4M/iGmYHU0rjZbHD+JIjqZmbGfH49FBdaF09WEix7x8yu2bvUujhEpEJkkA8X9YwFYOKMjTg1g0dERESkUbA0qVFYWMjy5csZPnx4xTq73c7w4cNZuHBhjfYxZcoUxo4di7+/f5X1c+bMITIyko4dO3LnnXeSknLsFkEFBQVkZmZWubmLinkaTSmpAVX7xBfln/x+spNhxQdm2dUnmJr3hf63meVFk2HqtfBCJ3i+E3x2jWn1svN3yHefryc5jtxUWPyWWVaVhvWiulS2gZv+gEkoZh4w33MA3a+wLraGFhQDCWUtGJf96YKAkiKY8aBZ7nszRHer27H8wuDSt02CZNXHsPbLk9vP4jehIAOadYJOF9UtppNls0Gva8zyqk/qvr+CLPjtabM87GHwCa77PqX+efnBlR+Yf699S2HWww17/NSd5uKLKSPh+Y7w473mYgkNMBdpNO4b0REvDzuLdqQyZ3Oy1eGIiIiICBYnNQ4fPkxJSQlRUVFV1kdFRZGYeOJhbEuWLGHdunXccsstVdaPGjWKDz74gNmzZ/Of//yHuXPnMnr0aEpKSqrdz8SJEwkODq64xcfHn/ybamDlSY2wpjAk/EgdRkJQc9Pe6Xh94k9k8RtQnA+xvUylhquNmgiXvGnav0R1Myf6sg7Aph9h9hPw/gXwTAuYPAC+HW/awxxYZU40intZ9FrZQONulSeQxVqD/2paTBVkwPd/KWsz54QWgyC0pdXRNay+N5n71VOrtoVa+j9I3mgqB858xDXHajW4cq7Jj/eak7K1kZ9hEsEAZ9xvTZVGue5XllW5LIbDW+u2r/kvmav9w9pC33GuiU8aRlgbuPR/gM18z6yqx5alTqepqJrzH3h9CLzc07SH27sIcJqfaZ5+5vt21x/1F4eI1FhciC/jBrUCTLVGSakSjiIiIiJW87A6gLqYMmUK3bp1o3///lXWjx07tmK5W7dudO/enbZt2zJnzhzOPvvso/bz8MMPM2FC5VX8mZmZbpPYSM0xJ8dDm1pSw+6APjfAb/+GZVNObuBvfmbl/IMhE+rnynq7A3qMNTeAwhw4uBr2L4d9y2D/CsjYA8mbzG3VR2Y7Dx/TMubc51wfk7heXpq5shxMqxxVaTQODg+4+HV4cyhs+xl2LzDrT4UB4X/W+gxzMj11O6z93CQ5spPht7JByGc/aqosXGXoA6at1d5F8NYZtatKKMo3iY2IjtD5YtfFdDICo6HdcNg6y1RrDH/s5PazdykseNUsn/Mv0yJN3EuHEab6a85E+PGvENUZYnq4Zt+lpebvgo3fm4sejpwXZnNAy0FmXk3CeRDcHH6cYP72WfwmtB7qmhhEpE7+b1g7Plu6ly1J2Xy1fB9X9HOP/yuKiIiINFWWVmpERETgcDhISkqqsj4pKYno6OjjvjYnJ4fPPvuMm2+++YTHadOmDREREWzbtq3a5729vQkKCqpycxdpFTM1muAJlN7Xg93DXEG77ivTg702lr9rruAObw8J59dLiEfx8jcnJwbdDVe8D/euhfu2wFWfmSub25wJ3sGmemTJW5C8pWHikrpZ9HrZQOMuDfe1JDUTmQBn/d0sF+WAwws6W9TOyEp2e2W1xtJ3zNXgs58wPwNjepifp67k8IAxb4NvqElQpO+p+S3nkNnHWf8wiWGr9bza3K/+DEqrr+g8ptJSU6Hx7igzm6PV6arkcmdDH4D2I83v6KnXmbaDJ8vpNMmuaffBi51hynBY8LJJaDi8ocNouGgy/G0r3PgjDLjdJDSgsrXl5umQtrvu70tq7JlnnsFms/HXv/61Yl1+fj7jx48nPDycgIAAxowZc9T/XaTpC/bz5O6z2gHw/M+bySus5e8LEREREXEpSys1vLy86NOnD7Nnz+biiy8GoLS0lNmzZ3PXXXcd97VffPEFBQUFXHvttSc8zr59+0hJSSEmJsYVYTcqTXamBpgraBPOM+2nviw7WRfe3syyiOtjhrxGdQOPat57UT4sLGtvMuSv1rY3CYwyQ6XLB0uXlsKnY82VwSveh5H/ti42ObG8dFj0hlm2aqCxHN/Au2Djj7BvCXQYZU60n4p6Xg2/PglJa80V3ivLKsNGP1c/yYOQFnDXspM76eoTDBHtXB/Tyeg42nzNZB2AHb+Zyo2ayEmBb++ArT+Zx10ugQteUiWXO7Pb4dI34a1hkLYLvr4Vrv68dt8/Rfmw/mvzPXhwVeV6r0BTDdLpAvM15h147H1EJkCbYbBjjmmHNeLJk3o7UjtLly7lzTffpHv37lXW33vvvUybNo0vvviC4OBg7rrrLi699FLmz59vUaRilesGtuS9BbvYl5bHO/N3Mv7MRvJ7TEREROQUZHn7qQkTJnDDDTfQt29f+vfvz6RJk8jJyWHcONOP+vrrrycuLo6JEydWed2UKVO4+OKLCQ8Pr7I+OzubJ554gjFjxhAdHc327dt54IEHaNeuHSNHjmyw99VQmuxMjXLn/Mtc0bhviTnBkLLV3FaX9bt2eEF0d5PkKE92hLWB1Z9AdhIExUG3RjYw2G43/da3zjLv4+zHqk/MSOOw+I0jBhpfaHU0Uh27w1RGLXod+t1y4u2bKr8w6HKp+fk3s2w4ePex0GJA/R3TP8Lc3JmHN3S73FTPrfy4ZkmNXfPhq5sh66BpJzjqGehzoxIaTYFvKFz5MfxvOGz7BeY8U1kNdjwZ+8zcrBXvQ26KWefwNsmurmOgzRnma62mBtxhkhor3jdtsbz8T+rtSM1kZ2dzzTXX8Pbbb/PUU09VrM/IyGDKlCl88sknnHXWWQC8++67dOrUiUWLFnHaaadZFbJYwNvDwf0jO3LPZ6t4fc52xvaLJzygFt/XIiIiIuIylic1rrzySpKTk3n00UdJTEykZ8+ezJw5s2J4+J49e7D/6crozZs3M2/ePH766aej9udwOFizZg3vv/8+6enpxMbGMmLECJ588km8vZveH52pZe2nQppi+ymA0FamxQmYq2L3Ly+7LTP3eWlly8tgSdnMA58Q0/YBzBXcjTFh0O4cCIwxJ8Q2TzMnPaTxyc8wA8LB+oHGcnxBsbqaGaDfzSapAeAVAOc8YW087qLnNSapsWma+b1yrGqf0hL44wWY8zQ4SyGiA1z2LkR3bdh4pX5Fd4ULXzaVGr8/C7G9IOHco7dzOmHXPPP3x6Zp5msCIKi5+V7sff3JJ/3ajzB/A6XtgjWfa/h8PRs/fjznnXcew4cPr5LUWL58OUVFRQwfXpnsTEhIoEWLFixcuFBJjVPQBd1jefuPHazbn8krv27j8Qu7WB2SiIiIyCnJ8qQGwF133XXMdlNz5sw5al3Hjh1xlp+0/hNfX19mzZrlyvAatfRcMyi8yVZqHMk/3LRu6DDCPHY6IW0n7Dsi0XFwDeSnm+f9ws2w8cbI4WFOov3xX1jxgZIajdWKDxrPQGORmojrY07AHlhp2qUFHn8+lZSJ6WFm5hxab2Y4VVfxk5VkTnLvnGse97gKzv0veAc0bKzSMLpfAfvKLpj45na4bQ6EtzXPFebAmqmw5G04tKHyNa1ON/MwOp5rfs/Xhd0B/W6Fn/5uEm6qBKo3n332GStWrGDp0qVHPZeYmIiXlxchISFV1kdFRZGYmFjt/goKCigoKKh4nJmZ6dJ4xVp2u41HRnfi6v8t5qNFu7lxUCtaRaiSSkRERKShNYqkhpy81JwmPFPjRGw202oqrA10v9ysKy40J6UOrjEn9xpzu4Ze15qkxvbfTE/60JZWRyRHKi0xJ5IABt3VOAYai5yIzQZXfAD7lkJnJUtrzGaDXtfArEdg1SdHJzW2/wZf32aGnHv6wXnPVw4Yl6ZrxFNwcDXsXQRTr4VL3zZtI1d+aBLeYL4eul9pkhlRnV17/F7Xwm//NomTXX9A66Gu3b+wd+9e7rnnHn7++Wd8fHxcss+JEyfyxBOqkmvKBrWLYFjHZszZnMxzP21m8tW9rQ5JRERE5JSjXipuzOl0Vg4KPxUqNWrCw8tcpdznhsbfDiSstRkEirNyoG9jcGgTfH07zHgISoqsjsY6W2ZC+h7Thqbb5VZHI1JzIS1MD3+1S6udbleA3cNU/h3aZNaVFMOvT8GHl5iERmQXc8W+EhqnBg8vM68nIMokFt4YDAtfNQmN0NYw8mmYsAEumOT6hAaAb4ipCAIzeFxcbvny5Rw6dIjevXvj4eGBh4cHc+fO5eWXX8bDw4OoqCgKCwtJT0+v8rqkpCSio6uvhHv44YfJyMiouO3du7cB3ok0tAdHJWCzwbQ1B1m5J83qcEREREROOTrj4cZyCksoKjFtuMJOxUqNpqD39eZ+5Ufm5JmV0vfAt/8Hrw+ENZ/B4tdh6nVQlG9tXFYpP4HU+wbw9LU2FhGpfwHNoP1Is7zqY8jYD+9fAL8/BzhN+59bZ0OzjlZGKQ0tMBoufx/sZbPL2g2Hqz+Hu1fAwPHHnr/iKv1vM/ebp5uqTnGps88+m7Vr17Jq1aqKW9++fbnmmmsqlj09PZk9e3bFazZv3syePXsYOHBgtfv09vYmKCioyk2ank4xQYzp3RyAiTM2HbM1soiIiIjUD7WfcmNpZa2nfDzt+HqpNY5bSjgffMMg6wBsnw0dRjZ8DDmH4Y/nYen/oMR8TdFuuBl+umUGfHoljP2kcbfycrVDG03ffJu9+t76ItI09bwaNk8z7YVWfgR5qeAVaK7E73aZ1dGJVVoOhPGLTRvC0FYNe+zIBFPVuWOO+T094smGPX4TFxgYSNeuVSt7/f39CQ8Pr1h/8803M2HCBMLCwggKCuLuu+9m4MCBGhIuTDinAz+sPsCSnanM3niI4Z2jrA5JRERE5JShSg03Vj5PQ1UabszDu7K1xPL3G/bY+Znw20R4qQcses0kNFqdDrf8Ctd+Bdd8CZ7+5kTKh5dW9g8/FZTP0kg4D0LirY1FRBpOh5HgFwF5aSahEdMDbp+rhIaYIeENndAoN+AOc7/iAyjMtSaGU9iLL77I+eefz5gxYxg6dCjR0dF8/fXXVocljUBsiC83DWkNwDMzN1FcUmpxRCIiIiKnDiU13Fj5PI0QJTXcW3kLqi0zISux/o9XlA8LX4OXe8LcZ6AwG2J6wnXfwA0/QPM+ZrvWp8P134F3sBmS+sFFkJta//FZLS8NVn9mlstPJInIqcHhCYPvMVVa/W+Dm382J7NFrNR+hEmo5KfD2s+tjqbJmzNnDpMmTap47OPjw+TJk0lNTSUnJ4evv/76mPM05NRz57C2hPp5su1QNl8s32d1OCIiIiKnDCU13Fh5UiNMQ8LdW2QCxA8AZ4np415fSopNO5VX+sCshyE3BcLbmV7ht82BtmeBzVb1NfH94MYfwC8cDqyE986DrKT6i7ExWPkRFOWagcAtB1sdjYg0tMF/gb8nwrnPmWo6EavZHdDvVrO8+E1Q736RRiPIx5O7z2oPwAs/byG30OIZeSIiIiKnCCU13FhqThEAoUpquL/eN5j7FR9CqYtL151O2PgDvD4IvhsPmfsgMBYueBn+bzF0ufjoZMaRYnrAjdMhIBoObYD3zoWMJnolWmkJLHnbLA+4/fifi4g0XUpmSGPT61rw9DO/h3fNszoaETnCtae1pEWYH8lZBUz5Y6fV4YiIiIicEpTUcGPlg8JD/TwtjkTqrMvF4B0EaTth1x+u22/GPvjfcJh6LRzeDL6hMOIp+MsK6HMDODxqtp/IBLhpBgS3gJRt8M5oSN3hujgbiy2zIH23+Zy6XW51NCIiIoZvSOUMrsVvWBqKiFTl5WHn/pEdAXhj7nYOZxdYHJGIiIhI06ekhhsrbz8Vqpka7s/Lv3IQ7YoPXLPP0lL4+nbYv8xc3Tn0frhnNQy6Gzx9a7+/sDYwbjqEtYWMPfDuuZC82TWxNhblJ4p6Xw9eftbGIiIicqT+t5n7zdMhfY+1sYhIFed1i6FH82ByCkt4efZWq8MRERERafKU1HBjmqnRxJS3oNr4vWsGci95C3bPA09/uGMenPUP8Amu2z5D4mHcDIjsDFkHTWLj4Jq6x9oYHNoEO+eaAcH9brE6GhERkaoiE6DNMHCWwtL/WR2NiBzBbrfx0OhOAHyyeA87krMtjkhERESkaVNSw42llrWfClH7qaYhtidEd4eSQlgztW77StkOvzxulkf8C8Lb1jW6SoFRcOM0iOkJuYfh/fNh3zLX7d8qS9409wnnQUgLa2MRERGpzoA7zP3y96Ew19pYRKSKgW3DOSshkuJSJ8/NamLVzCIiIiKNjJIabiw91wwKV6VGE9KnrFpj+ftmwPfJKC2Bb++E4jxzRWffm10WXgW/MLjhe4gfAPkZ8MFFNR9cWlwIabthzyJY97W52jR5i+tjrI28NFj9mVnuf7u1sYiIiBxL+xEQ0hLy02Ht51ZHIyJ/8uCoBOw2mLEukeW706wOR0RERKTJquGUYGmMUnM0U6PJ6XY5zPoHJG801Q/x/Wq/j0Wvwd7F4BUIF74CNpvr4wTTyuq6b+DTq0zbpo/GwGXvmNkbmQfMLetg1fvMA6a6489sduh5NZzxkGlx1dBWfgxFuRDZBVoNafjji4iI1ITdYWZr/PR3WPyWaV1ZX7/nRaTWOkYHcnmfeKYu28vE6Rv54o6B2PQ9KiIiIuJySmq4KafTWTkoXJUaTYdPMHS5BFZ/Aiveq31SI3kLzH7SLI/8d/23UfLyh6s/h8+vh62z4LOra/Y6hxcExkBQLNgcZvbHyo9gzRdmnsXp94F/eP3GXq60xMwfARhwu04OiYhI49brWvjt33BovamSbH261RGJyBHuPacD363ez7LdaXy/+gAX9YyzOiQRERGRJkdJDTeVU1hCUYlpTxSmSo2mpff1Jqmx7msYORF8gmr2upJi+PYOKCmAdsPNfhqCpw9c+RH8cI+ZBeIdCEFxEBRTmbgovw+KhcBY077qyOTB3qUw+wnY9QcsmgwrPoBBd8PA/zP7q09bZkH6bvAJMZUyIiIijZlvCPQYC8vegcVvKKkh0shEB/tw2+ltePnXbdz/xRqCfT0Z1jHS6rBEREREmhQlNdxUWlnrKR9PO75eDoujEZdqcRpEdIDDW2DdV9B3XM1et+Bl2L8cvIPhgpcbtuLAwwsueR0uetW0xqit+H5www+w/Vcz4DxxDcx52lRQDL3ffAYe3i4PG6gcEN7nBvDyq59jiIiIuFL/201SY/N0SN9T/5WZIlIrfzm7PVsPZTNjXSK3fbicd27ox5D2EVaHJSIiItJkaFC4myqfp6EqjSbIZqussljxQc1ek7QB5kw0y6OfgWCLytxPJqFRzmaDdmfDbXPhsnchrK2ZvzHzQXilL6z6xLSKcqVDm2DHHDPTo98trt23iIhIfYlMgDbDwFkKS/9ndTQi8iceDjsvX9WLczpHUVhcyi0fLGXRjhSrwxIRERE5sYIsqyOoESU13FRq2TyNECU1mqYeV4HdEw6sgMS1x9+2pKis7VQhdBhlXuvO7HboeimMXwwXvGRaV2XsgW/vhNcHwcYfwel0zbHKqzQ6nqurXEVExL30v93cL38fCnOtjUVEjuLpsPPq1b04s2Mz8otKuem9pSzblWp1WCIiIiLVK8qHX/8Nk7pDxj6rozkhJTXcVHpZUiNMQ8KbJv8ISDjPLJ+oWmPei3BwtZkJccFLTWfQtcMT+twIf1kJ5/zLvL/kTTD1GvjfcNi9oG77z0uH1Z+Z5QF31DFYERGRBtZhJIS0hPx0WPuF1dGISDW8PRy8fm0fTm8fQW5hCTe+u5SVe9KsDktERESkql3z4Y0h8PuzkJdqZuY2ckpquKnUnCIAQpXUaLr63GDu10yForzqtzm4Bub+xyyf+18IjG6Y2BqSpy8MvgfuWQ2n3weefrB/Gbx7Lsz+lxmQfjJWfgRFuRDZBVoNcW3MIiIi9c3ugP63meXFb7quilFEXMrH08Fb1/VlYJtwsguKuf6dJazdl2F1WCIiImKFnb/DwslQmGN1JEZeOnz/F3jvXEjZCgFRcPn7MGSC1ZGdkJIabqp8UHion6fFkUi9aT3MtETKz4AN3x/9fHGhaclUWgwJ50O3yxo6woblGwJnP2oqN3pdCzjhj+fh/fNrXxZXWmKGkAMMuK3pVLeIiMippde1Jtl/aD3smmd1NCJyDL5eDqbc2Jd+rULJyi/m2imL2XAg0+qwREREpKHkpcG34+H9C2DWI/DG6bBvmXXxOJ2w/huY3B9WvG/W9b7BtILvcrFbnCdTUsNNpeWWJzVUqdFk2e3Qq3xg+PtHP//7c5C0DvzC4fxJbvEDxyUCo+GiyWaYuFcg7FloSuQ2z6z5Prb+BOm7TUurblfUW6giIiL1yjcEeow1y+VzokSkUfLz8uDdcf3p1SKEjLwirp2ymM2J7jGIU0REROpg03SYfBqs+giwgW8YpG6HKSPgt4lmVm5DytgHn14FX9wI2UkQ3h5unA4Xvgy+oQ0bSx0oqeGm0jRT49TQ6xqw2WH3fDi8rXL9gZWmSgHgvOchoJk18Vmp66Vwx+8Q09NkvD+9Emb93VSwnMjiN8x9nxvAy69ewxQREalX5QPDN02D9D3WxiIixxXg7cF74/rTvXkwqTmFXPO/RWw7lG11WCIiIlIfclLgy5vhs6sgOxHC28FNM+EvK6DrZeAsgbnPmOTG4a31H09piWlbO3kAbJkBdk8440G4Yx60Glz/x3cxJTXcVGpZ+6kQtZ9q2oJiof0Is1xerVFcAN/caX74dbnE3E5VYW3g5p/gtP8zjxe+Cu+OgrRdx37NoU2wY45JFvW7pSGiFBERqT+RCdBnHIx+1lz1JSKNWrCvJx/c1J/OMUEczi7k6rcXsfNwI+mrLSIiInXndMK6r01rp3VfmvNPg/9qkgctTjPVEJdNgTFTwCcYDqww7aiWvF1/c/KS1pvkyYwHoDAb4gfAHX/AmY+Ap0/9HLOeKanhptJzTWmSKjVOAb3LBoav/tRUIcyZCMkbwb8ZnPu8tbE1Bh7eMGoijP3E/DLYvxzeGAobvqt++/JZGh3PNTNLRERE3N0Fk6D/reAdYHUkIlIDIX5efHTLADpGBXIoq4Cr317EnpRcq8MSERGRuspKgqnXwpfjIPcwRHaGW36Bc54AT9+q23a7DO5cCK3PgOI8mP43+PgyyEp0XTxF+TD7X/DmUNi/zLRxP+95GDcTIju57jgWUFLDTaXmaKbGKaP9CAiIhpxk+O0pmP+SWX/+i+Afbm1sjUnCeSbr3bw/FGTA59fDtL+ZH+Dl8tJNcghgwO2WhCkiIiIiEubvxce3DqBdZAAHM/K56u1F7EtTYkNERMQtOZ2w6lNTnbHpR7B7wBkPwW1zIa7PsV8XHAfXfQujngGHN2z7BV477dgX6tZUQbaZ5fH6INO+vrQYEs6Hu5aYriV2908JuP87OAU5nc7KQeGq1Gj6HB5mtgaYhIaz1Ay37nSBtXE1RiEtYNx0GHyPebz0bZgyvHIeycqPoCjXZMpbnW5dnCIiIiJyyosI8OaTWwbQOsKf/el5XP32Yg5m5FkdloiIuELmQZj+AMybBCnbrY5G6lPGPvj4cvj2DshPh5gecNscOPNh8KjBeVu7HU67E27/HaK7m7mxn18P39wB+Rk1jGE/rPvKfM29ORSeaWFmeaRuNxdKX/EhjP3YtLlvImxOZ30163JfmZmZBAcHk5GRQVBQkNXhHCUrv4huj/8EwMZ/jcLXy2FxRFLvUnfAy73MckA0/N9C8FPf7OPa+jN8czvkpoBXAJz3Asx52szbuOAl6HOj1RGKiIg0GY3972er6HORmjiYkceVby5iT2ouCdGB/Hj3EDwcuv5QRMRtbZsNX99m2g+Va5ZgrpRPOA9ie4HNZl18ALmpJs6YHtCsg7Wx/FlxASx7FxZOBpwQ2xNie5vPLbYX+IZYHGAZpxOWvwc//RMKs8DhBcMehkF/MRcon4ziQjM8fN6L5qLm4Hi45A1oNaRym9ISMyNj72LYs8jcZ+w9el9Bzc0F0Wc+bNq1u4ma/v2spEY1Gvt/Pvam5nL6s7/h42ln05OjrQ5HGspn18DmGXDVp9BhpNXRuIfMA/DVLbB7fuU6nxCYsBG8/CwLS0REpKlp7H8/W0Wfi9TUvrRczn9lHum5Rfz7kq5cM6Cl1SGJiEhtlRSbOah/PA84IbILBEbBzt9N+59yQXEmuZFwHrQcDA7PhomvIMu0JFr3FWyfbWJyeMHZj8Jp461vSVRaAms+h9+ehow9x94urK1JbsSVJTpieoCXf8PFmX0INk0z7c33LjbrmveDiyZDs46uOcaeReZC3bRdgA0G3GGSOXsWwb5lJolyJJsdorqaQeTxA8x9cHPXxNLAlNSog8b+n4/Ve9O5aPJ8YoN9WPDw2VaHIw2lKB/yUptUqViDKCmG35+Fuc8CTpMxH/Gk1VGJiIg0KY3972er6HOR2nh3/k6e+GED4f5ezLl/GIE+DXSSS0RE6i7zIHx1c+VFlX3GwaiJZjh0XrrpJrHpR3NflFP5Op8Q6DDKJDjane36k/NFebD1J1j7pbkvPmLuaGAsZB0wy61Oh4tfM229G5rTaS7inf0vSN5o1gVEw7AHIbw9HFgJB1aY+7RdR7/eZjeVMOWVHLG9IboreHi7Lsb0PbDxR9j4A+xZCJSdTvfwNUmhAbeD3cWddAqyYObDsPLDo5/zCoTmfSuTGM37gnega49vESU16qCx/+fjt82HGPfuUjrHBDH9Hs0FEKmRXfNh51yT1PAOsDoaERGRJqWx//1sFX0uUhtFJaWMfPF3dhzO4f+GteWBUQlWhyQiIjVxZLsprwDT8rrbZdVvW5Rvzk1s/MGcyD+yRZWHD7Q9C9oMg5CWJsEQEl/7k9XFhbDjN1ORsWkaFGZXPhfeDrpeBl3HQER7WPE+zHzEJFq8g+Dc/0L3KxquPdau+fDL47BviXnsEwxDJkD/26rvsJGbWpng2L/S3JcnZo5k94SozpVtq+J6m8RHbapikjebf6eNP8DBVVWfi+1lWjt1u8L8G9WnTdNg8RvgH1mZxIjq4vokSiOhpEYdNPb/fHyzch/3Tl3NkHYRfHTLAKvDEREREZFTXGP/+9kq+lyktn5an8htHy7Hy8POr/edQfNQtUwVEWm0/txuKqobXP4eRLSr2etLS0z7ok3TzInz9N3Vb+cbamYrhLQwt4rleLPsG2rmL+yaZxIZG783w6bLBcdD10tNMiO629EJi5TtptXRvqXmceeL4fwX63eWa+Ja+OUJ2PazeezhC6fdAYPvMe+nNrISy5IcKyoTHrkpR2/n4WMGcR/Zuiq8fWXbLafTJC/KExmHt1S+1maHFoNMIiPhvPpPZJzCavr380lOLRErpeYUARDq72VxJCIiIiIiIuIq53SO4rQ2YSzakcpzszbz0theVockIlK/8jNgw3eQtAHC25q5AFGdG/9g48wD8OXNsGeBedz3Jhg5ETx9ar4PuwNaDjK3EU+Z4c+bpkHiGpPgSN8L+ekmQZGXZtZXxyvQDKY+MpHhHwldLjEVI837Hb/yIrwtjJsJ81+EOc/Ahm/N7IaLJ0O74TV/PzWRusPMzFj7hXlsc0CfG2DoAxAUc3L7DIyGjqPNDUxyImNv1STHgVVQkGkqQsqrQsB8djE9ILSVqaA5cuC23dNUzXS6ADqeCwHNTi4+qRdKarihtJxCAEL91GNVRERERESkqbDZbPzjvM5c8Oo8vlt1gHGDW9MzPsTqsEREXKukCLb/Cqs/g83Tq855KBfcwrTYqbh1hbA25uS91bb9UtZuKsWcFL/wJdPOqS5sNjMHIrpr1fX5meZEe/peM9chY88Ry3shJ7lyaLRPCHS+0FRktBpSu/ZEDg8Yej+0Pdu8t5St8NEY0wZq+BPVt4KqjawkM+90+XuVQ9O7joEz/26SKq5ks1VWtXS52KwrLTUJlYrWVSvg4Grz2e2eZ24Ann7Q/hzodKG5b+zJtVNYI/hJILWVllue1FClhoiIiIiISFPSNS6YS3s156sV+3jqxw18ccdAbA3V21xEpL44neYk8urPYN2X5mR8uWYJ5or4tF2mWiFjrzl5n7EHtsyo3M7Dx2wb1bUy2dHiNNcOhD6ekmKY83RZuylMK6fL33f9Sfkj+QSBT9l7rU5hLmTsMxUvMT3Ao47nCuN6w+2/mzkXS96EJW/B9t/g0rfMczWVlVhZKbF/BexeAMV55rl2w81w7ZgedYu1Nux20xYsop2ZGQLm3/PwZhNf6g4zbLvtWWa4uzR6Smq4ofKkRpjaT4mIiIiIiDQ594/syLS1B1i2O40Z6xI5t9tJtuQQEbFaxn5Y+zmsngrJGyvX+0VAt8uhx5UQ07Nqe6S8NDi0ERLXQdI6k+g4tAGKcs3MgyOHNvs3g743Q7+bISCy/t7Hn9tN9bsFRvy7du2m6oOXHzTr4Pp9nvssdBgB3443VRtTzoEzHoIh9x5dLZOXdsRMi7L76oZ3N+8HZz8GrU93bbwny+FRmRwTt6OkhhtKLWs/FaL2UyIiIiIiIk1OdLAPtw1ty8uzt/LMjE2c3SkSb49atBERETkZxQVm9sCeBWamwsHVpjoiINIkDwIizayGgGZl90es9wqoTEwUZJtBy6s/hZ2/A06z3uENCedCj6vMFfGOY5zX8g2tnDVRrrQU0naaBEfSepPs2LcUspNg7jMw7wXTdum0OyGmu+s+k/S95r388d8j2k29bAZvN3XthsP/LYQf7zVzNn57CrbOgjMehMNbK6swUrcf/Vqb3VTVxPaGuF4Q19dUZqjyUFxESQ03lJ5rBoWrUkNERERERKRpun1oGz5dsoc9qbl8sGA3tw5tY3VIItLU5GfC3iWVSYz9y6ufb5G288T78vA1yQ6/CEjeZKoqyrUYBD3GQueLwDfk5GK1202bp/C2Zm4EmNkcG3+ARa+b4c+rPzG3lkNMcqPj6NrNlSh3eBts/M7s+8DKyvXR3eHy9+q33VRj4xdm3vPaL2Da30wi6ePLjt4utBXE9SlLYvQ2n5V3QENHK6cQJTXcUGqOZmqIiIiIiIg0Zf7eHtw/oiMPfLWGl3/dypg+zXVhm4jUTVaimW2wZ5FJZCStB2dp1W38IsycipaDzNX1zhLIPmRmYGQfgpxDkJ1cdl+2vijXzEtI32NuAGFtTSKj+xXmhHd9cHiaiomul8K+ZSa5seHbysHPoa1gwB3Q8xozm+JYnE5T+bHhe5PIOLJNFjbzWXS+CHrfYH27KSvYbObfscVAmH6/+bqJ7lpZhRHb2yQ/RBqQkhpuxul0aqaGiIiIiIjIKWBMn+a8M38nmxKzeHn2Vh6/UH2/RaSG8jPg4BrTQurgKnPSv7qKi9BWppKi5UBz0jq8Xe1bBBVkV012BMaaq/UbstVQ875w2RTI+BcsfRuWvWsGj898CH79N/S+DvrfBmGtzfalpaYypbwiI21X5b7sHtD6DOh0ASScV7+zOtxJSDxc/ZnVUYgASmq4neyCYopKTC9CVWqIiIiIiIg0XQ67jX+c15lrpyzmo0W7uX5gS9o0UzsPEfmTvDSTvDiwqjKJkbqjmg1t5gr7FoNMNUaLgRAUU/fjeweYW1gjaJMXHAfDH4ehD8Caz0z1xuEtsOg1s5xwHgTGwKYfIetg5es8fMwMiU4XQIeRZq6HiDRaSmq4mfJ5Gj6edny9NChORERERESkKRvSPoKzEiL5ddMhJs7YxNvX97U6JBFpSE6nGeBdnF95n7LNJC7KExnpu6t/bXALiO1hBjTH9oLm/cAnuCGjt46XH/S9CXrfCDt+NQmNbb+YZEbFNgEmgdHpQpPQ0AwIEbehpIabKZ+nEaYqDRERERERkVPCI+cmMHdLMj9vSGLh9hQGtg23OiQRqYv0Pabl0Y45pnXTkQmLksKjH9dEaCuI6VmWwOgJ0T3AXz8rsNtNwqLdcEjebNpSFedBx3NNi6lTcUaGSBOgpIabSS2bpxGipIaIiIiIiMgpoV1kIFf3b8GHi3bz1LQN/HDXEOz2BuxVL+IqJcWmFVDuYSjMMSf0C8tvOea+4IjlIx+XFJrB0HZPcHiU3Xua+Qd2j2M/5/Aqu5Wtq1j2qtyuYpuy7UNaQmRn89hVUrbDhu9g4/dwYOVJ7sQGHt4QFGcSFzE9yhIZ3dUuqSaadYTRz1gdhYi4gJIabiZdQ8JFREREREROOX8d3p5vV+5n/YFMvl65n8v6NLc6JJHjKymG5E3mBP7BVaZNUtI6U33gDjz9ILa3GUDdvJ+5BUbVbh+HNlUmMpLWVa632c1ci4TzICjWzHPw8D7i3vvodQ5vk4BpyOHbIiKNlJIabiY1x8zUCFVSQ0RERERE5JQRHuDN+LPa8cyMTfx31mbO7RaNn5f+Sy+NRG0SGF6BZpizl3/ZLdDcewdU87j85m8qKEqLoKQISosr70uLzPGPeq7IVHdUPFdo1pccuVxY+brydcX5kLwFCjJg9zxzKxfcomqSI6a7STiUczohcW1lIuPwlsrnbA5oPRQ6XwgJ50NAZH39a4iINHn6C8jNpJXN1Aj187Q4EhEREREREWlINw5qxUeLdrMvLY+3f9/JPcPbWx2SnKry0mHnXNj5h0lkHCuB4R1U1iKpbFB1TE8Ia2PmHDRmpaWQshX2LTW3vUvh0AbI2GNu67822zm8ILq7SXA4PMycjLRdlftxeEGbM6HzRdBxNPiFWfJ2RESaGiU13ExabnlSQ5UaIiIiIiIipxIfTwcPjkrg7k9X8sbc7YztH09UkIbcSgMoLTHJi22zYfts2LcMnCVVt3HXBEZ17HYzf6FZR+h1rVlXkAX7V5QlOpaZ+9zDsH+ZuZXz8IV2Z0Pni6HDCPAJtuQtiIg0ZUpquJk0zdQQERERERE5ZZ3fPYZ35u9k5Z50nv9pM89e1sPqkKSpyjxoEhjbZsOO3yAvrerzER2g7VllbZh6um8Co6a8A6HNGeYGptVU2q6yBMcSKMyF9ueYm5e/paGKiDR1Smq4mdSy9lMhaj8lIiIiIiJyyrHZbPzjvM6MeX0BXyzfx42DWtM5NsjqsOREivLB7jCDnhuronzYs7AskfErHFpf9XnvYHNCv93Z0PZsCIm3Js7GwmaDsNbm1v1yq6MRETmlKKnhZtLKBoWrUkNEREREROTU1KdlKOd3j+HHNQd5atoGPrx5AA67zeqwpCjfXLmfuh1Stpv71B2QsgMy95sr/btfAX1uhOhuDRdXSRHkHIacZHPLTalczkmufO7QRijKPeKFNojrbRIY7c6GuL5mboSIiIjFGsVvo8mTJ/Pcc8+RmJhIjx49eOWVV+jfv3+12w4bNoy5c+cetf7cc89l2rRpADidTh577DHefvtt0tPTGTx4MK+//jrt27v/EDXN1BAREREREZEHRyXw0/okFmxPYch/fmVsvxZc2S+e6GDN2Kh3absgaYNJWFQkMHZAxj7AeezXFWTC0v+ZW1wf6H0DdB0D3gGuiSs7Gbb9DNt/M7GUJy3y02u+j4DoskqMs8yAa/9w18QmIiLiQpYnNaZOncqECRN44403GDBgAJMmTWLkyJFs3ryZyMjIo7b/+uuvKSwsrHickpJCjx49uPzyylK/Z599lpdffpn333+f1q1b889//pORI0eyYcMGfHzc9w88p9OpmRoiIiIiIiJCfJgfT1/ajX9P28DBjHxe/GULL/+6lbMSIrlmQAuGtm+GXdUbrpWfCT//E5a/d+xtvIPMbImwNhDeFsLaVj4+tMG8duMPsH+5uc16BLpdbqo3YnvWLp7SUkhcDVt+gq2zzBDrYyVVbHbwiwD/ZuBffv+n5dBWENnJtFUSERFpxGxOp/M4lxHUvwEDBtCvXz9effVVAEpLS4mPj+fuu+/moYceOuHrJ02axKOPPsrBgwfx9/fH6XQSGxvLfffdx9/+9jcAMjIyiIqK4r333mPs2LEn3GdmZibBwcFkZGQQFNR4epNm5RfR7fGfANj4r1H4ejksjkhEREREpPH+/Ww1fS7SEPKLSpi1PpGPF+9hyc7UivXNQ325qn8LLu/bnMhA9724r9HY/ht8fzdk7DWPo7tXTVqUL/tHnDgpkHMYVn1iEhyp2yvXx/Q0yY1ul5lWVdUpyDKxbJ0FW3+G7KSqz0d3h/YjIKrLEYmLZuAb2rSHeIuISJNQ07+fLU1qFBYW4ufnx5dffsnFF19csf6GG24gPT2d77777oT76NatGwMHDuStt94CYMeOHbRt25aVK1fSs2fPiu3OOOMMevbsyUsvvXTCfTbW/3zsTc3l9Gd/w8fTzqYnR1sdjoiIiIgI0Hj/fraaPhdpaFuTsvhkyR6+Wr6PzPxiADzsNkZ0ieKaAS0Z2CZc1Ru1VZAFP/0Tlr9rHoe0hIsmQ+vT675vpxN2zSur3vgeSsq6Unj6m8RGnxshtpdpb7V1FmyZBbsXQGlR5T48/aHtmSaR0X4EBMXUPS4RERGL1PTvZ0vbTx0+fJiSkhKioqKqrI+KimLTpk0nfP2SJUtYt24dU6ZMqViXmJhYsY8/77P8uT8rKCigoKCg4nFmZmaN30NDSs0paz2leRoiIiIiIiLyJ+2jAnnsgi48OCqBH9cc5JPFu1mxJ53paxOZvjaRVuF+XD2gBZf1iVdL45rYMQe+uxsy9pjH/W6F4Y+7bgaGzWaSI61Ph5wUWP2pSXCkbIUV75ubX7gZ7H2ksDbQfiR0GAEtB4OHt2viERERcROWz9SoiylTptCtW7djDhWvqYkTJ/LEE0+4KKr6k1o2TyNESQ0RERERERE5Bh9PB5f1ac5lfZqz8WAmnyzewzcr97MrJZenp2/iv7O2cEmvOO4c1pZWEf5Wh9v4FGTBz4/CsnfM45AWZdUZQ+vvmP7hMOguGDjeVGMsfw82fGcSGnZPaDkIOow0yYyIdvUXh4iIiBuwNKkRERGBw+EgKalqD8ikpCSio6OP+9qcnBw+++wz/vWvf1VZX/66pKQkYmIqyy6TkpKqtKM60sMPP8yECRMqHmdmZhIfH1+bt9Ig0jUkXERERERERGqhU0wQT17clYdGJ/DD6gN8smQPa/ZlMHXZXr5Yvpfzuscy/sy2JESrRRoAO+bC93dBenl1xi0w/AnXVWeciM0GrQab2+j/mOHi0d3BR/8+IiIi5SydEuXl5UWfPn2YPXt2xbrS0lJmz57NwIEDj/vaL774goKCAq699toq61u3bk10dHSVfWZmZrJ48eJj7tPb25ugoKAqt8YoNcf0zQxVUkNERERERERqwd/bg7H9W/D9XUP46s6BnJUQSakTflh9gFGT/uCW95exck+a1WFapyAbfpwAH1xoEhohLeD67+G85xsuofFnfmHQaogSGiIiIn9iefupCRMmcMMNN9C3b1/69+/PpEmTyMnJYdy4cQBcf/31xMXFMXHixCqvmzJlChdffDHh4eFV1ttsNv7617/y1FNP0b59e1q3bs0///lPYmNjqwwjd0dpZTM1Qv08LY5ERERERERE3FWflmG8c2MY6w9k8Nqc7Uxfe5BfNibxy8YkBrcLZ/ywdgxsG47NdooMFd/5O3w3vrI6o+/NcM4T4B1obVwiIiJSLcuTGldeeSXJyck8+uijJCYm0rNnT2bOnFkx6HvPnj3Y7VULSjZv3sy8efP46aefqt3nAw88QE5ODrfddhvp6ekMGTKEmTNn4uPjU+/vpz6l5ZYnNVSpISIiIiIiInXTJTaYyVf3ZntyNm/M2c43K/czf1sK87el0KtFCHed2Y6zEiKbZnIjPwMyD8DSKbD0bbMuuAVc9Cq0OcPa2EREROS4bE6n02l1EI1NZmYmwcHBZGRkNKpWVP/38XKmr03kiQu7cMOgVlaHIyIiIiICNN6/n62mz0Xczb60XN7+fQefLd1LQXEpAAnRgYw/sx3ndovBYXeD5IbTCXlpJmGReQAy91e/XJhV9XV9b4Jz/qXqDBEREQvV9O9nyys1pOZSy9pPhaj9lIiIiIiIiLhY81A/nrioK3ed1Z4p83by4cJdbErM4u5PV/LCz1t45NxOnNM5yuowKzmdkLwZdsyBnXPh8BaTsCjKrdnrfUIgvB2c/U9oM6weAxURERFXUlLDjaSVDQoP06BwERERERERqSfNAr15aHQCd57RlvcW7OLdBTvZeTiHOz9azqx7h9K2mUWDs8EkLXbMNYmMHXMgO7H67fzCISgWguLK7mMhqPkR62LAy78hIxcREREXUVLDjWimhoiIiIiIa0ycOJGvv/6aTZs24evry6BBg/jPf/5Dx44dK7bJz8/nvvvu47PPPqOgoICRI0fy2muvVcz/E2nqgv08uWd4e245vTV3fryC37ck868fNvDeuH4NN2cjPxN2zatMYhzeXPV5Dx9oMdBUWsT1geA4CIwBT9+GiU9EREQanJIabsLpdFYkNVSpISIiIiJSN3PnzmX8+PH069eP4uJiHnnkEUaMGMGGDRvw9zdXb997771MmzaNL774guDgYO666y4uvfRS5s+fb3H0Ig3L39uDJy7swogX5zJ3SzK/bjrE2Z3qKbmXlw5J62Dn7yaJsW8ZOEuO2MAGsb1MEqPNMIgfAJ4+9ROLiIiINEpKariJ7IJiikrMTHdVaoiIiIiI1M3MmTOrPH7vvfeIjIxk+fLlDB06lIyMDKZMmcInn3zCWWedBcC7775Lp06dWLRoEaeddpoVYYtYpnWEPzcPacMbc7fz5I8bGNI+Am8Px8nvsDAHkjfBoY1Vb1kHjt42rE1ZEuNMaDUE/MJO/rgiIiLi9pTUcBPpuWaeho+nHV+vOvzhKCIiIiIiR8nIyAAgLMycLF2+fDlFRUUMHz68YpuEhARatGjBwoULq01qFBQUUFBQUPE4MzOznqMWaVh3ndWOr1fsY1dKLu/M28Wdw9qe+EVF+ZCy9U/Jiw2QvvvYrwmKgxanmURG6zMgtKXL3oOIiIi4PyU13ERqTlnrKVVpiIiIiIi4VGlpKX/9618ZPHgwXbt2BSAxMREvLy9CQkKqbBsVFUViYvWDiSdOnMgTTzxR3+GKnLy0XfDHC7BlJjTvB0PuheZ9a/zyAG8PHhyVwH1frObVX7dyae84ooKqaf3kdMLu+TD/Jdg2+0/to47g3wwiO0FkZ2iWUHbfEXxDTurtiYiIyKlBSQ03kVo2TyNESQ0REREREZcaP34869atY968eXXaz8MPP8yECRMqHmdmZhIfH1/X8ETqLmU7/PE8rP6sMsGw6UdzazkEhvwV2g2HGgz/vqRXHB8t3s3KPen8Z8YmXriyZ+WTpSWwaRrMnwT7l1eu9wk2CYvITtCsU1kioxP4R7jyXYqIiMgpQkkNN5GuIeEiIiIiIi5311138eOPP/L777/TvHnzivXR0dEUFhaSnp5epVojKSmJ6Ojoavfl7e2Nt7d3fYcsUnPJW+CP/8LaL8BZata1PQv63gSbZ8KaqbB7nrlFdYPB90CXS8Bx7FMFdruNxy/owkWT5/P1yv1cc1pL+sT6wupPYcErkLrdbOjwhl7XwIA7IaJ9jRImIiIiIjWhpIabSM0xMzVCldQQEREREakzp9PJ3XffzTfffMOcOXNo3bp1lef79OmDp6cns2fPZsyYMQBs3ryZPXv2MHDgQCtCFqm5pA3w+3Ow/hvAada1HwlnPFDZbqrTBXDmI7DoNVj+HiStha9vgV//BQPvhl7XgpdftbvvER/CFX2bM3PZJtZPfYzethnYcg6ZJ31CoP+t0P82CIis73cqIiIipyAlNdxEWtlMjVA/T4sjERERERFxf+PHj+eTTz7hu+++IzAwsGJORnBwML6+vgQHB3PzzTczYcIEwsLCCAoK4u6772bgwIHVDgkXaRQOrjHJjI3fV65LOB+G/g1iex29fXAcjPy3eX7p/2DRG5C+B2bcD3Ofgf63mwSFX1jV12Xs43HvT3jM+z38c/PNuqDmMHA89L4evAPq7z2KiIjIKU9JDTdRPlMjVDM1RERERETq7PXXXwdg2LBhVda/++673HjjjQC8+OKL2O12xowZQ0FBASNHjuS1115r4EhFamD/CpPM2Dy9bIUNOl8IQ++H6G4nfr1vqNl24F2w8iPTRip9N8x52gz77nODSVjkZ8KCl2HtF/iVFoMNNpbG84nHJdx/+8ME+Vdf2SEiIiLiSkpquAnN1BARERERcR2n03nCbXx8fJg8eTKTJ09ugIhEasnphD2LYN4LsPWnspU26DrGVF5Edqr9Pj19TWVGn3Gw4VuYN8m0pVr0Gix+s3LIOECr0yk67W7G/+jFjsO5eP+2i3+c39kFb0xERETk+JTUcBOp5e2nlNQQERERERE5dRUXwvqvYdHrcHCVWWezQ7cr4PT7oFmHuh/D4QHdLjMJku2zTXJj1x+AzcziGPxXaN4HT+BR2yFufHcp7y3Yxdj+8bSLDKz78UVERESOQ0kNN5FWPihcMzVEREREREROPdnJsOwdWDYFspPMOg8f6H6FSTKEt3X9MW02aDfc3A5vAw9vCImvssmwjpEM7xTJLxsP8cQPG/jgpv7YbDbXxyIiIiJSRkkNN5GmmRoiIiIiIiKnnoNrYPEbsPYLKDH/LyQwBvrdYtpE+Yc3TBwR7Y751D/O68zvWw7zx9bD/LLxEOd0jmqYmEREROSUpKSGG3A6nRVJDc3UEBERERERaeJKS2DzDJPM2PVH5fq4PnDa/0Hni8DReKr4W0X4c/PprXl9znae/HEDp7ePwMfTYXVYIiIi0kQpqeEGsguKKSoxgwxVqSEiIiIiItJE5WfAyo/MUO703WadzQFdLoYBd0J8P0vDO567zmzH1yv2sSc1lynzdjL+zGNXdoiIiIjUhZIabiA918zT8PG04+ulq11ERERERESajOJC2D0PNv4Ia6ZCYbZZ7xtq2kv1uwWC46yNsQb8vT14aHQC905dzeTftjGmd3Oig32sDktERESaICU13EBqTlnrKVVpiIiIiIiIuL+cFNj6E2yZAdt+hcKsyueaJcBpd0K3K8DLz7oYT8LFPeP4cOFuVuxJ55kZG5k0tpfVIYmIiEgTpKSGG0gtm6cRoqSGiIiIiIhI/SotgX1LYdOPZq5FViI06whRXSCqq7mP7Ax+YTXfp9MJh7fA5umweSbsWwLO0srnA6Kgw0jocim0GQY2m8vfVkOw2Ww8cWFXLpw8j29XHeC6gS3p07IWn5OIiIhIDSip4QbSNSRcRERERESk/hTlwfbfYPM0k3TIPVz1+f3Lze1IQXFliY4jkh3h7SoHeJcUwe4FsGWmSY6k7az6+qhu0HEUdBgNsb3Abq+/99eAujUP5oo+8UxdtpfHv9/At+MH47C7Z5JGREREGiclNdxAao6ZqRGqpIaIiIiIiIhr5KSUJRymw/ZfoSi38jmfYGg/EhLOM1UayZsgaX3ZbR2k74HM/ea29afK1zm8zPaBsbBnERRkVH2u9VDoMMrcQuIb7r02sPtHdWT62oOs3Z/BF8v2MrZ/C6tDEhERkSZESQ03kFY2UyPUz9PiSERERERERNxY6g7YNN0kMvYsrNoCKjjeJDE6ngstB1VWXABEdoIul1Q+zs+AQxtNgqMi2bHBzMZIXGtuAH4Rpq1Uh1HQ9kzwDmyY92mxiABv7hnenqembeS5WZsZ3C6C+DD3mg8iIiIijZeSGm6gfKZGqGZqiIiIiIiI1I7TCRt/gN+frUw2lIvuBgnnm0RGdLeaz7LwCYYWp5lbudJSyNhjkhvpeyCuN8T1AbvDde/FjdwwqBWfLd3LtkPZjJz0Ow+OSuC601piVysqERERqSMlNdyAZmqIiIiIiIjUktNp2krN/hccXGXW2RzQanBZImM0hLiwLZLdDqGtzE3wdNh598Z+3PfFapbsTOWx79fz45oD/GdMd9o0C7A6PBEREXFjSmq4gdTy9lNKaoiIiIiIiJzYnsUmmbF7nnns6Q8Dx8Npd4JfmLWxnULiw/z47NbT+Gjxbp6ZsYmlu9IY/dIf3DeiAzcPaaMB4iIiInJSlNRwA2nlg8I1U0NEREREROTYEtfCr0+ZAeBghnP3uwWGTICAZtbGdoqy221cP7AVZ3aM5JFv1vLH1sM8PX0T09Ym8txl3ekQdWrMGRERERHXsVsdgJxYmmZqiIiIiIiIHFvKdvjyZnhjiElo2BzQ+3q4ewWMmqiERiMQH+bHBzf159kx3Qn08WD13nTOe/kPXp69laKS0hPvQERERKSMKjUaOafTWZHU0EwNERERERGRI2Tsh7n/gZUfgbPErOtyKZz5CES0tzY2OYrNZuOKfvEM7dCMf3y7ll82HuKFn7cwY52p2ugaF2x1iCIiIuIGVKnRyGUXFFNU4gRUqSEiIiIiIgJAzmGY9Xd4uReseN8kNNqPgNt/h8vfVUKjkYsO9uHt6/vy0tiehPp5svFgJhdNns9/Z22moLjE6vBERESkkVOlRiOXnmvmafh42vH1clgcjYiIiIiIuJWiPPD0tToK18jPhO2/mvZSG3+AwmyzvsUgOPtRaDnQ2vikVmw2Gxf1jGNwuwge+34909Yc5NXftjFzfSLPXtad3i1CrQ5RREREGiklNRq51Jyy1lOq0hARERERkdp6baBJarQaYm4th4B/uNVR1VzaLtg8E7bMgF3zobSo8rno7nD2Y9DubLDZLAtR6iYiwJvJV/fmgu4H+ce369l2KJsxry9g/LB23HtOBxx2/duKiIhIVUpqNHKpZfM0QpTUEBERERGR2shKhLSdZvnQBljyllmO7HJEkmNw40pylJbAvqWweYapyEjeVPX5sLbQcTR0GGVit6ujclMxqmsMp7UJ518/buDrFft59bdtrNybxstjexEe4G11eCIiItKIKKnRyKXlaEi4iIiIiIichMBouH877J4PO/+AXfMgeSMcWm9uS94020V1rZrk8Atr2DjzM2H7bFORsfUnyEutfM7mgBYDoeMok8jQrIwmLcTPixeu6MmwjpE89NUa5m9L4fxX5jH5mt5qRyUiIiIVlNRo5NLKZmqEKqkhIiIiIiK15R8BnS8yN4DsZJPk2DWvMsmRtM7cFr9htonqCvEDICASvAPBO8jc+wSVLR/x2NOv+tZPxQWQmwq5hyE3xQz2PvJxxboUOLy1alspn2Bod46pyGh7VsMnWcRyF/aIJSE6kDs+Ws6O5ByufHMh/zy/M9ed1hKbWo2JiIic8pTUaOTKKzVC/TwtjkRERERERNxeQDPocrG5wRFJjvJKjk2VSY6asDmqJj6KciAnBQqzahdXeDtTidFxtEmoOPT/n1Ndh6hAvhs/mAe/WsP0tYk8+t16VuxO4+lLu+HnpVMZIiIipzL9JdDIlc/UCNVMDRERERERcbWjkhyHTJLjwCooyDStoQqyzHJBVtnjspuzFJwlkJ9ubn9mc4BfeOXNv3w5ouxxhKnCCG0FYW0a6h2LGwn08WTy1b2ZMm8nE2ds4ttVB9h4MIvXr+1Nm2YBVocnIiIiFlFSo5FLz9VMDRERERERaSABkdDlEnM7HqcTinKPSHqUJT48/SoTGN7BGuQtdWaz2bjl9DZ0bx7C+E9WsDkpiwtfnc9/L+/OqK4xVocnIiIiFtBfmI1cann7KSU1RERERESksbDZwMsfgmKgWQdo3gfangktBkBEO/ANVUJDXKp/6zCm/WUI/VuHkV1QzB0freDp6RspLim1OjQRERFpYPors5FLyykbFK6ZGiIiIiIiInIKiwz04eNbBnDbUNOu7K3fd3D1/xZzKCvf4shERESkISmp0cilaaaGiIiIiIiICACeDjuPnNuJ16/pTYC3B0t2pnL+y/NYuivV6tBERESkgWimRiPmdDorkhqaqSEiIiIiIiJijO4WQ4foQO74cDlbD2Uz9q1FXDOgBS3C/Ajz9yI8wJtwfy/CA7wI8/fC28NhdcgiIiLiIkpqNGLZBcUUlTgBVWqIiIiIiIiIHKltswC+HT+Yh79ey/erD/DBwt3H3DbQ24OwAK+yREdlwqNFmB8X9YzDx1NJDxEREXehpEYjlp5r5mn4eNrx9dIfWCIiIiIiIiJH8vf24KWxPRnRJYrlu9NIzSkkJbuQlJxCUrILSM0ppLjUSVZBMVkFxexOyT1qH1Pm7eTFK3vSJTbYgncgIiIitaWkRiOWmlPWekpVGiIiIiIiIiLVstlsnN89lvO7xx71nNPpJDOvmJScgrJERyEpOQWkZhdyOLuAaWsT2ZKUzcWT5zPhnI7cNrQNDrvNgnchIiIiNaWkRiOWWjZPI0RJDREREREREZFas9lsBPt5EuznSZtmRz//l7Pb8/DXa/lpQxL/mbmJ3zYd4vkrehAf5tfwwYqIiEiN2K0OQI4tLUdDwkVERERERETqS3iAN29e14dnx3TH38vBkl2pjH7pD75avg+n02l1eCIiIlINJTUasbSymRqhSmqIiIiIiIiI1AubzcYV/eKZcc9Q+rQMJbugmPu+WM3/fbyi4mJDERERaTyU1GjEKio1/DwtjkRERERERESkaWsR7sfntw/k/pEd8bDbmLEukZGTfmfO5kNWhyYiIiJHUFKjEdNMDREREREREZGG47DbGH9mO74dP5h2kQEcyirgxneX8uh368grLLE6PBEREUFJjUYtPVczNUREREREREQaWte4YH68ewg3DmoFwAcLd3P+K3+wdl+GtYGJiIgIHlYHIMeWWtZ+SjM1RERERERERBqWj6eDxy/swlkJkfzti9VsT87hktfmc8/Z7TmrUySZecVk5BWRmVdEZn4RGXmVt8wjl/PNdt4OOy0j/GgV7k/rCH9ahfvTquxxmL8XNpvN6rcsIiLiFixPakyePJnnnnuOxMREevTowSuvvEL//v2PuX16ejp///vf+frrr0lNTaVly5ZMmjSJc889F4DHH3+cJ554osprOnbsyKZNm+r1fdSHtJyyQeGaqSEiIiIiIiJiiaEdmjHrr0P5x7frmLb2IM//vIXnf95S6/0UFpeybn8m6/ZnHvVcoI/HEYkOf1qXJTvaNAsg2FfnBERERI5kaVJj6tSpTJgwgTfeeIMBAwYwadIkRo4cyebNm4mMjDxq+8LCQs455xwiIyP58ssviYuLY/fu3YSEhFTZrkuXLvzyyy8Vjz08LM/dnJS0svZToZqpISIiIiIiImKZUH8vXr26F8NXRfLfWVsoLCklyMeDYF9Pgn09CSq7D/b1JMin6rogX7NdXmEJOw/nsCslh52Hc9mdksOuwzkcyMgnK7+YNfsyWPOn9lY2G5ydEMkNg1oxpF2EqjlERESwOKnxwgsvcOuttzJu3DgA3njjDaZNm8Y777zDQw89dNT277zzDqmpqSxYsABPT3OlQqtWrY7azsPDg+jo6HqNvb45nc6KpIZmaoiIiIiIiIhYy2azcUmv5lzSq/lJ76N9VOBR6/KLStidkluR8Nh1OKdiOSmzgF82HuKXjYdo28yf6we2Ykyf5gR4u+fFmyIiIq5g2W/BwsJCli9fzsMPP1yxzm63M3z4cBYuXFjta77//nsGDhzI+PHj+e6772jWrBlXX301Dz74IA6Ho2K7rVu3Ehsbi4+PDwMHDmTixIm0aNHimLEUFBRQUFBQ8Tgz8+hS0IaWXVBMUYkTUKWGiIiIiIiISFPl4+mgY3QgHaOPTnhsO5TNhwt38eXyfWxPzuGx79fz3KzNXNanOdcNbEnbZgEWRCwiImItu1UHPnz4MCUlJURFRVVZHxUVRWJiYrWv2bFjB19++SUlJSVMnz6df/7znzz//PM89dRTFdsMGDCA9957j5kzZ/L666+zc+dOTj/9dLKyso4Zy8SJEwkODq64xcfHu+ZN1kH5PA0fTzu+Xo4TbC0iIiIiIiIiTU27yACeuKgrix45mycu7EKbZv5kFxTz3oJdnP38XK6bsphfNiRRUuq0OlQREZEG41b1iqWlpURGRvLWW2/hcDjo06cP+/fv57nnnuOxxx4DYPTo0RXbd+/enQEDBtCyZUs+//xzbr755mr3+/DDDzNhwoSKx5mZmZYnNipaT6lKQ0REREREROSUFujjyQ2DWnHdaS2Zv/0w7y/YxexNh/hj62H+2HqY+DBfrj+tFVf0jSfYT4PFRUSkabMsqREREYHD4SApKanK+qSkpGPOw4iJicHT07NKq6lOnTqRmJhIYWEhXl5HJwBCQkLo0KED27ZtO2Ys3t7eeHt7n+Q7qR+pZUmNECU1RERERERERASw222c3r4Zp7dvxp6UXD5ctIupS/eyNzWPf0/fyPM/b+aSXnFc0D2WXi1C1flBRESaJMvaT3l5edGnTx9mz55dsa60tJTZs2czcODAal8zePBgtm3bRmlpacW6LVu2EBMTU21CAyA7O5vt27cTExPj2jdQz9JyNCRcRERERERERKrXItyPv5/XmcWPDGfipd1IiA4kv6iUT5fs5er/Lab7E7O47PUFPDtzE3M2HyK7oNjqkEVERFzC0vZTEyZM4IYbbqBv377079+fSZMmkZOTw7hx4wC4/vrriYuLY+LEiQDceeedvPrqq9xzzz3cfffdbN26laeffpq//OUvFfv829/+xgUXXEDLli05cOAAjz32GA6Hg6uuusqS93iy0nLNTI1QJTVERERERERE5Bh8vRxc1b8FY/vFs2RnKlOX7mX+9sMkZRawbHcay3an8dqc7TjsNrrEBjGgdRgDWofTr1WYWlWJiIhbsjSpceWVV5KcnMyjjz5KYmIiPXv2ZObMmRXDw/fs2YPdXllMEh8fz6xZs7j33nvp3r07cXFx3HPPPTz44IMV2+zbt4+rrrqKlJQUmjVrxpAhQ1i0aBHNmjVr8PdXFxWVGvoDQ0REREREREROwGazMaBNOAPahON0OtmTmsvinaks2ZnK4p0p7E3NY82+DNbsy+DtP3Zis0FCdHmSI4y2kQEUlZRSVOKksLiUopJSCktKK5aLypYLj3jeBnSMDqRnfMgp2T67qKSUXzcd4otle9mdkku7yAA6xQSREB1Ip5ggmof6YrPZrA5TRKTJsTmdTqfVQTQ2mZmZBAcHk5GRQVBQkCUxPPLNWj5ZvId7zm7Pved0sCQGEREREZGaaAx/PzdG+lxEpDE5kJ5XluAwSY4dyTku3X/rCH96NA+mZ3wIPVuE0ikmEG+PpjnTY3tyNp8v28tXy/dzOLvgmNsFenuQEBNIQnSQSXbEBJIQHYifl6XXGIuINFo1/ftZP0UbqfRczdQQEREREREREdeIDfHl4l5xXNwrDoDkrAKW7Exlyc4UFu9MJTEzHy+HHU+HHW8Pc+/lYcfTYatYLn/eq+z5wpJS1u3PYOfhnIrbt6sOAODlsNMpNohe8SH0iA+mZ3worcL9aly54HQ6KSpxkldUQkFRCQ67jTB/L8sqH3ILi5m25iCfL9vL0l1pFesjArwY07s5A9qEsf1QDhsTM9l4MItth7LIKihm6a60KtvbbNAyzI9OMSbRcUaHZnRvHqyKDhGRWlBSo5FKLWs/pZkaIiIiIiIiIuJqzQK9Oa97DOd1j6nzvtJzC1m9L4NVe9JZtTeN1fsySM0pZPXedFbvTa/YLtjXkx7xIQT6eFBQVEJ+USl5RSXkV9xKyS8qqVhX+qfeIt4eduJCfIkN8a24jw3xIS7UPI4O9nFpdYjT6WT1vgymLt3LD6sPVAxbt9tgWMdIruwXz1kJkXg6TOv0sxIqX1tUUsr25Gw2Hcxi48FMNhzMZFNiFslZBexKyWVXSi4z1iXyws9biAvx5dxu0YzuFkPP5iHY7UpwuFpyVgELd6SwcHsKeYXFtAz3p3WEP60i/Gkd7q/5MiJuRkmNRiotp2xQuH6oioiIiIiIiEgjFuLnxRkdmnFGBzPP1Ol0sjc1j5V701i9N4NVe9NYdyCTjLwift+SXOv9221Q6oSC4lJ2HM5hx+HqW2fZbNAswLsi6RET7EOovxeBPh4E+XgS5GvuA49Y9vNyHFUlkZZTyDcr9zN16V42J2VVrG8Z7scVfeMZ07s50cE+x43Z02EnITqIhOigiuoYgMPZBRWJjpV70/htUzL70/N4+4+dvP3HTmKCfRjVNZpzu8XQp0WoJQkOp9NJSk4hSZn5BPt6EhXkU5G4cRfpuYUs2pHKwu2HWbA9ha2Hso+7faifZ0WCo2W4P60i/CqSHkE+OjdXUFzC1qRs1h/IICu/mLgQX+LD/Gge6kuwr6cqjepRbmExK/ek06aZPzHBvlaH02hopkY1GkPv2/7//oVDWQX8ePcQusYFWxKDiIiIiEhNNIa/nxsjfS4iIpUKi0vZnJjFmv3pFBaX4uvpwMfTgY+nvezeUWWdr6cD77Jlr7JWV0kZBexLz+VAej4H0vPYn5bHgYw89pctFxSX1jouh91WJenh6+lg9d4MCkvMvrw97IzuGs2V/VowoHWYy5MMeYUlzN1yiOlrE5m9MYmcwpKK5yIDvRnd1VRw9GsVhsNFxy4tdXI4u4B96XnsS8tjX1ou+9OOWE7PI7+o8rO02UwsMcGmMiYm2CSMYssSR3EhvkQEeJ/wsykuKSW3qIScgmJyCkrILay8LypxEurnSZi/F2H+XoT4edXq/WYXFLN0VyoLt6ewYPth1h/I5M9nPDvHBDGobThhAV7sOpxjKmYO53Ao69hzUQDC/b1oEe5HZKA3EQFlt0BvmgV4VTxuFuiNv3fTuHY8p6CYjQczWX8gk/UHMli3P5Oth7IoKqn+FHKAtwfNQ33Lbn5V7uND/Qjy9VDSo5acTifLdqfxxbK9TFtzsOLnQkJ0IGd0bMawDpH0bRXqdsnGmqjp389KalTD6v98OJ1OOvxjBkUlThY8dBaxIcrCiYiIiEjjZfXfz42VPhcRkYbjdDpJzSlkf3qeSXik53MwPY/M/CIy84rJKjD3mflFZOUXk5lXRPGf+1sdoWtcEFf2jefCnnEE+zbMlfr5RSX8sfUwM9Ye5OcNSWSVtbsCM7tjZBdTwdE+MqBK6668P7Xvyq9YX/k4I6+oIvmzLz2PwhMkgGw2czI/I6/omCezj+TpsBEV5ENssC/ennZyCorJLSwhpyxxkVNQXKukk80GIb6ehPp7Ee7vRaifF+EB5r488eHv7cHafRks2H6Y1fsyKPnTv2e7yAAGtQ1nYJtwBrQJP+bc2pyCYnal5LDrcG7ZfQ67UnLYeTj3uIPg/8zX00FEYGWiIyLAGw+7jeJSJyWlpWX3TopLnZSW3ZdU3JdSXOKk1OnEbrPh7+2Bn5eDAG8P/Lw8CPB24Oftgb+3B/5ejrJ7D/y9HRXbejrsOOw2POy2svvKx8dKOKXlFFYmL8rudx7OOSohBKZ9XNe4IEL9vNhflhBLPkFCCCDQ24PmYX50iwuiV4tQerUIoX1koMuSdE3JwYw8vl6xny+X72PnERVpEQHepOQUVPl3CfD2YEi7CIZ1bMawjpEnrB5zF0pq1IHV//nIyi+i2+M/AbDxX6Pw9XJdP0gREREREVez+u/nxkqfi4hI4+V0miHk5QmOyuRHMW2b+dMl1tquGQXFJczfdpjpaxP5aX0imfnFJ35RLdhtEBNs2nSVX2Ufd8QV9jHBvnh52E1VR04BB9PzOZiRx4Ej7g9k5HEwPZ9DWflHzT85HofdVnlivuwkvd1uIyO3iJScQjLyik7qPcWH+TKoTQSD2plERmRQ3U/yZuUXsTsll72pJsGRnF3I4ewCDmcVmPvsQpKzCsgrKjnxzixks3FUssNmg/Tc6j/r6CAfusQGmVtcMF1ig4gL8T2q4iK/qIT96XnsTc0tq/Qx1T7l94ezC6vdv7+Xgx7xIfRqEUKv+FB6tgghIsDb5e+7XHFJKQXF5bcSCoqOWC42CaW4EPM90NDJlvyiEn7ekMQXy/cxb2tyxfeSn5eD87rFcHnfePq1CiU9t4jftyYzd3Myc7ckk5JT9bNNiA5kWMdIhnVsRp+W7lvFoaRGHVj9n489KbkMfe43fDztbHpydIMfX0RERESkNqz++7mx0uciIiKuUFhcysIdKcxYe5CfNiSRlltY0arLtOmyV9u6y7e8vZeHgwAfj7IEhklaRAe7bk5GcUkpSVkFHEzP40BGPsUlpRWVBH7eDnNfXnXg7cDLYT9uO6LiklLScotIyy0kJbvQ3OcUkpZTSOoRt4y8ItpFBjCwrBojPszPJe/nZOQUFJclOQpIzjKJj5TsQkqdTpNMcJRXUNgrKicqkwzmvny5uNRJbkF5lUsxOYWm0iW7oLjq+iOWcwtLKqo+aqtVuB9dYoPpEhdk7mODXJZgyCssYX96LtuTc1i9N52Ve9JZvS+d3MKjk0AtwvzoWZ7oaBFK55ggvDzsFBaXkpFXRHpuIel5RaTnli3nFpGeV3Z/xHJ2QXFZ0qKkIpHx5yqeY/Fy2GkZ7kebZv60aRZA6wh/2jbzp01EAKHHqPQ5GU6nk3X7M/li+V6+W3WgSiKvf+swLu/TnHO7xRyzpVlpqZO1+zOYszmZOVsOsWpvepUqjkBvDwa3i6BT2Wfo5WHHy2E7YtlxxPKf7j3sRFrYTk1JjTqw+j8fq/emc9Hk+cQG+7Dg4bMb/PgiIiIiIrVh9d/P9W3y5Mk899xzJCYm0qNHD1555RX69+9/wtc19c9FREQaXvlpPM0okOo4nU5KnVBcWlrZ2qrkyFZXR6wvdRId7NPgg9BLSp1sPZTFyj3prNyTxso96dUOcvdy2PF02KrMmXEFL4cdbw873p52vD0ceHuY5N6J2rKF+nnSOsIkO9o086dNhD/BvibR4eSI0+vOKndm+YgHmxIz+XL5PjYlZlWsiwn2YUzv5lzWpzmtIvxr/Z5Scwr5Y2syc8qqOFJzqq+QqalXrurFBT1i67SPk1XTv5+bxgSbJiY113zhhfi5LgMoIiIiIiK1N3XqVCZMmMAbb7zBgAEDmDRpEiNHjmTz5s1ERkZaHZ6IiJxilMyQ47HZbDhs4LA33lb2DruNhOggEqKDuKp/CwAy84tYszfDJDn2mmRHWm4R5fkMm83M9Ajx9STYz4tQP7Mc4udFsK+neeznRbCfJ0E+Hnh7mCql8qSFt4epaPJy2I85X6Sk1MmB9Dx2HM5hR3I2O5Jz2Fm2fCAj31QP7UlnxZ50l3wOXh52RnaJ5vI+zRncLqJOba/C/L24qGccF/WMo6SsiuP3LckkZuZTWFxaeSsppaisFdeR66o8X1yKr2fj/fopp0qNalh9RdXvW5J5evpG2kcF8spVvRr8+CIiIiIitWH138/1acCAAfTr149XX30VgNLSUuLj47n77rt56KGHjvvapvy5iIiIiNQXp9PJvrQ8SkqdhPp5EejjccxkREPILSxm1+Fcdhw+ItlxOIfcgspZN0fmG23YjlpXLtjXk/N7xHJh91iC/Rq2SsYdqFLDjQ3t0IyhHZpZHYaIiIiIyCmtsLCQ5cuX8/DDD1ess9vtDB8+nIULF1oYmYiIiEjTZbPZLJ2R8md+Xh50jg2ic6wuUmkslNQQERERERGpxuHDhykpKSEqKqrK+qioKDZt2nTU9gUFBRQUFFQ8zszMrPcYRURERERONXarAxAREREREWkKJk6cSHBwcMUtPj7e6pBERERERJocJTVERERERESqERERgcPhICkpqcr6pKQkoqOjj9r+4YcfJiMjo+K2d+/ehgpVRERExjyCxQAADpVJREFUROSUoaSGiIiIiIhINby8vOjTpw+zZ8+uWFdaWsrs2bMZOHDgUdt7e3sTFBRU5SYiIiIiIq6lmRoiIiIiIiLHMGHCBG644Qb69u1L//79mTRpEjk5OYwbN87q0ERERERETklKaoiIiIiIiBzDlVdeSXJyMo8++iiJiYn07NmTmTNnHjU8XEREREREGoaSGiIiIiIiIsdx1113cdddd1kdhoiIiIiIoJkaIiIiIiIiIiIiIiLiJpTUEBERERERERERERERt6CkhoiIiIiIiIiIiIiIuAUlNURERERERERERERExC0oqSEiIiIiIiIiIiIiIm5BSQ0REREREREREREREXELSmqIiIiIiIiIiIiIiMj/t3fvoVXXfxzHX985z9nFbe6SZ5s6nbjmJTbwNg8WkpN0xUgzshgxLRDxbMxEiCTbRoJS0MWwFV3sj1JrwswiM1u2SJyuyWzGHBqCwpxLQp2jqXg+vz/Eg+d4frbfD7fv+e48H3Bg5/s9zvdhLw578d73HEdgqQEAAAAAAAAAAByBpQYAAAAAAAAAAHAElhoAAAAAAAAAAMARWGoAAAAAAAAAAABHYKkBAAAAAAAAAAAcgaUGAAAAAAAAAABwhFi7B4hExhhJ0pUrV2yeBAAAAIh8t39vvv17NG6hVwAAAAADN9BewVIjjN7eXknS+PHjbZ4EAAAAcI7e3l6lpKTYPUbEoFcAAAAA/7t/6xWW4c+p7uL3+9XV1aWkpCRZlmXLDFeuXNH48eN17tw5JScn2zIDIguZQDjkAqHIBMIhFwh1vzNhjFFvb6+ys7MVE8M73N5Gr0CkIhcIRSYQDrlAKDKBcO5nLgbaK7hSI4yYmBiNGzfO7jEkScnJybxIIAiZQDjkAqHIBMIhFwh1PzPBFRp3o1cg0pELhCITCIdcIBSZQDj3KxcD6RX8GRUAAAAAAAAAAHAElhoAAAAAAAAAAMARWGpEKLfbrerqarndbrtHQYQgEwiHXCAUmUA45AKhyET04GeNcMgFQpEJhEMuEIpMIBw7csEHhQMAAAAAAAAAAEfgSg0AAAAAAAAAAOAILDUAAAAAAAAAAIAjsNQAAAAAAAAAAACOwFIjAm3btk0TJ05UXFycioqKdPToUbtHwhD65ZdfVFpaquzsbFmWpT179gSdN8botddeU1ZWluLj47Vw4UKdOnXKnmExJDZv3qzZs2crKSlJY8aM0ZIlS9TZ2Rn0mP7+fvl8PqWnp2vUqFFatmyZLly4YNPEGAp1dXUqKChQcnKykpOT5fV6tW/fvsB5MoEtW7bIsiytXbs2cIxcRJ+amhpZlhV0mzJlSuA8mRj+6BbRi16BUPQKhEOvwL+hV0CKvF7BUiPCfPnll1q3bp2qq6t17NgxFRYWatGiRerp6bF7NAyRvr4+FRYWatu2bWHPv/HGG9q6das++OADHTlyRImJiVq0aJH6+/uHeFIMlaamJvl8PjU3N+vAgQO6ceOGHnvsMfX19QUe89JLL+mbb75RfX29mpqa1NXVpaeeesrGqTHYxo0bpy1btqi1tVW//fabFixYoCeffFJ//PGHJDIR7VpaWvThhx+qoKAg6Di5iE7Tp0/X+fPnA7dff/01cI5MDG90i+hGr0AoegXCoVfgXugVuFNE9QqDiDJnzhzj8/kC92/evGmys7PN5s2bbZwKdpFkGhoaAvf9fr/JzMw0b775ZuDYpUuXjNvtNjt37rRhQtihp6fHSDJNTU3GmFsZGDlypKmvrw88pqOjw0gyhw8ftmtM2CA1NdV8/PHHZCLK9fb2mry8PHPgwAEzf/58U1VVZYzhtSJaVVdXm8LCwrDnyMTwR7fAbfQKhEOvwH9Dr4Ax9AoEi7RewZUaEeT69etqbW3VwoULA8diYmK0cOFCHT582MbJECnOnDmj7u7uoIykpKSoqKiIjESRy5cvS5LS0tIkSa2trbpx40ZQLqZMmaKcnBxyESVu3rypXbt2qa+vT16vl0xEOZ/PpyeeeCLo5y/xWhHNTp06pezsbE2aNEllZWU6e/asJDIx3NEtcC/0Ckj0CtyNXoE70SsQKpJ6ReygfFf8Xy5evKibN2/K4/EEHfd4PDp58qRNUyGSdHd3S1LYjNw+h+HN7/dr7dq1mjdvnh566CFJt3Lhcrk0evTooMeSi+Gvvb1dXq9X/f39GjVqlBoaGjRt2jS1tbWRiSi1a9cuHTt2TC0tLXed47UiOhUVFemzzz5Tfn6+zp8/r9raWj3yyCM6ceIEmRjm6Ba4F3oF6BW4E70CoegVCBVpvYKlBgA4iM/n04kTJ4LetxDRKz8/X21tbbp8+bJ2796t8vJyNTU12T0WbHLu3DlVVVXpwIEDiouLs3scRIiSkpLA1wUFBSoqKtKECRP01VdfKT4+3sbJAAB2olfgTvQK3IlegXAirVfw9lM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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the model"
      ],
      "metadata": {
        "id": "DAiLdZRUEgpE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "torch.save(model.state_dict(), \"NoahBert.pth\")\n",
        "files.download('NoahBert.pth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "DourCxtcElP9",
        "outputId": "f9a785f4-4ea2-4f5d-93a1-a73e588bb847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ae686a96-7c25-4a44-9940-5a4bc63e00c6\", \"NoahBert.pth\", 438802475)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating the model"
      ],
      "metadata": {
        "id": "pKnrjSMWEkCv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "# If notebook disconnects during training, then here, first load the best model we saved , then check validation performance\n",
        "\n",
        "test_accuracy, test_loss, incorrect_predictions, pred_df = get_validation_performance(test_set, model, batch_size, device)\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPpUVToDEo-6",
        "outputId": "3e4d3f7e-25e9-48fe-933e-86d1be50ae50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of correct predictions = 688\n",
            "0.7652947719688543\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Error Analysis"
      ],
      "metadata": {
        "id": "fZB1G4LsEoTP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "\n",
        "seed_everything()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "## print out up to 5 test set examples (or adversarial examples) that model gets wrong\n",
        "print(\"Number of incorrect predictions:\", len(incorrect_predictions))\n",
        "random.shuffle(incorrect_predictions)\n",
        "\n",
        "if len(incorrect_predictions) == 0:\n",
        "    print(\"All predictions are correct!\")\n",
        "else:\n",
        "    print(\"Below are 5 random incorrect predictions:\")\n",
        "    num_to_print = min(5, len(incorrect_predictions))\n",
        "    random_indices = np.random.choice(len(incorrect_predictions), size=num_to_print, replace=False)\n",
        "    for i in random_indices:\n",
        "        text = incorrect_predictions[i][0]\n",
        "        print(f'Text: {text}')\n",
        "        print(\"Predicted label:\", incorrect_predictions[i][1])\n",
        "        print(\"Correct label:\", incorrect_predictions[i][2])\n",
        "        print(\"------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0in_DZWEtrP",
        "outputId": "f6b0fbd4-4dcf-43c7-d813-4a88f0525525"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of incorrect predictions: 211\n",
            "Below are 5 random incorrect predictions:\n",
            "Text: @ stephdamartian they made up endearing names for us like nigger spook & amp ; monkey\n",
            "Predicted label: 2\n",
            "Correct label: 0\n",
            "------------------------------\n",
            "Text: @ user nah b i dont fuck with those ching chong ling long phones\n",
            "Predicted label: 0\n",
            "Correct label: 2\n",
            "------------------------------\n",
            "Text: the family of ron o'neal needs to sue wu - tang for that trash song they released in his name.\n",
            "Predicted label: 2\n",
            "Correct label: 1\n",
            "------------------------------\n",
            "Text: @ joebiden is such a retard! & # 8220 ; @ irritatedwoman : biden calls himself & # 8216 ; white boy & # 8217 ; in meeting with black pastors http : / / t. co / bqx1iev0wd & # 8221\n",
            "Predicted label: 2\n",
            "Correct label: 0\n",
            "------------------------------\n",
            "Text: we should just cancel the shithole countries and just give it to trump\n",
            "Predicted label: 1\n",
            "Correct label: 0\n",
            "------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Robustness Test"
      ],
      "metadata": {
        "id": "xqbIpcyk9wUX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Misspelling"
      ],
      "metadata": {
        "id": "8b72kR-O90UO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "test_accuracy, test_loss, incorrect_predictions, pred_df = get_validation_performance(test_set, model, batch_size, device)\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hi0dsILU91RC",
        "outputId": "3e256a39-8afc-4672-9f80-ffe725370c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of correct predictions = 673\n",
            "0.7186095661846496\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Noise"
      ],
      "metadata": {
        "id": "vh6VBJT690zG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "test_accuracy, test_loss, incorrect_predictions, pred_df = get_validation_performance(test_set, model, batch_size, device)\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qpnoq4pZ91rl",
        "outputId": "f20321f5-b7b0-4b34-aa4d-6afecfbaf936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of correct predictions = 669\n",
            "0.6941601779755283\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grammar Noise"
      ],
      "metadata": {
        "id": "PZbgAoRK92At"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "test_accuracy, test_loss, incorrect_predictions, pred_df = get_validation_performance(test_set, model, batch_size, device)\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mkeoJaZ92YV",
        "outputId": "95214431-a7cb-47aa-9d93-e2d275a1e124"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of correct predictions = 671\n",
            "0.70638487208008898\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mislabelling"
      ],
      "metadata": {
        "id": "r-moBV_v92_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed_everything()\n",
        "\n",
        "test_accuracy, test_loss, incorrect_predictions, pred_df = get_validation_performance(test_set, model, batch_size, device)\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8boTmX593Ss",
        "outputId": "27a33950-425d-449c-f7d8-b4cc3de0c796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of correct predictions = 541\n",
            "0.59177975528364849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Robustness Result Graph"
      ],
      "metadata": {
        "id": "53Zg2LQOXoQT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data\n",
        "categories = ['Misspellings', 'Noise', 'Grammar Noise', 'Mislabelling']\n",
        "accuracy = [71.86, 69.41, 70.63, 59.17]\n",
        "threshold_accuracy = 76.43\n",
        "\n",
        "# Define colors\n",
        "bar_colors = ['skyblue', 'lightgreen', 'lightcoral', 'lightpink']\n",
        "\n",
        "# Create bar graph\n",
        "fig, ax = plt.subplots()\n",
        "bars = ax.bar(categories, accuracy, color=bar_colors, alpha=0.7, label='Accuracy')\n",
        "ax.axhline(y=threshold_accuracy, color='red', linestyle='--', label='Threshold Accuracy')\n",
        "\n",
        "# Title and labels\n",
        "plt.title('Robustness Test on NoahBERT')\n",
        "plt.xlabel('Robustness Test')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "\n",
        "# Add data labels\n",
        "for bar, val in zip(bars, accuracy):\n",
        "    ax.text(bar.get_x() + bar.get_width() / 2, val + 1, f'{val:.2f}%', ha='center', va='bottom', color='black')\n",
        "\n",
        "# Adjust layout to make room for the legend\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show legend with adjusted position\n",
        "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "XJk2NGEPWqv_",
        "outputId": "4da65d58-0346-4f82-e24d-50b58a6f185e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}