sdg_bert_classification_domain_adaptation.ipynb

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        "<a href=\"https://colab.research.google.com/github/ELehmann91/NLP/blob/master/sdg_bert_classification_domain_adaptation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train an SDG Classifier using the Hugging Face (Hub) Training Notebook and annotated Data from OSGD (https://osdg.ai/)"
      ],
      "metadata": {
        "id": "okAor0oihKeA"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkKjHOVDxaOz"
      },
      "source": [
        "In this notebook we'll take a look at fine-tuning a multilingual Transformer model called [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) for text classification. By the end of this notebook you should know how to:\n",
        "\n",
        "* Load and process a dataset from the Hugging Face Hub\n",
        "* Create a baseline with the zero-shot classification pipeline\n",
        "* Fine-tune and evaluate pretrained model on your data\n",
        "* Push a model to the Hugging Face Hub\n",
        "\n",
        "Let's get started!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9kCiZKcnjKQz"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! pip install datasets transformers sentencepiece huggingface_hub\n",
        "! apt install git-lfs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlaIZxM9jJ6f"
      },
      "source": [
        "To be able to share your model with the community there are a few more steps to follow.\n",
        "\n",
        "First you have to store your authentication token from the Hugging Face website (sign up here if you haven't already!) then execute the following cell and input your username and password:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "PdTqjaIvTg-g"
      },
      "outputs": [],
      "source": [
        "!git config --global credential.helper store"
      ]
    },
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        },
        "id": "lk956I0ByT0J",
        "outputId": "f9d0d408-a83c-4066-d582-9416d1f0df02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global credential.helper store"
      ],
      "metadata": {
        "id": "EhFWH8kdbap3"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Drcw9oC2yn_e"
      },
      "source": [
        "## The dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "6XHoLWDsyavx"
      },
      "outputs": [],
      "source": [
        "#In this notebook we'll be using the 🤗 Datasets to load and preprocess our data. If you're new to this library, check out the video below to get some additional context:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "kufi1lg8IkQv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df_osdg = pd.read_csv('https://zenodo.org/record/5550238/files/osdg-community-dataset-v21-09-30.csv',sep='\\t')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_osdg.shape)\n",
        "df_osdg[:3]"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 161
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        "id": "OaVhLF69Id5z",
        "outputId": "46b4adde-39c7-4fe8-c83a-c82effe6a4a0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32120, 7)\n"
          ]
        },
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          "output_type": "execute_result",
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              "                          doi                           text_id  \\\n",
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              "1        10.18356/eca72908-en  00028349a7f9b2485ff344ae44ccfd6b   \n",
              "2  10.1787/9789264289062-4-en  0004eb64f96e1620cd852603d9cbe4d4   \n",
              "\n",
              "                                                text  sdg  labels_negative  \\\n",
              "0  From a gender perspective, Paulgaard points ou...    5                1   \n",
              "1  Labour legislation regulates maximum working h...   11                2   \n",
              "2  The average figure also masks large difference...    3                1   \n",
              "\n",
              "   labels_positive  agreement  \n",
              "0                7   0.750000  \n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "(32120, 7)\n"
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              "                          doi                           text_id  \\\n",
              "0  10.6027/9789289342698-7-en  00021941702cd84171ff33962197ca1f   \n",
              "1        10.18356/eca72908-en  00028349a7f9b2485ff344ae44ccfd6b   \n",
              "2  10.1787/9789264289062-4-en  0004eb64f96e1620cd852603d9cbe4d4   \n",
              "\n",
              "                                                text  sdg  labels_negative  \\\n",
              "0  From a gender perspective, Paulgaard points ou...    5                1   \n",
              "1  Labour legislation regulates maximum working h...   11                2   \n",
              "2  The average figure also masks large difference...    3                1   \n",
              "\n",
              "   labels_positive  agreement  \n",
              "0                7   0.750000  \n",
              "1                1   0.333333  \n",
              "2                6   0.714286  "
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              "      <td>10.6027/9789289342698-7-en</td>\n",
              "      <td>00021941702cd84171ff33962197ca1f</td>\n",
              "      <td>From a gender perspective, Paulgaard points ou...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0.750000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.18356/eca72908-en</td>\n",
              "      <td>00028349a7f9b2485ff344ae44ccfd6b</td>\n",
              "      <td>Labour legislation regulates maximum working h...</td>\n",
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              "      <td>0004eb64f96e1620cd852603d9cbe4d4</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ],
      "source": [
        "print(df_osdg.shape)\n",
        "df_osdg[:3]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_osdg = df_osdg[df_osdg['agreement']>.95]\n",
        "print(df_osdg.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZKuRdYyGOT-",
        "outputId": "cbf15119-0c75-472a-ecbb-30ce4bbdfc0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(14754, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SUXtp-YTJeY",
        "outputId": "1d0a7ece-902e-4e66-ac63-1a6b09d1c9cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "These studies examined the increasing participation levels of Asian women in business ownership. An interesting finding of the researches in the United Kingdom (Dhaliwal, 1998 and 2007; Ram and Jones, 1998) is that Asian women are not strongly represented in self-employment because there is a tendency for some women entrepreneurs to be “invisible” and their existence unacknowledged. These “hidden” women (Dhaliwal, 1998) say it is their husband, father or brother who run the business and they are masking the extent of their role even if they are playing a pivotal role in the management of the business.\n",
            "5\n",
            "For the case studies, national definitions of smallholder will be considered. These definitions emphasise income sources and the ability of farm households to transition either into commercially successful farm operations or out of farming altogether. Sometimes the definition includes a minimum share of farm labour that is contributed by the farmer’s family (FAO, 2014).\n",
            "2\n",
            "The existence or creation of linkages between policy areas would provide a coherent institutional architecture and enhance water policy efficiency. It would also ensure that investment plans better reflect basin priorities and that public expenditures across sectors are better aligned with water policy objectives. The potential for policy coherence at the watershed level could be improved through the systematic involvement of river basin organisations and councils; this is an area that needs to be further exploited. In particular, the Agenda foresees the creation of a Ministry of Land Use Planning for a long-term land-use planning strategy; a National Observatory of Sustainable Land Use Planning; and a National Development Planning Institute to ensure the long-term harmonisation among different sectoral and territorial development policies in Mexico. Such tools would help move from traditional ad hoc bilateral discussions between CONAGUA and other agencies (CONAFOR, SEDESOL, SAGARPA, etc.) The current institutional set-up for dealing with climate change issues might serve as an example to mobilise key institutions of the federal public administration towards policy coherence (see Box 1.10).\n",
            "6\n",
            "A tailored approach may be required as basins are faced with specific challenges and are endowed with distinctive capacities. Building on institutions such as CONAGO, IMTA and ANEAS, there is ample room to collect, review and benchmark success stories, innovative mechanisms or institutional organisations in support of effective decision making, and better water policies at all levels. This is concrete implementation of the commitments related to human rights, anticorruption and democratic governance mentioned in the Pact.\n",
            "6\n"
          ]
        }
      ],
      "source": [
        "for i in range(101,105):\n",
        "  print(df_osdg.iloc[i]['text'])\n",
        "  print(df_osdg.iloc[i]['sdg'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YqzPdpMTcxH",
        "outputId": "35d7fcaa-b9d7-4c54-e2cf-e0ba3828ba6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ],
      "source": [
        "df_osdg.iloc[3]['sdg']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UrdGBehUUqH",
        "outputId": "a0542e93-c7fd-4000-c18d-a17c88f2e9d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5     2607\n",
              "4     2438\n",
              "3     1845\n",
              "7     1664\n",
              "6     1457\n",
              "11    1242\n",
              "1     1239\n",
              "13    1176\n",
              "2      970\n",
              "8      843\n",
              "14     714\n",
              "9      652\n",
              "15     539\n",
              "10     443\n",
              "12     237\n",
              "Name: sdg, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "df_osdg['sdg'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of2mM-hRbf2i",
        "outputId": "65e54d16-5b33-46aa-c78d-cc58493c6f50"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "en    18023\n",
              "r3       19\n",
              "jb       11\n",
              "d8        4\n",
              "lt        3\n",
              "9x        3\n",
              "bn        2\n",
              "q6        1\n",
              "Name: lang, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "df_osdg['lang'] = [te[-2:] for te in list(df_osdg['doi'])]\n",
        "df_osdg['lang'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iz4X18xhbfih"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Owm_syWPy3i"
      },
      "outputs": [],
      "source": [
        "len_lst = []\n",
        "for t in range(0,2000):\n",
        "  text = df_osdg.iloc[t]['text']\n",
        "  len_lst.append(len(text.split()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JzeFUq6RD3p",
        "outputId": "6b98040e-4e67-4601-e04d-133e3f3dcfee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134.0"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ],
      "source": [
        "import numpy as np\n",
        "np.quantile(len_lst,.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JFXFeX-jm372"
      },
      "outputs": [],
      "source": [
        "#df_osdg['label'] = df_osdg['sdg']\n",
        "#df_osdg['sdg'] = [str(a) for a in list(df_osdg['label'])]\n",
        "\n",
        "df_osdg['sdg'] = df_osdg['sdg'].apply(lambda x: 'sdg_'+str(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JHr4lvx8nMVA",
        "outputId": "0334c950-349f-446c-d4e3-b573c161cd6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       labels_negative  labels_positive     agreement\n",
              "count     18066.000000     18066.000000  18066.000000\n",
              "mean          0.663290         4.836046      0.951245\n",
              "std           6.207737        15.264043      0.104111\n",
              "min           0.000000         0.000000      0.636364\n",
              "25%           0.000000         3.000000      1.000000\n",
              "50%           0.000000         4.000000      1.000000\n",
              "75%           0.000000         6.000000      1.000000\n",
              "max         777.000000       864.000000      1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-69d0a5ad-28e6-4837-a59d-f1b328573ac4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>labels_negative</th>\n",
              "      <th>labels_positive</th>\n",
              "      <th>agreement</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18066.000000</td>\n",
              "      <td>18066.000000</td>\n",
              "      <td>18066.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.663290</td>\n",
              "      <td>4.836046</td>\n",
              "      <td>0.951245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.207737</td>\n",
              "      <td>15.264043</td>\n",
              "      <td>0.104111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.636364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>777.000000</td>\n",
              "      <td>864.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69d0a5ad-28e6-4837-a59d-f1b328573ac4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-69d0a5ad-28e6-4837-a59d-f1b328573ac4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-69d0a5ad-28e6-4837-a59d-f1b328573ac4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "df_osdg.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nahKuHGLknpA",
        "outputId": "cf9ba245-682b-46ab-c9a3-e17855deaa4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sdg_5     2607\n",
              "sdg_4     2438\n",
              "sdg_3     1845\n",
              "sdg_7     1664\n",
              "sdg_6     1457\n",
              "sdg_11    1242\n",
              "sdg_1     1239\n",
              "sdg_13    1176\n",
              "sdg_2      970\n",
              "sdg_8      843\n",
              "sdg_14     714\n",
              "sdg_9      652\n",
              "sdg_15     539\n",
              "sdg_10     443\n",
              "sdg_12     237\n",
              "Name: sdg, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "df_osdg['sdg'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CUFpeSukI7sJ"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "dataset =  Dataset.from_pandas(df_osdg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYAXCSdzkDQn",
        "outputId": "90ee3c08-22fe-4fce-b029-6be818c857db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['doi', 'text_id', 'text', 'sdg', 'labels_negative', 'labels_positive', 'agreement', 'lang', '__index_level_0__'],\n",
              "    num_rows: 18066\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hS-mUiejF-T",
        "outputId": "7f16c51c-63e4-4509-adcb-f9f178272153"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'__index_level_0__': 173,\n",
              " 'agreement': 1.0,\n",
              " 'doi': '10.1787/5km35m63qqvc-en',\n",
              " 'labels_negative': 0,\n",
              " 'labels_positive': 7,\n",
              " 'lang': 'en',\n",
              " 'sdg': 'sdg_8',\n",
              " 'text': 'Next, employment and welfare officers develop an individual action plan for the client through a joint interview. The client is registered for work at the PES office and is referred to vacancies once his or her job preparation is completed. The Employment Support Programme for welfare recipients (cont.)',\n",
              " 'text_id': '01942c36ba48e886d31bd25be57fb6d6'}"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ],
      "source": [
        "dataset[100]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7RNOHYp30-RP"
      },
      "source": [
        "## Mapping Labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bwky2OLs1D_s"
      },
      "source": [
        "During training, 🤗 Transformers expects the labels to be ordered, starting from 0 to N. But we've seen that our star ratings range from 1-5, so let's fix that. While we're at it, we'll create a mapping between the label IDs and names, which will be handy later on when we want to run inference with our model. First we'll define the label mapping from ID to name:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dz_epm6j5xw_",
        "outputId": "4df23ef1-c5e5-42b8-8c6c-31a4b77cff1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['sdg_1', 'sdg_10', 'sdg_11', 'sdg_12', 'sdg_13', 'sdg_14',\n",
              "       'sdg_15', 'sdg_2', 'sdg_3', 'sdg_4', 'sdg_5', 'sdg_6', 'sdg_7',\n",
              "       'sdg_8', 'sdg_9'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ],
      "source": [
        "labels = df_osdg['sdg'].unique()\n",
        "labels.sort()\n",
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfylgK4bfJJe",
        "outputId": "d12b7e3a-db7b-4d65-cc91-80ef0925d883"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sdg_1': 0,\n",
              " 'sdg_10': 1,\n",
              " 'sdg_11': 2,\n",
              " 'sdg_12': 3,\n",
              " 'sdg_13': 4,\n",
              " 'sdg_14': 5,\n",
              " 'sdg_15': 6,\n",
              " 'sdg_2': 7,\n",
              " 'sdg_3': 8,\n",
              " 'sdg_4': 9,\n",
              " 'sdg_5': 10,\n",
              " 'sdg_6': 11,\n",
              " 'sdg_7': 12,\n",
              " 'sdg_8': 13,\n",
              " 'sdg_9': 14}"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ],
      "source": [
        "label_names = labels #[\"terrible\", \"poor\", \"ok\", \"good\", \"great\"]\n",
        "id2label = {idx:label for idx, label in enumerate(label_names)}\n",
        "label2id = {label:idx for idx, label in enumerate(label_names)}\n",
        "label2id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sss01_y36rFK",
        "outputId": "0db6debc-6865-442f-eff7-0d57020f8981"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'sdg_1',\n",
              " 1: 'sdg_10',\n",
              " 2: 'sdg_11',\n",
              " 3: 'sdg_12',\n",
              " 4: 'sdg_13',\n",
              " 5: 'sdg_14',\n",
              " 6: 'sdg_15',\n",
              " 7: 'sdg_2',\n",
              " 8: 'sdg_3',\n",
              " 9: 'sdg_4',\n",
              " 10: 'sdg_5',\n",
              " 11: 'sdg_6',\n",
              " 12: 'sdg_7',\n",
              " 13: 'sdg_8',\n",
              " 14: 'sdg_9'}"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "#id2label = {int(label): 'sdg_'+ str(label) for  label in labels}\n",
        "id2label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfVHXF721JgM"
      },
      "source": [
        "We can then apply this mapping to our whole dataset by using the `Dataset.map()` method. Similar to the `Dataset.filter()` method, this one expects a function which receives examples as input, but returns a Python dictionary as output. The keys of the dictionary correspond to the columns, while the values correspond to the column entries. The following function creates two new columns:\n",
        "\n",
        "* A `labels` column which is the star rating shifted down by one\n",
        "* A `label_name` column which provides a nice string for each rating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5nyo1ke1BdU"
      },
      "outputs": [],
      "source": [
        "def map_labels(example):\n",
        "    # Shift labels to start from 0\n",
        "    label_id = label2id[example[\"sdg\"]]\n",
        "    return {\"labels\": label_id, \"label_name\": id2label[label_id]}"
      ]
    },
    {
      "cell_type": "code",
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        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'__index_level_0__': 0,\n",
              " 'agreement': 0.75,\n",
              " 'doi': '10.6027/9789289342698-7-en',\n",
              " 'label_name': 'sdg_5',\n",
              " 'labels': 10,\n",
              " 'labels_negative': 1,\n",
              " 'labels_positive': 7,\n",
              " 'lang': 'en',\n",
              " 'sdg': 'sdg_5',\n",
              " 'text': 'From a gender perspective, Paulgaard points out that the labour markets of the fishing villages have been highly gender-segregated in terms of the existence of \"male jobs\" and \"female jobs\"; however, the new business opportunities have led to the male population of the peripheral areas now working in the service industry in former \"female jobs\": \"That boys and girls are doing the same jobs indicates change, because traditional boundaries between women and men\\'s work are being crossed. But the fact that young people are still working represents continuity with the past\" (Paulgaard 2002: 102). When Paulgaard refers to continuity with traditions, she refers to the expectations of young adults to participate in adult culture, thus these fishing villages traditionally have no actual youth culture. As described earlier, Paulgaard (2015) concludes that in some of Norway\\'s peripheral areas school is still \\'foreign\\', a time waster stealing time from young adults who should instead spend their time on what is considered to be \"real\" work.',\n",
              " 'text_id': '00021941702cd84171ff33962197ca1f'}"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "dataset = dataset.map(map_labels)\n",
        "# Peek at the first example\n",
        "dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_80_FcUMKj0",
        "outputId": "94c840ec-b851-4dfc-bb19-fee036fb4b37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['doi', 'text_id', 'text', 'sdg', 'labels_negative', 'labels_positive', 'agreement', 'lang', '__index_level_0__', 'labels', 'label_name'],\n",
              "        num_rows: 16259\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['doi', 'text_id', 'text', 'sdg', 'labels_negative', 'labels_positive', 'agreement', 'lang', '__index_level_0__', 'labels', 'label_name'],\n",
              "        num_rows: 1807\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "dataset = dataset.shuffle(seed=142)\n",
        "dataset = dataset.train_test_split(test_size=0.1)\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5cs5bz21Aru"
      },
      "source": [
        "## From text to tokens\n",
        "\n",
        "Like other machine learning models, Transformers expect their inputs in the form of numbers (not strings) and so some form of preprocessing is required. For NLP, this preprocessing step is called _tokenization_. Tokenization converts strings into atomic chunks called tokens, and these tokens are subsequently encoded as numerical vectors. \n"
      ]
    },
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        {
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            "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpfi5lgowb\n"
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            "storing https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json in cache at /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79\n",
            "creating metadata file for /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79\n",
            "https://huggingface.co/bert-base-uncased/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpqbw3w9n7\n"
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          "text": [
            "storing https://huggingface.co/bert-base-uncased/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
            "creating metadata file for /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
            "loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.19.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpj1b3medz\n"
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            "storing https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt in cache at /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
            "creating metadata file for /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
            "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp2ya8w65f\n"
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          "name": "stderr",
          "text": [
            "storing https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json in cache at /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
            "creating metadata file for /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
            "loading file https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
            "loading file https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
            "loading file https://huggingface.co/bert-base-uncased/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/bert-base-uncased/resolve/main/special_tokens_map.json from cache at None\n",
            "loading file https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79\n",
            "loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.19.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30522\n"
          ]
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              "model_id": "b2984c4af6d64918a0b77f1ae61dfaa2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"bert-base-uncased\" # \"xlm-roberta-base\"\n",
        "#model_checkpoint = \"distilbert-base-uncased\" # \"xlm-roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "#tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "print(tokenizer.vocab_size)\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True,max_length=135)\n",
        "\n",
        "\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4G8Voa3SEgem",
        "outputId": "83c0d2b7-0729-4744-ae45-251233c6b430"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cls_token': '[CLS]',\n",
              " 'mask_token': '[MASK]',\n",
              " 'pad_token': '[PAD]',\n",
              " 'sep_token': '[SEP]',\n",
              " 'unk_token': '[UNK]'}"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ],
      "source": [
        "tokenizer.special_tokens_map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S-j5XO3Enkg9"
      },
      "outputs": [],
      "source": [
        "label2id = {v:k for k,v in id2label.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BU-pnDr_Eg8S"
      },
      "source": [
        "When you feed strings to the tokenizer, you'll get at least two fields (some models have more, depending on how they're trained):\n",
        "\n",
        "* `input_ids`: These correspond to the numerical encodings that map each token to an integer\n",
        "* `attention_mask`: This indicates to the model which tokens should be ignored when computing self-attention\n",
        "\n",
        "Let's see how this works with a simple example. First we encode the string:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0EJfrQekFmV",
        "outputId": "42dc92d1-43cb-4fff-f893-cc2c67869bcd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 2651, 1045, 1005, 1049, 3228, 2019, 17953, 2361, 8395, 2012, 19875, 2102, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ],
      "source": [
        "encoded_str = tokenizer(\"Today I'm giving an NLP workshop at MLT\")\n",
        "encoded_str"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TDQV24_FEl0"
      },
      "source": [
        "## Loading a pretrained model\n",
        "\n",
        "To load a pretrained model from the Hub is quite simple: just select the appropriate `AutoModelForXxx` class and use the `from_pretrained()` function with the model checkpoint. In our case, we're dealing with 5 classes (one for each star) so to initialise the model we'll provide this information along with the label mappings:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "568c3f0536834553a1fe8f7e406e35d9",
            "690cda0fa04044018da101a670a2cf9b",
            "1df8750ac68b477caf928d6e2365b753",
            "e5118d6d688e4191bd80d3343a98c3aa",
            "b4a5fc29ad8f4cf1afb6b95dafd932f4",
            "3c2f9d798c5a47939daf06c6de923bca",
            "17e1f89cb70548af9352f8d93f71b8f6",
            "7f8c8ed9e3b84f44ab512a6e03ec7df0",
            "2eb737f99ece4392a599553c92ded4c8",
            "1b5a76a2f7ed443f88dda20110bb61fd",
            "c730a0a7964a43e59d4abbd85de7e0af"
          ]
        },
        "id": "LwK8qA4xE3Gf",
        "outputId": "8f0e7b2b-02cb-41ea-af28-b74d87a18cda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"sdg_1\",\n",
            "    \"1\": \"sdg_10\",\n",
            "    \"2\": \"sdg_11\",\n",
            "    \"3\": \"sdg_12\",\n",
            "    \"4\": \"sdg_13\",\n",
            "    \"5\": \"sdg_14\",\n",
            "    \"6\": \"sdg_15\",\n",
            "    \"7\": \"sdg_2\",\n",
            "    \"8\": \"sdg_3\",\n",
            "    \"9\": \"sdg_4\",\n",
            "    \"10\": \"sdg_5\",\n",
            "    \"11\": \"sdg_6\",\n",
            "    \"12\": \"sdg_7\",\n",
            "    \"13\": \"sdg_8\",\n",
            "    \"14\": \"sdg_9\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"sdg_1\": 0,\n",
            "    \"sdg_10\": 1,\n",
            "    \"sdg_11\": 2,\n",
            "    \"sdg_12\": 3,\n",
            "    \"sdg_13\": 4,\n",
            "    \"sdg_14\": 5,\n",
            "    \"sdg_15\": 6,\n",
            "    \"sdg_2\": 7,\n",
            "    \"sdg_3\": 8,\n",
            "    \"sdg_4\": 9,\n",
            "    \"sdg_5\": 10,\n",
            "    \"sdg_6\": 11,\n",
            "    \"sdg_7\": 12,\n",
            "    \"sdg_8\": 13,\n",
            "    \"sdg_9\": 14\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.19.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmplfd2vjoj\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "568c3f0536834553a1fe8f7e406e35d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f\n",
            "creating metadata file for /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f\n",
            "loading weights file https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = len(labels)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels, label2id=label2id, id2label=id2label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRp2oWkHHXe9"
      },
      "source": [
        "These warnings are perfectly normal - they are telling us that the weights in the head of the network are randomly initialised and so we should fine-tune the model on a downstream task.\n",
        "\n",
        "Now that we have a model, the next step is to initialise a `Trainer` that will take care of the training loop for us. Let's do that next."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PRg_dIHHb1E"
      },
      "source": [
        "## Creating a Trainer\n",
        "\n",
        "To create a `Trainer`, we usually need a few basic ingredients:\n",
        "\n",
        "* A `TrainingArguments` class to define all the hyperparameters\n",
        "* A `compute_metrics` function to compute metrics during evaluation\n",
        "* Datasets to train and evaluate on"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C4NteWTlE2-D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aad00199-a2aa-4bdb-96c4-08c9fdd8e452"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "batch_size = 16\n",
        "num_train_epochs = 10\n",
        "logging_steps = len(tokenized_datasets[\"train\"]) // (batch_size * num_train_epochs)\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=f\"{model_name}-finetuned-osdg\",\n",
        "  evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate=7e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    weight_decay=0.01,\n",
        "    logging_steps=logging_steps,\n",
        "    push_to_hub=True,\n",
        "    #push_to_hub_organization=\"Deutsche Gesellschaft für internationale Zusammenarbeit\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "VCjOVjHIPJh6",
        "outputId": "9bc93e3d-ec85-45de-ac62-92a7f1799761"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'bert-base-uncased'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 147
        }
      ],
      "source": [
        "model_name"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3AIRgJENLxt"
      },
      "source": [
        "Here we've defined `output_dir` to save our checkpoints and tweaked some of the default hyperparameters like the learning rate and weight decay. The `push_to_hub` argument will push each checkpoint to the Hub automatically for us, so we can reuse the model at any point in the future!\n",
        "\n",
        "Now that we've defined the hyperparameters, the next step is to define the metrics. In the MARC paper, the authors point out that one should use the mean absolute error (MAE) for star ratings because:\n",
        "\n",
        "> star ratings for each review are ordinal, and a 2-star prediction for a 5-star review should be penalized more heavily than a 4-star prediction for a 5-star review.\n",
        "\n",
        "We'll take the same approach here and we can get the metric easily from Scikit-learn as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b605f30b-9fba-48dd-8d26-b96c09d1004b"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import mean_absolute_error, accuracy_score,confusion_matrix,f1_score\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return {\"f1_score\": f1_score(labels, predictions,average='macro'),\n",
        "            \"accuracy\": accuracy_score(labels, predictions)}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhHMU_UMkEZt",
        "outputId": "128d8fc7-8602-4fce-8e75-d350b51b202d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['doi', 'text_id', 'text', 'sdg', 'labels_negative', 'labels_positive', 'agreement', 'lang', '__index_level_0__', 'labels', 'label_name', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 16259\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ],
      "source": [
        "tokenized_datasets['train']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5uOQhosANMu1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88dd838e-4edb-49a9-f17b-b28c49a3712a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/bert-base-uncased-finetuned-osdg is already a clone of https://huggingface.co/peter2000/bert-base-uncased-finetuned-osdg. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ],
      "source": [
        "from transformers import Trainer \n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5xGHux5lNThD",
        "outputId": "351e43c0-582e-4103-a995-30d2df62ab9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 16259\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 10170\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10170' max='10170' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10170/10170 44:50, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.655500</td>\n",
              "      <td>0.701648</td>\n",
              "      <td>0.750613</td>\n",
              "      <td>0.810183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.467400</td>\n",
              "      <td>0.707683</td>\n",
              "      <td>0.775421</td>\n",
              "      <td>0.823464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.313400</td>\n",
              "      <td>0.713787</td>\n",
              "      <td>0.788688</td>\n",
              "      <td>0.834532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.174200</td>\n",
              "      <td>0.832587</td>\n",
              "      <td>0.791744</td>\n",
              "      <td>0.836193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.121500</td>\n",
              "      <td>0.960276</td>\n",
              "      <td>0.778974</td>\n",
              "      <td>0.829552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.090600</td>\n",
              "      <td>1.042991</td>\n",
              "      <td>0.800745</td>\n",
              "      <td>0.843387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.025600</td>\n",
              "      <td>1.070226</td>\n",
              "      <td>0.804558</td>\n",
              "      <td>0.848367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.015600</td>\n",
              "      <td>1.149449</td>\n",
              "      <td>0.793251</td>\n",
              "      <td>0.841173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.002700</td>\n",
              "      <td>1.184847</td>\n",
              "      <td>0.802463</td>\n",
              "      <td>0.848367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>1.211954</td>\n",
              "      <td>0.799976</td>\n",
              "      <td>0.847814</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-1017\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-1017/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-1017/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-1017/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-1017/special_tokens_map.json\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-2034\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-2034/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-2034/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-2034/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-2034/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-3051\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-3051/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-3051/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-3051/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-3051/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-4068\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-4068/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-4068/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-4068/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-4068/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-5085\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-5085/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-5085/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-5085/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-5085/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-6102\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-6102/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-6102/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-6102/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-6102/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-7119\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-7119/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-7119/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-7119/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-7119/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-8136\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-8136/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-8136/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-8136/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-8136/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-9153\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-9153/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-9153/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-9153/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-9153/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-osdg/checkpoint-10170\n",
            "Configuration saved in bert-base-uncased-finetuned-osdg/checkpoint-10170/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-osdg/checkpoint-10170/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-osdg/checkpoint-10170/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-osdg/checkpoint-10170/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10170, training_loss=0.19965661796196313, metrics={'train_runtime': 2690.5049, 'train_samples_per_second': 60.431, 'train_steps_per_second': 3.78, 'total_flos': 1.12809954438333e+16, 'train_loss': 0.19965661796196313, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F_USoVX1z6CT"
      },
      "outputs": [],
      "source": [
        "#trainer.save_model()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fine_tuned_model = AutoModel.from_pretrained(\"roberta-base-finetuned-sdg\")"
      ],
      "metadata": {
        "id": "A0GyjR3xcF76"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429,
          "referenced_widgets": [
            "01347121b7b6476487b8ca0e1e1bf5bc",
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            "a261c517fdc14ba09dad5190bbc16421",
            "c2989890c73144e4996922d206ffb33c",
            "52b5271ae52d43ec956045377a77915f",
            "d1a4df3bc02543f6ba0c86ee8bd8f215",
            "8a44d7584d8d41e8ac8aab977cb608ac",
            "3c08b6a32e2245cdb1e3713bb93bdad2",
            "eb19741f4ffd4003a5d1b04bd97dd206",
            "e71f3e89891f4a149472c06005656c72",
            "1c8cc19baae647ad9bc37307c2bac7d1",
            "4d8f590d5bf148a9936e6cfc970f9af1",
            "cf59c60645a74bae94c7ad60f294e13f",
            "b64f351361964c74a65e69f7d96d19f2",
            "798ea3d3f02e46f0ba36c89d732bffa0",
            "001161fd86704b9a8be2a2946bd0c6e9",
            "fbcd859b1017422b81ae356248034bbd",
            "0ea4e410dbcb43f38335df9868ea5570",
            "2fdf18dcbcff4103b10e1f3d326e7083",
            "ad12c27141f94b6f945178f64d3389e4",
            "26d8f22613aa44de8af609278e146a62"
          ]
        },
        "id": "b9P50RxcZLC4",
        "outputId": "d9a8b2aa-32a8-4915-95d0-467f991bbcf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to distilbert-base-uncased-finetuned-osdg\n",
            "Configuration saved in distilbert-base-uncased-finetuned-osdg/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-osdg/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-osdg/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-osdg/special_tokens_map.json\n",
            "Several commits (5) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.34k/255M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01347121b7b6476487b8ca0e1e1bf5bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/May25_10-20-41_96f24b3736f1/events.out.tfevents.1653474049.96f24b3736f1.71.8:   5%|4         …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c8cc19baae647ad9bc37307c2bac7d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Enforcing permissions...        \n",
            "remote: Allowed refs: all        \n",
            "To https://huggingface.co/peter2000/distilbert-base-uncased-finetuned-osdg\n",
            "   527bc72..bb05fe7  main -> main\n",
            "\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Text Classification', 'type': 'text-classification'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.8433868289983398}]}\n",
            "remote: Enforcing permissions...        \n",
            "remote: Allowed refs: all        \n",
            "To https://huggingface.co/peter2000/distilbert-base-uncased-finetuned-osdg\n",
            "   bb05fe7..d9f3548  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/peter2000/distilbert-base-uncased-finetuned-osdg/commit/bb05fe7224334f6ad8896804c6c1980de108b655'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KZEs5SMcygfG"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub('sdg_classifier', organization=\"Deutsche Gesellschaft für internationale Zusammenarbeit\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WjsX_whPlDaE"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = trainer.predict(tokenized_datasets['test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "G-ZPUgUHQ5Xn",
        "outputId": "e04cf2a4-6f21-46de-b107-6e8946eca3b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id. If text, labels_negative, sdg, __index_level_0__, doi, lang, agreement, labels_positive, label_name, text_id are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 1807\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='113' max='113' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [113/113 00:04]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.argmax(pred[0], axis=1)"
      ],
      "metadata": {
        "id": "2n1JQQi3RKI2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = tokenized_datasets['test']['sdg']"
      ],
      "metadata": {
        "id": "OUo5TqX6RAW9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def conmpute_confusion_matrix(predictions, labels):\n",
        "    #predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    predictions_ = [id2label[i] for i in predictions]\n",
        "    cm = confusion_matrix(labels, predictions_, labels=np.unique(labels))\n",
        "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
        "    cm_perc = cm / cm_sum.astype(float) * 100\n",
        "    annot = np.empty_like(cm).astype(str)\n",
        "    nrows, ncols = cm.shape\n",
        "    for i in range(nrows):\n",
        "        for j in range(ncols):\n",
        "            c = cm[i, j]\n",
        "            p = cm_perc[i, j]\n",
        "            if i == j:\n",
        "                s = cm_sum[i]\n",
        "                annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n",
        "            elif c == 0:\n",
        "                annot[i, j] = ''\n",
        "            else:\n",
        "                annot[i, j] = '%.1f%%\\n%d' % (p, c)\n",
        "    cm = pd.DataFrame(cm_perc, index=np.unique(labels), columns=np.unique(labels))\n",
        "    cm.index.name = 'Actual'\n",
        "    cm.columns.name = 'Predicted'\n",
        "    figsize=(20,20)\n",
        "    fig, ax = plt.subplots(figsize=figsize)\n",
        "    plt.title('confusion')\n",
        "    sns.heatmap(cm, cmap= \"YlGnBu\", annot=annot, fmt='', ax=ax)\n"
      ],
      "metadata": {
        "id": "ZgfAypubSON_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conmpute_confusion_matrix(pred[0], labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "suMwQQBZSTot",
        "outputId": "8bb777b3-662b-4311-a25e-4b7aede27fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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Il3EyxnQ3xqwxxqyZNOkzb8UEAAAAAAC8yJOLbl4Va+0kSZNSXnFJCAAAAAAgkzJcUJARvpCwoAUBXLXIyMMaPHCcDh85LmOM2rdvqAcfaurtsC5buTKF9OGYR9NelyqZX6+Onq+wXNn1UPs7dORoym1NXxj5uRav2JB23PJ5g9W4/Rtq3vgm9X3iHslaRR48oe79PtDRYzEa2ru57m1YVcnJVoeOntSTAz7WgYMnHK9fRgwd8raWL1+jvPnCNH/+WG+H41GZvR/jwgKpH0v0ZQDAteXxRTeNMXnPs/mktTbh7H5r7dHzv5sZFkBm4/Sim4cOHtOhQ8d0Q6VwxZyKVds2A/X2O/1VrlwJj5d9rRfddLmMNv3wmhq2GaHObW/XqZgzGve/xeccV6p4Pr02rL0eeHKiNv/4mmrd/byOHovR8wNaKzYuXq+NXaCcoVl18lRKW/R4qJ6uK1dEfYdPzVB8Ti+6uXr1RmXPnlWDBo1x/Iue0wsVerMfS4G1WKHTi256sx9LgdWXA6kfA/7FzxfdfGu5X36n3dPnLr9ZdHOdpEOStkralvr8L2PMOmNM9QsnKwDg0goUzKMbKoVLknKEZlN42WI6GJU5h5W6t1+vXXsOa+/fF4+/QZ1KWrLyTxkjGWOUI1sWSVLO0KyKjDouSWnJCknKnj1E3r4j1NW45ZZKyh2W09thOMKf+jHSC6R+LNGXAQDXlhMJi8WS7rXW5rfW5pN0j6QFkp6UFFj3BATgUfv3HdSmTbt0Y9Xy3g7lqrRpWkOzF6xOe939wbv0w4JnNO7VBxWWK3va9oZ1KmnJyo1KTExW3+HT9MOiYdr84whdX66IJs/8Ie24Z/q21IbvXlG7FjX1ypj5jtYFVy+z92PgLPoyACCjnEhY3Gqt/frsC2vtN5Jus9b+LCmLA+UDCAAxMbHqFfGmBg/uqtDQ7Jd+g48JDnbrngZVNW/RWknS/z5doZvqP6M7mr+sA4ei9fLgNmnHFS2cW7v3HlZQkEvdOtVRnRYv6/rbB2rDlv3q+/jdaed8adTnqnznEM38YpW6P3iXN6qFK5TZ+zFwFn0ZAFK5jH8+nPrnc6CMSGPMQGNMqdTHAElRxhi3pGQHygfg5xISEtU7YqSaNb9TjRrX8nY4V6VR3cr67c89OnQkZZHNQ0dOKjnZylqrTz77XtWqlpYk3VajnH5es0OSVKViyjXhf+05LEmat2iNalYre865Z36+Ss2b3OxALZAR/tCPAYm+DAC4dpxIWHSSVFzSvNRHydRtbkntHSgfgB+z1mrYMxMUXraYHu7a3NvhXLU2zWpo9vx/LgcpVCBX2vNmjW/Spq1/S0q5HGTxypS7hURGHdd15YooX95QSVK92hW1dUekJCm8VMG099/bsKq27YzyeB1w9fylHwP0ZQDAteTxu4RkDHcJATIbp+8SsnbtJj3YebgqVCgpkzo9rXefTqpbt5rHy75WdwnJni1EG1a+opvqPaPo1MUyJ775sCpXLCFZqz37j6j3M58q6lC0vp0zSPd2HKm4MwmSpK4d79TjXeorMTFJe/8+qicGfKxjx2P0ybjuKhdeSDbZau/fR9Vn2NS0BTmvltN3CenXd6RWrd6o48eilS9fbj3Vs4Patm3oSNlO31nBm/1YCqy7Kzh9lxBv9mMpsPpyIPVjwL/4+V1Cxqzwy++0e3rVdaTdPJawMMbMl3TBk1trW1z6LCQsgMzG6YSFN13r25peStHCuTXm5QfUrts4R8s9y+mEhTc5/SXP2wLpi57TCQtvC6S+HEj9GPAvfp6wGOunCYsIZxIWQR4895upP1tLKixpSurrjpKYmwwAV+jvA8e9lqwAAAAAnOaxhIW1doUkGWNGWmtr/GvXfGPMGk+VCwAAAAAAMj8nFt3MYYwJP/si9XkOB8oFAAAAAACZlCcvCTmrt6Tlxpidqa9LS+ruQLkAAAAAAHiNy4kpAn7MiYRFLkmVJZWR1ELS7ZIOO1AuAAAAAADIpJzI9wyz1kZLyimpvqRxkiY4UC4AAAAAAMiknEhYJKX+bCrpPWvtQkkhDpQLAAAAAAAyKScuCdlvjJkoqZGkEcaYLHImUQIAAAAAgNcY4+0IMjcnEgftJX0tqYm19rikvJL6O1AuAAAAAADIpDw+w8Jae1rSnH+9jpQU6elyAQAAAABA5sWlGQAAAAAAwOeQsAAAAAAAAD7HiUU3AQAAAAAIOCy6mTHMsAAAAAAAAD6HhAUAAAAAAPA5JCwAAAAAAIDPYQ0LAAAAAAA8wLCIRYYwwwIAAAAAAPgcEhYAAAAAAMDnkLAAAAAAAAA+hzUsAAAAAADwAJawyBhmWAAAAAAAAJ9DwgIAAAAAAPgcEhYAAAAAAMDnsIYFAAAAAAAe8P/s3XmcTfUfx/HX996ZMYxdjC17KopkiWTfl7EvRYUUKnt2KVp/KVtkLf1KC1FUKCJ7CSGJZFcaYzdixmzf3x8zJvMzGMtd5t738/G4D3PO+Z77/Xx97znnzme+53s0h8XNUcJCxMXibbSnQ3Arpwn2dAhuc3pPf0+H4FYF7vnY0yG4zaFfW3s6BHERh/Gvrz7xNs7TIbjNubgjng7BrUIC8no6BBERl9MtISIiIiIiIiLidZSwEBERERERERGvo4SFiIiIiIiIiHgd/7qRU0RERERERMRNjIYI3BT994mIiIiIiIiI11HCQkRERERERES8jhIWIiIiIiIiIuJ1NIeFiIiIiIiIiAsY4+kI0jeNsBARERERERERr6OEhYiIiIiIiIh4HSUsRERERERERMTraA4LERERERERERdwaA6Lm6IRFiIiIiIiIiLidZSwEBERERERERGvo4SFiIiIiIiIiHgdzWEhIiIiIiIi4gJGc1jcFI2wEBERERERERGvo4SFiIiIiIiIiHgdJSxERERERERExOsoYSEiIiIiIiIiXkeTboqIiIiIiIi4gCbdvDkuG2FhjGl4yc/ZjDHvGWO2GWM+McaEuqpeEREREREREUn/XHlLyGuX/DwGCAfCgI3ANBfWKyIiIiIiIiLpnLtuCalgrb0v6edxxphObqpXRERERERERNIhVyYs8hhj+gMGyGqMMdZam7RNk32KiIiIiIiITzOaxOKmuDJxMAPIAmQGPgBuAzDG5AW2urBeEREREREREUnnXJawsNaO+r/XsaT1R6y1j7uqXhFfFh5+nM6Pj6Rpk76ENe3HrA8XXVZm+fKNtGj2HC1bDKBt68H8/PNOAPbvO0ybVoNo0ew5tm7ZBUBcXDxPdHmJqKgLbm2H+KenHqvC9wueZfn8Z3hndBsyBCUO8hvcuw5rFvZi5Vc9eaLjA6nu27ZZWdYu6s3aRb1p26wsAEGBTj6a+ijL5z9Dp/YVk8u+8WIY99ydz/UNSiMdt+IL/O1zfGD/ER5uNSr5Va1SLz7+cFmKMtZaRr/2Kc0aDqNdy5Hs3HEwed8ObV+mXcuR/LJ1L5DY3h5dx3pte0VEvJVHHmtqjOlirX3fE3WLpGcBTieDBj9OqdLFOPdPFG1aD6bKg2UoUeL25DKVK99D7dpvYYxh166D9O87lkXfTOCzOd8xdHgXChTIw+uvvs+EiXcy+9OlhIVVI2PGDB5slfiDvHmy8ETHB6jVfBLRF+KY+lZbmje6Bwzkz5uV6mGTsNaSK2fIZftmz5qRfk/XpHH76Vgs38zpztKVu6h0f2E2bj7E2zPW8OWsrnwwZyOl7gzF6XSwfWe4B1qZOh234gv87XNcpGheZn/xIgDx8Qk0rDWQWnXLpSizbs12Dh08ypffvMqv2/bx+ksf8+HsYXz+2SoGDnmY/AVy8ebrcyg74WnmzVlJ46YPeG17RUS8lafmkhjloXpF0rXceXJQqnQxAEIyZ6RY8QIcjTiZokxISMbke+Wizkcn/xwQGEB0VAzRURcICHQSGXmOlSs20UQMSLEAACAASURBVLxFDfc2QvxWQICD4AyBOJ0OMmYM5MixszzeviLjpqzi4hRHJ06eu2y/GlWLs+bHfZyOjOJMZDRrftxHzaoliIuLJ2PGQAIDHMnPOB/YszZvTvzenc26Jh234gv8+XO8Yf1OCt6em/z5c6VYv/L7rTRtVhljDGXKFufs2fMcO3aagAAn0dExREfHEBDo5GzkeVav3EbT5lU81AIR8STj8M2Xu7hshIUxZtuVNgGhrqpXxF8c/usoO3fup0zZOy7btuy7nxg39hNOnDzD1KlDAXikQwOGDp5ETEwsI1/qzpTJ8+jWvRUOh+bAFdc7cvQsU//7AxuW9SM6Oo5VP+xl9Q97mTy6Dc0a3UPDOndx4uR5Xnh9MfsPpfwlKG9oVv4+ciZ5OTwikryhWVm4dAetw8ry9SdPMeX9ddSreSe/7gwn4thZdzcvzXTcii/wt8/xkm820qBxpcvWHz16itC8OZOX84Tm4FjEado9UosXhs0kJiaO4S8+xoypC3niqcbppr0iIt7ElbeEhAINgFP/t94AP7iwXhGfd+5cFH16v8XQoV3InDnTZdvr1nuAuvUeYNPGHbz99hxmvv8C+fPn5oNZiYObDh4MJyLiBMWKF2DwoLeJjY2jd++HKVI0v7ubIn4iW9ZgGtS6k8oNxhN5NpppY9rRqmkZgoKcXLgQR+P202lU927GvNyCVp1mpuk94+MT6Dn4cyBx9MYn0x6jS69PeXFgAwrky8bcr37hu5W7XNms66LjVnyBv32OY2PiWL3iF3r1bZXmffLlz8WM/w4E4NDBo0REnKJosXw8P+Q9YmPjeKZXcwoXyeuqkEVEfIorU70LgczW2oP/9zoArHRhvSI+LTY2jr69x9A0rBr16qc+QeFFFSqW4q8/Izh1KjLF+gnjP6V3n0f4aNZi2rSpw3MDHuOdd+a6Mmzxc9UqF+PQ4dOcPHWeuLgEvlm+kwr33U74kUgWL9sBwDfLdnJ3ycsH4B2JiCR/3mzJy/lCs3IkIuVnutPDlZj31S/cX7Ygkf9coMeAuXTv9KBrG3UddNyKL/DHz/G6tdu5q1Qhct2W9bJtefLkIOLIvyPCjkacIndo9hRl3nl7Ps/2bsGnHy+nZeuH6PNcG6ZP/trlcYuI+ApXPiWkq7V27RW2dbj4szEmh6tiEPE11lpGPD+FYsUL0LlLWKplDh4MT54PYMdv+4iJiSV79izJ2zdu+I08eXJSpEg+oqNjMA4HDochOlozl4vrHA4/w/1lChIcHAjAQw8UY/e+43z7/e88WKkoAFUqFmHfwROX7btq3V6qP1icbFmDyZY1mOoPFmfVur3J27NlDaZujZLM/eoXMgYHYhMs1kJwsEfmlb6MjlvxBf76Of528YZUbwcBqFGrLAu/Wo+1lm2/7CVz5ozkzv1vwuLnjbvInTs7hQqHEh0Vg3EYHMYQHR3jrvBFxAsY45svd/GGb3PLgfs9HYRIerB58+989eVqSpYsRMsWAwDo268D4eHHAXj44fp8t/QnvvxyFQEBToIzBDFmXL/kic+stUyd+jljxvYDoG27ugwa+DbxcfG8MPIpzzRK/MKWXw+z6LsdLPmsO3HxCfz2+xE+nruJ4OBAJr3Rmqceq8L58zEMfPFLAMqUzs9j7Sow8MWvOB0Zxfhpq1g0uxsA46au5HRkVPJ79+tRk7enr8Zay6p1e+n8SCWWz3+GWZ9t8khb/5+OW/EF/vg5jjp/gZ9+2MHwFx9NXjdvzkoA2rSvyUPV72Xt6l9p3mg4wcFBjHylc3I5ay3vTlvEf97qDkCrttV5fvC7xMXHM2zEo4iISNqYi5lwjwVgzBZrbbnUt/7h2eBEboF4G+3pENzKaYI9HYK4SIF7PvZ0CG5z6NfWng7BrXTc+i5/ugZFx5/2dAhuFRKgeTDEV5R049/r3a/MrDU++TvttsequaXfvGG6Yp/sQBERERERERG5cd6QsEjBGNPNGLPJGLNp+vQ5ng5HRERERERERDzAG+awSDGUxFo7HZieuKRbQkSu1/Bhk1m18mdy5srGV1+P9XQ4IskyBAXw+QddyBAUgNPpYNF3Oxjzzgq++OAJMocEAZArZwhbfz1M1z6zgcTHlS785CkatpsGgMNh+GZOd44cjaTTs58A8NZLzSlbOj8Y2H/gBH2HL+B8VPqa1E7HrfgKf/osX7gQy5OPjyYmJo74+Hjq1C/P0z2bezosEfEy7pyg0he5PGFhjMmZyuqz1trYpJ/ruDoGEX/SsmVNOnZsyJAhkzwdikgKF2LiaPfEB5yPiiEgwMH8D7uyYs1uWnWamVxm+rj2LF3xe/JypXKF2Ljlz+TlJx+tzO59x8iSOUPyupFvfMs/5xKfMvDiwAZ06VCJd95L9SFVXkvHrfgKf/osBwUFMG3mc2QKCSY2No6uj42marV7KFO2uKdDExHxGe64JWQzcAz4A9id9PMBY8xmY0x5a+3Jq+4tItelQsVSZMuW2dNhiKTq4siHgAAngQEOLp34OXNIBqpWKsq3y/9NWNR86A5WrN0NQL7QrNSpXpJPP9+c4j0vJisAgoMD8fBc0jdEx634Cn/6LBtjyBSSOGFtXFw8cXHxyU9FERGRW8MdCYvvgMbW2tustbmARsBC4BlgshvqFxERL+FwGJbO68G21QNZ/eM+tvx6OHlbwzp3se6nfSkSEA9WKsIPGw8AMGpwQ14Zu5SEVDISY19uwdZVAylR9DZmfvKTy9shIgIQH5/Aw61GUbfaczxQ5W7uLVPM0yGJiPgUdyQsKltrl1xcsNYuBapYa9cDGa68m4iI+JqEBEv9NlOpUGcs5e4twJ0l8iRva97oXhYs/jV5OW+eLJw+E0V0dCx1a5Tk+Mlz/LojPNX37T9iAffXeovd+47RrGFpl7dDRATA6XQw+4sX+fb70fz26wH27D587Z1ExK8Y45svd3FHwiLcGDPYGFM46TUIiDDGOIEEN9QvIiJeJvJsNOs27KfmQyUAyJE9E+XuLcDy1buTy9R86A5WrdsLQIVyhahf807WL+nL5DfbULVSUd7+T6sU75mQYPnym+00qVfKfQ0REQGyZM1EhUp38sPa7Z4ORUTEp7gjYdEBKAgsSHoVSlrnBNq5oX4REfECOXNkImuWxPu9gzMEUL1KcfbuPw5A0/qlWLbqDy7ExCWXr1W1BN8nzV/xn/HLqFB3LJUbjOeZgfNYt2E/vYd8AUCR2/+d27l+rTvZk/SeIiKudOrkWc5GngcgOjqG9T/uoEjRvB6OSkTEt7j8KSHW2uNAryts3uPq+kX8zYD+49mw8TdOnzpLrRrd6dmrHa3b6GE84nmhubMw/tWWOJwGhzF8veQ3lq36A4Bmje7hnXf/fbKHw2EoUihnckLjSowxjH+tJZlDMmAM7NgVwdCXF7q0Ha6g41Z8hT99lo8dO8OLw2YSn5CATbDUa1CB6jXLejosERGfYqyLplM3xnwNXPHNrbXNrv0uf6TDud5FUoq30Z4Owa2cJtjTIYiLFLjnY7fVVbFcIVqHlWHIS55JPhz6tbVH6vUUHbe+y5+uQdHxpz0dgluFBGg0h/iKkj79eJ37P1njk7/Tbu5QzS395soRFm8l/dsKyAt8lLT8CBDhwnpFRCSd27jlEBu3HPJ0GCIiIiLiQS5LWFhrVwEYY8ZYaytcsulrY8wmV9UrIiIiIiIiIumfOybdDDHGJD+UOunnEDfUKyIiIiIiIiLplMsn3QT6AiuNMfuSlosA3dxQr4iIiIiIiIjHGJ+eocP13JGwyArcAxQFmgEPAnrmnIiIiIiIiIhckTtuCRlhrY0EsgC1gUnAFDfUKyIiIiIiIiLplDsSFvFJ/zYBZlhrFwFBbqhXRERERERERNIpdyQsDhtjpgHtgcXGmAxuqldERERERERE0il3zGHRDmgIvGWtPW2MyQcMdEO9IiIiIiIiIh6jSTdvjssTFtba88AXlyyHA+GurldERERERERE0i/dmiEiIiIiIiIiXkcJCxERERERERHxOu6Yw0JERERERETE7xiHJrG4GRphISIiIiIiIiJeRwkLEREREREREfE6SliIiIiIiIiIiNfRHBYiIiIiIiIiLmA0hcVN0QgLEREREREREfE6SliIiIiIiIiIiNdRwkJEREREREREvI7msBARERERERFxAc1hcXM0wkJEREREREREvI4SFiIiIiIiIiLidZSwEBERERERERGvo4SFiIiIiIiIiHgdTbopIiIiIiIi4gKadPPmeHXCIsHGejoEt3GYQE+H4FaWeE+H4Db+1rcX4s94OgS3CXJm9nQIbvXnr+08HYLb3NnoJ0+H4FY7F5f3dAhu43QEeToEt7LWejoEt3Ea/+pb8V3+9D0ZQL/Py9XolhARERERERER8TpKWIiIiIiIiIiI1/HqW0JERERERERE0iuH7nm5KRphISIiIiIiIiJeRwkLEREREREREfE6SliIiIiIiIiIiNfRHBYiIiIiIiIiLmA0h8VN0QgLEREREREREfE6SliIiIiIiIiIiNdRwkJEREREREREvI7msBARERERERFxAaMhAjdF/30iIiIiIiIi4nWUsBARERERERERr6OEhYiIiIiIiIh4HSUsRERERERERMTraNJNERERERERERcwxtMRpG8aYSEiIiIiIiIiXkcJCxERERERERHxOkpYiIiIiIiIiIjX0RwWIiIiIiIiIi5gNInFTdEICxERERERERHxOkpYiIiIiIiIiIjXUcJCRERERERERLzOFeewMMZMBOyVtltre1/rzY0xdwEFgJ+stf9csr6htfbb64xVREREREREJN3QFBY352qTbm66mTc2xvQGngV2Au8ZY/pYa79M2vwaoISFiIiIiIiIiKTqireEWGs/uNorDe/9FFDeWtsCqAmMMMb0Sdrm1jzT8GGTqPpgZ8LC+qS6/ezZczzd4zVaNO9H06Z9+OLz5QDs33eY1q0G0LxZP7Zs2QVAXFw8XbqMJCrqgtvil6tbs3ozDRs8Q/16PZg+/fPLth8+fJTOnUbQLKwPjz02nCNHjgOwb99hWrXqT7OwPmzZ8juQ1L+dX/Da/vWntl4UH59Au1bD6fn0W5dt+3L+ampUfZq2LYfRtuUwPp+3AoD9+/+mfZvnad1iKL9s3Q0ktvepJ1732vb6U9/6wzm5aMGsfPVO8+TXls8fpXOLUgA81uxuvp3RisXTWjKoa4UrvofDYfhyUnOmj6qbvG7MoBp8PaUF/TuXT173zCNlqVulkOsacx3Cw4/TudMowpr2o1nT/sz6cPFlZWa+9xWtWg6kVcuBNA97jntLt+f06X84eTKSRzuOoHnYcyxftiG5fM9nR3P06El3NuO6+Mux649926juc7RuPpx2LUfwSNsXL9u+ccNOqlbqQbuWI2jXcgRTJy8A4OTJSDo9+gqtmg3j+2U/J5fv8+x4jh495bb4RcB/zlHiu675WFNjTG5gMFAKCL643lpb+xq7Oi7eBmKtPWCMqQnMM8YUxs0JixYta9GhYyOGDHk71e2ffPwNxUsUZMrUYZw8eYbGjXrRNKw6c+YsZdjwrhQokJvXXp1JuYmDmP3ptzQLq07GjBnc2QS5gvj4eF56aRoz3x9FaGgu2rYZSO3alShR4vbkMqPf+C/NW9SiZcvarP9xG2PHzGL0m/2YM2cJw4c9SYGCeXj11XeZOHEIn376DWHNanhl//pTWy/18axvKVo8P+f+iUp1e4NGlRn2fKcU6+Z99j2Dhz5G/gK38cbrHzFuQh8+m72cpmFVvbK9/ta3/nBO3v9XJM2eTRxU6HAY1n7UnqU/HOSBMnmpU6UwzZ5ZQExsAjmzBV/xPTq1KMXeP0+TOVMgAHcWzUF0TBxhTy/gv681IHOmQDIGB1D2ztxM/vQXt7TrWgKcTgYNeoxSpYtx7lwUbVsPocqDZShRomBymSe6NuOJrs0AWLFiEx9+sIjs2TPz0azFtG9fj7r1HuDp7q9Tp24lVqzYxN13FyFPnpyeatJV+dOx6299e9G7/x1CjhxZrri9XPmSTJrSP8W6bxatp227WtSpV4GePcZSu255Vq7Ywl13FyZPnhyuDlkkmT+do8R3pWXSzY9JvK2jKDAKOABsTMN+EcaY+y4uJCUvmgK3Afded6Q3oWLF0mTPduWLjTGGc+eisNZy/nw02bJlJiDASUCgk+ioC0RFxRAQ6CQy8hwrVmyieYua7gtermrbtt0UKpyP22/PS1BQII2bPMTy5T+lKLN3759Urpz4kXug8r0sX574153AACdR0ReIirpAYEAAkZH/sGLFRlq0qOX2dqSFP7X1oiNHTrB61VZata55XfsFBAQQHR1DdHQMgQGJx+6qlZsJa/6QawK9Sf7Wt/52Tn7wvnwcCj/L30fP0aHp3Uz/bBsxsQkAnDwTneo+eW/LRM2Kt/PZt38kr4uLSyA4KABjICDAQUKCpc9j9zPhoy1uaUda5M6Tg1KliwEQEpKRYsULcDTiyn9BX7xoHY0bVwUSj9uo6BhiYmJxOB3ExcUz68PFPNG1uVtivxH+dOz6W9/ejMBAJ9HRMcTGxOFwJLb34w+X0rlrY0+HJn7Gn85R3swY33y5S1oSFrmste8BsdbaVdbaJ4Brja4AeBw4cukKa22ctfZxoPrFdcYYj6eaO3ZszL69h6levSvNm/Vj6LAncDgcdOjQiGnTPmfokLfp3r01UyZ/RvfurXE49HAVbxERcZJ8eW9LXs4bmouI//sCdeddRfhu6XoAvvtuPefORXHqVCQdOjZm2rR5DBnyNt27t2Hy5M/o3r2N1/avP7X1otH/+Yj+Ax7B4bjyWXHZ0g20bjGU/n0ncCT8BAAPP1KXd6d/yfNDp/Fkt2ZMn7KAJ7s189r2+mPfXo2vnZOb1CjGwpX7AChaICsVSocyb3wYH49uxL0lb0t1n+HdH2D0extJsP/Ofb33zzOcPBPNl5Oa8/36PymcPysOh2HHnhNuacf1Onz4KDt37qdM2RKpbo+KusDatVupV78yAE2aPsSK5Rt5qusrdOvWktmfLiGsmfeNnrmUvx67/tC3ABjo8eSbPNzmBeZ9tiLVItu27qFty+d5pttb7Nn9FwCNmlRh5fdb6P7kaJ7s1pQ5ny6nabMHvb+94nP89RwlvuWat4QAsUn/hhtjmgB/A9ccv2et/esq29ZdsrgcuD8NcbjM2rVbuOvuIvz3g1EcOnSErk+MokKFUuTPn5sPZ70MwMGD4RyJOEGx4gUZNGgCsbGx9O7dgaJF83sydEmDQYO68MrL05k//3sqVChNaGgunE4H+fPnZtasV4HE/o04coLixW9n0MBxxMbG0btPB4oWLeDh6K+PL7V11cot5MyZlVKli7Jxw45Uy9SoVY5GTaoQFBTI3DnLGT5sGu+9P4x8+W9j5gfPA3Do4BEiIk5SrFgBhg2eQmxsHM/2bkORIvnc2Zyb5kt9ey2+dE4ODHBQu3Ih3no/cR5rp9NBtiwZaNP3a8qUvI0Jw2pRu/PcFPvUqnQ7J05H89ueE1QqkzfFtlen/fuXsWkj6zLi7XU8/XBZ7iqWk3WbD6cYkeFJ585F07f3GIYM6UzmzJlSLbNyxc+UK3cn2bNnBiBLlkxMmTYUgDNn/uHdGQuYMHEgL4yYSmTkOTp3DuO+ciXd1oZbxdeOXX/q2/9+NJzQ0JycOBFJjydHU7RYPspXuCt5+92livDtsrFkCglmzapf6Nfrbb7+djRZsmRi0tTE20Qiz5xj5ruLGPd2b0a9MJPIyHM83rkRZe9LPdkj4m6+do4S35OWFNkrxphswHPAAOBdoN8tjMHjD3r5Yv731KtXGWMMhQvno2DBPOzbdzhFmfHjP6ZPnw58NGsRbdrUZcCAx5n8zhwPRSwXhYbmJDxpciCAIxEnCA3NeVmZiZOGMH/BOPr26whA1qyZU5QZP+4j+vTtyKxZC2nTth4DBnbinUne1b/+1FaArZv/YOWKzTSs25dBz73Dhp92MHTQ5BRlsmfPQlBQ4v39rdrUYudv+y97n4kT5tKzd1s+/mgJrdrUpN9zjzD1nfluaUNa+VvfXosvnZOrVyjIjj0nOHE68daPI8fPsXTdAQC2/XEcm2Avm8fi/tJ5qFO5ECs+aMv4ITWpXDY/bw2qnqJMncqF2L7nBJkyBlIoXxb6vLaChtWKEJzB6ZZ2XU1sbBx9+4yhSVg16tV/4Irlvlm8jsZNUr9Na+qUz+nWoxWLF63l/vJ38drrz/LOO3NTLetJ/nbs+lPfAsl9mStXVmrXKc/2bftSbM+cOSOZQhKP32o1yhIXF8+pU2dTlJk29Uue7B7GN4vXU+7+krz8WjemeNk1SHyXv52jxDddM2FhrV1orT1jrd1ura1lrS1vrf3qFsZgr13EtfLly836H7cBcPz4afbv/5vbbw9N3r5hw2/kyZOTIkXyExV9AYfD4HA4iIrWDLmedu+9d3DwQDh//RlBTEwsixetpXbtSinKnDoZSUJC4v3i06d/TuvWdVJs37Bhe3L/Rkdd7F9DtJf1rz+1FaBP//YsWzGRb5eNZ/SYZ6n0QCleH/1MijLHjv072/rKFT9TtFjKv65v2riT3HlyULhIXqKjYzDGO9vrb317Lb50Tm5a89/bQQCW/XCQymUTR/cUKZCVwEDHZfNYjHn/Z6o9NodanebS9z8rWf/L3wwYvTp5e4DT0LllaWbM3UZwUEDyRdThcBAY4NmEhbWWF56fSrFiBejcuekVy509e56Nm3ZQu/blT0k5eCCciIgTVKpUmujoGBzGgTGGC9Exrgz9hvjTsetvfXv+/AXOnYtK/vnHH7ZT4o6CKcocP3Yam3Tb1q/b9pKQkJA8qgTg4IEjHD1ykoqV7k68BjkMxuCV7RXf5E/nKPFdaXlKyPukklRImssiXXiu/1g2bNzO6VNnqVnjSXr2epi4uHgAHn64Ac883ZahQyfSLKwvFstzAx4jR46sQOIFeurUuYwd+xwA7drVZ+DAccTHJfDiyG4ea5MkCghwMuKFp+j65CgS4uNp3boud9xRiLcnfMI995Sgdp1K/LRhO+PGzgJjqFihFC+82D15f2stU6fMZey4AQC0a1+fgQPGERcXz8iRPTzVrFT5U1uv5p2J8yhVuii1apfnk1lLWbliM84AJ9myhfDKaynbO33ql7w5picAbdrWYsigycTHJ/D8C108FX6q/K1v/eWcnDFDAFXvz8+It/+9C3Le0t283v8hFk1tSWxcPIPeWgNAnpwZebXvQzz1wnfXfN9Hw+5m/rLdRF+I5/f9J8mYIYCFU1qwauNfnD3n2V+ENm/exVdfraZkyUK0ajkQgL59HyE8PPEvfO0frg/AsmUbqPpgWTJluvwpKRMmfEqfPo8A0LhJVXr3fJN3ZyygZ+92bmpF2vnTsetvfXvyxBn69U58klFcXDyNm1SharUyfDb7ewDaPVyb75Zu5LPZ3xMQ4CRDhiDeGPMM5pKZ6CZNmEfPPm0AaNi4Mv16TWDmjIU826uV+xskfsmfzlHezJ0TVPoiY+3VBzgYY1pfshgMtAT+ttb2viUBGLPFWlsutW0J9jePj75wF4cJ9HQIbmWJ93QI4iIx8f94OgS3CXJmvnYhH2JtgqdDcJs7G/107UI+ZOfi8p4OwW2cjiBPh+BW8Qn+89f8OJv64699VbDTux8JKzfO374nG+726V/pay1e55O/065oXNUt/XbNERbW2s8vXTbGfAqsTWsFxpjUzqZnrbUXJ/Osk8p2EREREREREfFjN/JcmjuAPNdRfjNwDPgD2J308wFjzGZjTHlrbYpn6xhjuhljNhljNk2f7p2TMImIiIiIiIiIa6VlDouzpJzD4ggw+Drq+A6YZ61dkvR+9YHWwPvAZCDFNNPW2unAdPCvW0JERERERETEtzh8+oYX10vLLSFZbrKOytbapy55v6XGmLestd2NMRlu8r1vmfDw4wwZ/DYnTpwGY2jXrh6PP37lWbAlfRk2dCIrV24iV65sfL3wbU+H41L+1NaLGtbtS6aQYJwOB84AJ7PnvuzpkFzC3/o2vZ6XO7csTbuGJbEW/jhwisFj1hATm3g/8oinH6B1/ZLc13JWcvncOTMy+rnqdBm+hPdeqc99d+Xm598i6PbisuQyj4bdTeeWpSmcPyuV2n3Mqch/Z2cf8fQD1Kh4O1EX4hg8Zg079pxwX2Nv0IULMTz+2IvExMQRHxdP/QaV6dnL+yZevBX87bj1p74FiIw8x6gXZrJn92GMgVGvPEnZ+0p4OiyR6+Jv5ylJX655S4gxZnla1l1FuDFmsDGmcNJrEBBhjHECXjODm9PpYNDgTixc9DZzZv+HTz7+hj17/vR0WHKLtGxVmxnvvuDpMNzCn9p6qff+O5y581/z2WQF+F/fpsfzcmiuTDzevBQte31Fkx7zcTgMTWsWBeCeO3KRNfPlefrq5Quy5ufDALw771cGvrn6sjKbd0TQaei3/BVxNsX6GhULUjh/Nuo+MY8RE9bxUs8HXdCqWy8oKJCZ77/I/AVv8vn80axdu5Vftv7h6bBcwt+OW3/qW4DRr39M1Yfu5ctF/2HuF69QtFg+T4ckct387Twl6csVExbGmOCkCTNvM8bkMMbkTHoVAQpcRx0dgILAgqRXoaR1TsBrUu558uSkdOniAIRkzkjx4gWJiPD+v1JJ2lSsWJps2fzjiQ7+1FZ/4299m17PywFOQ3CQE6fDkDGDk6MnzuNwGAY/WYnR7228rHy1CgVYvekvAH7cGs4/UbGXldmx9ySHIy5/Ak/dKoVYsHwPAFt/P0aWzEHkzpnxFrfo1jPGEBKS+NjLuLh44mLjUzwO0pf423HrT3179ux5ft60i5atawAQGBRA1qwhHo5K5Pr523lK0per3RLSHegL5Ad+Bi5ebSKBSWmtwFp7HOh1hc170vo+7nT4E6l+zQAAIABJREFUr6Ps3LmfsmVLejoUEUkLY+j+5H8wxtC2XW3atKvt6YjkFksv5+WIE+d5b952Vs1qz4ULcazd/DdrN/9Np+alWL7+EMdOpnzsosNhKFYwG3sOnb6h+kJzZSL82Lnk5SPHzhGaK9Nl9Xij+PgE2rYZzKFDR3jkkQaUKXuHp0OSW8Rf+vbwX8fIkTMLLwx/l12/H6JU6SIMGvoomTJ5zR3PIuIFNIfFzbniCAtr7QRrbVFggLW2mLW2aNKrrLX2mgkLY8zXxpivrvS6pa24hc6di6J379EMGfoEmTNn8nQ4IpIGH3w0gs8+f5XJ0wYy+9NlbNr0u6dDklsoPZ2Xs2YOok6VQtTuPJeqHWeTMTiAFnVK0LB6UWZ9ueOy8mXvzM0vu455IFLPczodfDH/Tb5fMZVff93L7j8OeTokuUX8pW/j4xP4fcdB2ravzWdfvEzGjBmY+e5CT4clIuJT0vJY0wRjTPaLC0m3hzyThv3eAsYA+4EoYEbS6x9g7w3E6nKxsXH06f0mYWHVqV+/sqfDEZE0Cg3NCUCuXNmoXac827d55SlGbkB6Oy8/WC4/f0X8w8kz0cTFW5auO0jvx8pROF8Wlr3fhhUftCVjhgCWzWwDJM5BsXrT4RuuL+LEefLl/ncIet7cIUScOH/T7XCnrFlDqFSpNGvXbvV0KHKL+XrfhobmIDQ0J2XKJt66Vq9+RX7fcdDDUYmI+Ja0JCyestYmj1W11p4CnrpK+YvlVllrVwFVrbXtrbVfJ706ANVuPGTXsNby/PPvUKx4ATp3aebpcEQkjc6fj+bcuajkn3/8YTsl7ijo4ajkVkiP5+Xwo+e4767cBGdwAlDlvny8/8V2Huwwm1qd5lKr01yiLsRR94l5ydt/2HLjCYvl6w/Rok7iEwnuuys3Z8/FpIvbQU6ejCQyMvFWlujoGH78cRtFi17P9Fjirfypb2/LnZ3QvDk5sD8cgJ/W76BY8fwejkpExLdc87GmgNMYY6y1FiDp6R5B11FHiDGmmLV2X9L+xQCvm5Fo8+bf+erLVZQsWZiWLfoD0LdfR2rUKO/hyORW6N9/DBs3bOfUqUhqVO9Kr14P06ZtPU+H5RL+1FaAkyci6dt7PADxcfE0avIgD1Ur6+GoXMPf+jY9npd/2XWMb9ccYMGk5sTHW3bsPcGcb3alWjZntmAuxMRzLioued0nbzWmeMFsZMoYyJpZ7Rk6fi1rfz7M481L8VSbe7ktZ0a+ntKSVRv/ZPj4dazc8Bc1Kt7O8pltiLoQx5Cxa9zV1Jty7Ngphg19h4T4BBISLA0aVqFmLe/t15vhb8etP/UtwJDhjzJ00FRiY+MoWDAPL736pKdDErlu/naecjeHsZ4OIV0zSXmIKxcw5k2gMDAtaVV34JC1dkCaKjCmAYm3guxLWlUE6GatXXqtfRPsb37Tuw4T6OkQ3MoS7+kQxEVi4i9/koGvCnL614za1nrNk6hd7s5GP7m8jma1i5P3thCmf7bN5XVdy87FvvsL5f9zOq7nby7pX3xCjKdDcJs46/2ji26lYGdOT4cgLuJv35MNd/v0tJQNlqz1yd9plzR4yC39lpYRFoOBbkCPpOVtQN7rqCMrcA9QFGgGPAgcv479RUREfM5X32uuFREREZGrueYcFjbxT2o/AQeASkBtYOd11DHCWhsJZEnadxIw5bojFRERERERERG/ccURFsaYksAjSa/jwBwAa22t66zj4pimJsAMa+0iY8wrNxCriIiIiIiIiPiJq90S8juwBmhqrd0DYIzpdwN1HDbGTAPqAW8YYzKQtqeTiIiIiIiIiKRbDp+eocP1rpY4aAWEAyuMMTOMMXWAG/nvbgcsARokPR41JzDwBt5HRERERERERPzEFUdYWGsXAAuMMSFAc6AvkMcYMwWYn5anfCS9z3ngi0uWw0lMhIiIiIiIiIiIpCotk26es9Z+Yq0NAwoCW0h8coiIiIiIiIiIiEuk5bGmyay1p4DpSS8RERERERERuQJN3nhz9P8nIiIiIiIiIl5HCQsRERERERER8TpKWIiIiIiIiIiI17muOSxEREREREREJG0cxno6hHRNIyxERERERERExOsoYSEiIiIiIiIiXkcJCxERERERERHxOprDQkRERERERMQFHMbTEaRvGmEhIiIiIiIiIl5HCQsRERERERER8TpKWIiIiIiIiIiI11HCQkRERERERES8jibdFBEREREREXEBjRC4Ofr/ExERERERERGvo4SFiIiIiIiIiHgdJSxERERERERExOtoDgsRERERERERF3AYT0eQvilhIeJiBqenQ3CrIGdWT4fgNgb/ugIZ4z+f5V3fPODpENwqpPArng7Bbc4fGunpENzK6QjydAhuE0BGT4cgcotoELzIRToaRERERERERMTrKGEhIiIiIiIiIl5Ht4SIiIiIiIiIuIAx1tMhpGsaYSEiIiIiIiIiXkcJCxERERERERHxOkpYiIiIiIiIiIjX0RwWIiIiIiIiIi7gMJ6OIH3TCAsRERERERER8TpKWIiIiIiIiIiI11HCQkRERERERES8jhIWIiIiIiIiIuJ1NOmmiIiIiIiIiAtohMDN0f+fiIiIiIiIiHgdJSxERERERERExOsoYSEiIiIiIiIiXkdzWIiIiIiIiIi4gMNYT4eQrmmEhYiIiIiIiIh4HSUsRERERERERMTrKGEhIiIiIiIiIl5Hc1iIiIiIiIiIuIDDeDqC9E0jLERERERERETE6yhhISIiIiIiIiJeRwkLEREREREREfE6msNCRERERERExAU0QuDmuDRhYYypBFhr7UZjTCmgIfC7tXaxK+sVERERERERkfTNZQkfY8yLwNvAFGPM68AkIAQYYowZ7qp6UzN82CSqPtiZsLA+qW7f8NN2KlZ4lJYt+tOyRX/eeeczAE6ePEPHDsMIC+vDsmU/JZd/9pnXORpx0i2xy9UNGzqRB6t0Iqxp71S3W2t55ZUZ1K/Xg2Zhffjtt70A7Nt3mFat+tMsrA9btvwOQFxcPF06v0BU1AW3xS9Xt2b1zzRs8DT163Vj+vR5l23/4ovlVKn8KC2a96FF8z7MnbsUgH37/qJVq340C+v1f/07Qv0rbucP16Bnn2jIpu9G8/OyN+nZtREALzzXlg1L3mD9N6/z9UdDyReaI9V9/9n/Meu/eZ3137zO3PcGJK9/f8KzbFjyBqMGtU9eN7hXS8LqV3BtY66TP52ndM0VSX/86RwlvsmVIyzaAPcBGYAjQEFrbaQx5i3gJ+BVF9adQouWtejQsRFDhrx9xTLly9/N1Gkp8yiLFq6l/cMNqFevMt27vULdug+w4vuN3H13UfKE5nR12JIGLVvVpuOjjRkyeEKq21ev/pmDB8JZsnQKv/zyB6NGTuWzuW8yZ84Shg97kgIF8/Dqq+8yceIQPv30G8Ka1SBjxgxuboWkJj4+npdemsbM918iNDQXbds8R+3alShRolCKco0aP8QLL/RIsS6xf59K6t8ZTJw4NKl/a6p/xe18/RpUqmRBujxSm2phzxMTG8dXs4aweNlmxk1byEtj5gLwTJcGDO3Tit7D3rts/6joGCo3Gppi3T13FSIqOoZKDQaz8ONhZM2SkUwZM1CxXHHemDjfLe1KC387T+maK5K++Ns5SnyTK2+pibPWxltrzwN7rbWRANbaKCDBhfVepmLF0mTPluW69wsIdBIddYGYmFicTgdxcfF8+OFCuj7Z0gVRyo2oWLE02bJlvuL25cs30LxFTYwx3HffnURGnuPo0ZMEBjiJir5AVNQFAgMCiIz8hxUrNtKiRS03Ri9Xs23bbgoVzsftt+clKCiQxk2qsXz5T9feEa7QvxvUv+IRvn4NuuuOAmzcsoeo6Bji4xNYs34nLRpV4uw/UcllMmUKxlqb5veMjYsjY3AQxhgCA5zExycw4rm2vDL28r8OepK/nad0zRVJX/ztHOWtHMY3X+7iyhEWMcaYTEkJi/IXVxpjsuHmhEVabN26ixbN+5EnT04GDurEHXcUomnTagwYMI7PPvuO5wY8xqeffEsz/TUgXYmIOEm+vLclL+fNm4uIiJN06NiYwYPHExMTx0ujnmby5M/o3r0NDoemxfEWEREnUvZd6G38sm3XZeW+W/ojmzb+RpGiBRg6tCv58uWmQ8cmDB48jpiYWF4a9QyTJ8+he/e26l/xWun5GvTbrj8ZObA9ObNnJio6hoa17mPztv0AjBzYjo6tq3Pm7Hkatn851f2DMwSyduGrxMfF89bkr/h66SZ27fmb4yfP8uPi1/j0i7UUL5IXhzFs3X7AjS27Np2nUtI1V8S76BwlvsCVCYvq1toLANbaSxMUgUCniwvGmBzW2lMujOOaSpUuxvLvpxESkpFVq36mZ883WLLkHbJkCWHatOcBOHPmH2bM+IKJEwczYsRkIs/8Q+cuzSlX7k5Phi43KH/+3MyalXhX0sGD4UQcOUHx4rczaOA4YmPj6N2nA0WLFvBwlHIttWpVpGnT6gQFBTJ79rcMGTyeDz58Nal/XwPg4MG/k/q3IIMGjk3q347qX/Ea6f0atGvP34yZ8hVffzyU8+cv8MuOg8QnJF72R775GSPf/IwBzzanR+cGqY6QuLNKL/6OOEWRQnn49tPn2b7rEPsPHmXgqA+Ty8ybOYBeQ99jUM8WlClVmOVrfuX9T793Wxtvhs5TuuaKeDOdo8TbuSxFdjFZkcr649baXy9ZtdxVMaRV5syZCAnJCECNGuWJi43j1KnIFGWmTJ5Lj+5tWLRoLeXvv5vX/9ObdybN8US4ch1CQ3MSfuR48vKRIycI/b97v8eP+4g+fTsya9ZC2rStx4CBndS3XiA0NFfKvos4TmhorhRlcuTISlBQIABt29ZLnuDtUon9+2hS/9ZnwMDOvDNptmuDF7kOvnAN+mDOSqo2GU69ti9x+sw5du8LT7F9zvy1tGhUKdV9/45I/JvFgUNHWb1+B/eVLpJie9N65dny635CMmWgWOFQHn1mAi0bVyJjcJBL2nI9dJ5KSddcEe+ic5T4Am8Y0+PGO2BSd+zYqeR7a7dt2421luzZ/73f+MCBv4mIOEGlB+4hOvoCxmEwxhB9IcZTIUsa1a5diS8XrMRay9atu8iSJYQ8ef798rRhw3by5MlJkSL5iY66gMNhcDgM0dGa/djT7r33Dg4e+Ju//jxCTEwsixetoXbtB1KUOXr03yclfP/9BooXL5hiu/pX0gNfuAblzpUVgNvz56J5w4rM+XIdxYvkTd7etH4F/tj792X7Zc8WQlBQ4mDPXDmyUKVCSXbuPpy8PSDASc+ujRg75WsyBgcl/z85nY7k/TxJ56mUdM0V8S46R4kv8PzVHtI+C9cNeq7/WDZs3M7pU2epWeNJevZ6mLi4eAAefrgBS5f8yKezlxDgdJAhOIgxY/pjzL95lAnjP6FP3w4ANGnyED2ffYMZM+bTu9fDrg5drqF//zFs3LCdU6ciqVG9K70u7dtHGlKjRnlWr/qZ+vV6EJwxA6+99u+j2Ky1TJ0yl7HjEh+j1659fQYOGEdcXDwjR/ZItT5xn4AAJyNe6E7XJ0eSEJ9A69Z1ueOOQrw94WPuuacEtes8wKxZX7Pi+w04nU6yZcvC66/3Td4/sX/nMHbcIADatW/AwAFjkvr3ac80SvySP1yDPp3Wj5w5MhMbG0/fEe9zJvI8U0d3447i+UlIsBw6fIzeQxOfEHJ/mWI82bEOzwyewV0l8jPx9SdJSLA4HIa3Jn/F75ckLHp0qs9H81YTFR3DrzsPkSljBjYufYMlK7ZyJvK8p5qbzN/OU7rmiqQv/naO8lYO4/Jfd32auZ5Zu10SgDGbrbX3p7Ytwf7mN73rMIGeDsGtLPGeDsFtDE5Ph+BW1vU5SK9hPD9ATFwkwcZ6OgS3Cin8iqdDcJvzh0Z6OgQ387p5zl3G36634rv86bsUgOFOn/5C9cSalT7ZoTOr1XRLv+mWEBERERERERHxOi6/JcQYkzOV1WetTf7zVR1XxyAiIiIiIiIi6Ys7RlhsBo4BfwC7k34+YIzZbIwpb609eWlhY0w3Y8wmY8ym6dPnuiE8ERERERERkVvPYXzz5S7umHTzO2CetXYJgDGmPtAaeB+YDKSYqtZaOx2YDu6dw+LChRgee/R5YmJiiYtPoEH9KvTq7T0TmsmNCw8/xuBBEzhx4jTGGNq1q8/jncI8HZbcIsOGTmDlyk3kypWNrxdO8nQ4IjckPV2Dpr7ZnUZ1ynHsRCQV6iVOxFamVGEmvtaVDBkCiYtPoO/wmWz6ZS9Zs2Rk5oRnuT3/bQQEOBk/bSGz5q5Kfq8FHwzm2cEzeGXoI9xfphixcfFs2rqXnkPfTZ7MccyoTjSodR/no2Lo9twUtm4/AMCXHw6hUrkS/LBpF627vOn2/4e08rdzlK65IumPv52nJH1xxwiLyheTFQDW2qVAFWvteiCDG+pPk6CgQN7/7ygWfDmO+fPHsHbtFrZu3eXpsOQWcDqdDB7ShUWLJzF7zmg+/uQb9uz509NhyS3SslUdZrw70tNhiNyU9HQNmjV3Fc0f/0+Kda8O68Cr4z+ncqOhvDxmLq8OS3yqSffH6/P77sM80HAIDdq9xH9GPEpgYOLEiMEZAsmVIzOHj5xk9oJ1lK31HBXqDSJjcBBdHq4FQINa91G8SF7uqd6PnkNm8ParXZPrHDfta7r2m+ymVt84fztH6Zorkv7423lK3MsY4zTGbDHGLExaLmqM+ckYs8cYM8cYE3S1/d2RsAg3xgw2xhROeg0CIowxTrxo6mpjDCEhGQGIi4snNi4uxWPlJP3KkycnpUsXByBz5owUL1aQiIgTHo5KbpWKFe8hW7bMng5D5Kakp2vQug2/c/L0PynWWWvJmiUx/mxZMhEecSpxPZA5qV0hIcGcOv0PcXGJl/7qVUqxev1OAJas2Jr8Xpu27qFAvsTpr5rWL88nn68BYMOWPWTLmom8ebIDsHLdb5z9J8pFrbx1/O0cpWuuSPrjb+cpcbs+wM5Llt8AxllrSwCngK6p7pXEHQmLDkBBYEHSq1DSOifQzg31p1l8fDwtW/TnoapdePDBspQtW9LTIckt9tdfEezcuU99KyJeJz1fgwaO+pDXhnVk9/pJvP58R154YzYAU/+7hLtK5GffpslsWjqaASM/5OLj1BvUuo+lK39J8T4BAU4eaVWN71Ylrs+fNyd/hf/7y+7hIyfJnze1ubzFG+maKyKS+Au3L77SwhhTEGgCvJu0bIDawLykIh8ALa71/+dS1trj1tpe1tpySa+e1tpj1toYa+0eV9d/PZxOJ/MXjGXFyhn8um0Pf/xx0NMhyS107lwUvXu/wdBhXcmcOZOnwxERSSE9X4O6PVaPQS/N4o7KPRn00iymvNkNgHo1yrBtx0GKVXiGBxoOYdxLncmSOXHEReUKJflh4+8p3mfCq0+wbsPvrNvgnbfDSNrpmisiIsB4YBD/3lmRCzhtrY1LWv4LKHC1N3BZwsIY87Ux5qsrvVxV762QNWsIlR64h7Vrtng6FLlFYmPj6N37DcLCalC/fhVPhyMickXp8RrUsXV1FnyzAYDPF66nQtnEWwIea1uTL79NXL/vYAQH/jzGncXzU6RQHg7/fYLY2Pjk9xjWtzW5c2Zh0Euzktf9feQkBfPlSl4ukDcnfx9J8XAx8UK65oqI+L5Ln+6Z9Or2f9ubAkettT/fTD2uHGHxFjAG2A9EATOSXv8Ae11Y7w05efIMkZHnAIiOvsCPP/xC0WIFPRyV3ArWWp4fPonixQrSpUtzT4cjInKZ9H4NCo84RbXKdwNQs2pp9hw4AsCffx+nZtV7AMhzWzZKFs/H/kNHaVCzLEtXbUvev/PDtahXvQyP95yYfMsIwKLvNtOhdTUAKpUrQeTZ8xw5etpdzZIboGuuiIh/sNZOt9ZWuOQ1/f+KVAWaGWMOALNJvBVkApDdGHPxaaUFgcNXq8dc+sXAFYwxm6y1Fa61LjXufKzprl0HGDpkIvHxCSTYBBo2rMqzz7pvig2HCXRbXd7AEn/tQrfIz5t20LHjMEqWLIwj6aHB/fo/So0a1/wI3hIGp1vq8RYWtx22APTv/yYbN2zn1KlIcuXKTq9ej9CmbX231G3wzkkR5eYl2Fi31ufpa1BI4VfSXPaDib2oVuVubsuRhaPHz/Dy2Hns3hfOmyMfJ8Dp5MKFWPo8P5Mtv+4nX2gOpo/pQd482THG8Nbkr5g9fy3zZg6g/wv/5dBfxwE4u+8jDh0+njyJ5pffbuT1CV8AMO7lLtSvWZbzURfoPmAam7ftA2DZvBcpWTw/mUOCOXnqLD0GTmfZ6m2pB32J84dGXuf/zs3x5DkqkXvnN/fkNdffrrfiu/zpuxSA4U6f/kLVY90K93aom0ytWivN/WaMqQkMsNY2NcbMBT631s42xkwFtllrr/jYL3ckLHYCTay1+5KWiwGLrLV3X2tfdyYsPE0JC9/lb1+g3H2R9SQlLHyXuxMWnnY9CYubFRQUwPdfjOKhpsPdVuel3J2w8DyveSCby/nb9VZ8lz99lwIlLNKrm0hYFCNxxEVOYAvwqLX2wpX2DbjShluoL7DSGLMvabkI0O3KxUVERMRXxcTEeSxZISIiIu5nrV0JrEz6eR9QKa37uiNhkRW4BygKNAMeBI67oV4RERERERERSadc/lhTYIS1NhLIQuJEG5OAKW6oV0RERERERETSKXeMsLg4WUETYIa1dpExxn03y4qIiIiIiIh4gMOnZ+hwPXeMsDhsjJkGtAcWG2MyuKleEREREREREUmn3JE4aAcsARpYa0+TOBvoQDfUKyIiIiIiIiLplMtvCbHWnge+uGQ5HAh3db0iIiIiIiIikn65Yw4LEZH/sXff0VFUbRzHv7O7CYTeQ1FpgiAgIohK76iQ0LsKSImNIjWAFCvSi3R8QSlSpAkERHqx0QUEpCMlCZ1Q0nYz7x+J0RhQELYk+/ucs4fdKTvPzb1z2X32zh0REREREa+jOSwejOaSEBERERERERGPo4SFiIiIiIiIiHgcJSxERERERERExONoDgsRERERERERJ9AIgQejv5+IiIiIiIiIeBwlLERERERERETE4yhhISIiIiIiIiIeR3NYiIiIiIiIiDiBxTDdHUKKphEWIiIiIiIiIuJxlLAQEREREREREY+jhIWIiIiIiIiIeBwlLERERERERETE42jSTREREREREREnsBjujiBl0wgLEREREREREfE4SliIiIiIiIiIiMdRwkJEREREREREPI7msBARERERERFxAo0QeDD6+4mIiIiIiIiIx/HoERaG4dHhyQNwxMW4OwSXsVrSujsElzLQVMiS8lkMH3eH4FK3fx/k7hBcJkfRie4OwaUuHXnT3SGIk5g43B2Cy9jjotwdgkvZvOyzo8g/0QgLEREREREREfE4GsIgIiIiIiIi4gQWDT5+IBphISIiIiIiIiIeRwkLEREREREREfE4SliIiIiIiIiIiMfRHBYiIiIiIiIiTmAYprtDSNE0wkJEREREREREPI4SFiIiIiIiIiLicZSwEBERERERERGPo4SFiIiIiIiIiHgcTbopIiIiIiIi4gQWw90RpGwaYSEiIiIiIiIiHkcJCxERERERERHxOEpYiIiIiIiIiIjH0RwWIiIiIiIiIk6gEQIPRn8/EREREREREfE4SliIiIiIiIiIiMdRwkJEREREREREPI7msBARERERERFxAothujuEFE0jLERERERERETE4yhhISIiIiIiIiIeRwkLEREREREREfE4msNCRERERERExAkshrsjSNlcOsLCMIxZrjyeiIiIiIiIiKRMThthYRjG8r8vAqobhpEFwDTNQGcdW0RERERERERSNmeOsHgEiABGA6MSHjf+8tyltm7ZxYt136RO7c5Mm7Yo2folS9bzwvOv0LBBNxo26MbXX38HwIkTZ2nc+F0CA7qwZ89hAOx2B+3bDSQyMtqlZZDkQkMv0a7t+wTUf5fA+j2YPWtVsm1m/G85jRv1pnGj3jQI6EmpEi24du0mV65E8EqbgTQI6Mn6ddsTt3/n7eFcuHDFlcW4Z2rHIuLptm7ZzYt136JO7TeYNm1xsvXnzl2gXduBBAZ049VXBxAWdgmAEyfO0bhxDwIDuv2tnxrkUf3UG+2q8X1IP7atDGba6Lak8bVR+fkibFjam20rg5k4rA1Wa/KPVyWL5+PbBe/yfUg/tizvS8OXyySumzLyNbYs78t7PeonLuv5Zh1erlXKJWW6V6m9br2ZN9VtdHQMLZsPoHHDPjSo34sJn319x+2+Xf0jgfV70qB+L/r0Gg/AyZPnad6kH40a9GHvniNAfHk7tv/IY8vrTXUrqZMzExblgF3AAOC6aZqbgEjTNDebprnZicdNxuFw8MEHU5n++WBWhkwkZOUWjh37Pdl2L71ciWXfjGPZN+No1qwOAAsWrGFA/05Mmz6YGTOWAjBv3moCAqvh55fGlcWQO7BZrfTp8yorVo5h3oKPmffVGo4dO5tkm9c7BLJk6QiWLB1B9x6tKPfsk2TJkoFVIdto0aI28xd+kpjo2LhxJ8WLFyBXrmzuKM4/UjsWEU/3Zz81iJUhnxGycivHjp1Jss3wYV/QoGF1lq8Yx9tvtWD0qNnAH/1UR6ZNH8iMGcuAP/qpqh7TT+Xxz0znV6tSs/FIKtX/FKvFQpOAckwc9gqd3v2CSvU/5cy5q7RsVD7ZvpGRMbzVZw4V6w2lecfJfNy/MZky+vHkE3mJio6lSuAwypR6jIwZ0uKfMxPPlC7AqnX73VDKO0vtdevNvK1ufX19mDFzIEuWDWfR0k/5ftteftl7NMk2p0+F8vn0b5g9932+WTmSvv3aAvD1gnUE92/L5Kl9+WLmSgAWzF9L/cBKHlleb6tbSZ2clrADbjqIAAAgAElEQVQwTTPONM0xQHtggGEYE3DTJJ/79h3lsfx5ePTR3Pj6+vByvcqsX//zPe3rY7MSGRVNZGQ0PjYbERE32bhxOw0bVndy1HIvcubKypMlCgGQPr0fhQrn40L43UdHrAr5npdfrgiAzWYjMiqGmJhYLFYLdruD2bNW8XqHBi6J/X6pHYuIp0veT1VK1k8dP36G55+PHznw3POlWL8+foTbnfupHR7XT9lsFtKm9cFqteDn58PtyGhiYh0cP3URgE0/HCagbulk+x0/dZETp+O3CbsQwaUrN8mRLQN2u4O0aXwwDAObzUpcXBzB3V5m2PjkIwbdyRvq1lt5W90ahkG69GmB+BED9lgHxt8mRVz09QZatqpD5swZAMiePTMAtoTyRkVGY7NZiYi4xaaNuwhsUMWlZbhX3la3nspipM6Hy/5+zj6AaZpnTdNsBqwG5vx9vWEYWZ0dQ3j4ZfLkzpH4Ord/DsLDLyfbbu13PxIY0IWuXT8lNDT+Q0XrNvWYOvVrgoPHEhTUlEmTFhAU1AyLRXeE9TTnzl3g0KGTPFX68Tuuj4yMZtu2vdSu8zwA9epXYuP6HXTq8BGdOzdi/rw1BARW8dissdqxiHi68PArf+unshP+tyTyE8UKsPa7nwBYu/Ynbt2K5OrVCFq3eZmpUxcRHDw+oZ9aSFBQU4/qp0LDrzPhfxv4ZdP7HPz+IyJuRLFs1R5sVgtPl3wUgMC6T5Mv9z9/tHnmqcfw9bFy8vdLHDkezuUrN9m4rDdrNh6gYP6cWAyDfQfP/uN7uFpqr1tv5o1163DE0aRRX6pU6swLFUrxVOkiSdafPh3K6VOhvNJ6EK1bvMe2rXsBaNW6LtOnLmNAv8l0CmrI1MlL6BTU0GPL6411K6mPy0Y8mKYZAoTcYdV64BlXxXE31as/S/36VfD19WH+/G8J7juWL2d9TN68OZk9+xMATp8+T3jYZQoXfoQ+vUcTG2una7c2FCyYz83Ry61bUXTvOorg4HZkyJDujtts2riLMmWeIEuW+Gx5xozpmDy1HwDXr9/k8+nLGPdZbwYNnEJExC3atQvg6TJFXVaGh0HtWEQ8XZ8+7fnow2ksXbqBcuVK4O+fHavVktBPfQzEf1mI76cepU/vMQn9VGu391OZM/nxcs1SPFPjfa7fuM3M8a/TLLAcHd/9go/6NyaNr42N2w7jiIu763v458zE5OGv8nbfuZimCcCAT5Ykrp87pTM9B82nxxt1KFEsH5t+OMzshT86vWwPQ0quW/lnqa1urVYLi5cOIyLiFt26jOLokTMUKfpo4nq73cHp02HM/HIQ4eFXaPvqEJZ+M4I8eXPwxazBAPx+OoywsMsUKpSP4D4TiI2106VrcwoUzOuuYv0nqa1uJfXxhBSZ0weU+PtnJzRhAhmAsPBL+PtnT7JN1qyZ8PX1AaBZs9r8+uvxZO8zdswcunV/hdmzV9K0WR169W7HxAnznRu8/KvYWDvdu42iXkBlatd57q7brV71PS/Xq3THdVMmL6bzG41ZFbKNZ8oW45OhbzNx4p0nYXIXtWMR8XT+/tn+1k9dxt8/W7JtPpsQzNJlY+j+bhsAMmXKkGSb+H6qTUI/VZtevdsyccIC5xfgX1St8ASnz17m8tWb2O1xrPzuF8qXKcjOvaeo33octZuO4ocdxzl+8uId98+YPi3zpgXx0ZgQdv5yKtn6l2qW4pdfz5A+XRoKPJaDDt1nElj3afzS+ji5ZP8utdetN/Pmus2UKT3ly5dg27a9SZb7585O9Rpl8fGx8cgjuShQIA+nT4cl2Wbc2Pl07daCuXO+pUnTGvTs1YbJk5JPaulO3ly3knp4QsLCdPYBSpUqwulT5zl7JoyYmFhWhWylRo2kX2z/eleIDRu2U7jwI0nWb99+gFy5slGgQF6iIqOxWAwsFoOoKM2S606maTLovSkUKpSPdu3q33W7Gzdus2PnQWrUKJds3elToYSHX6Z8+RJERcVgMSwYhkF0VIwzQ79vasci4uni+6lQzp4JT+intlGjRtIJKK9eiSAuYQTCtGmLadKkZpL1ntxPnTt/lXJPF0hMIFR5oShHToSTI1v8h3tfHxvdOtfki/nbku3r42Nl1qQOLFi2gxVr9iZbb7NZCGpbjc+mryNtWp/E0RdWqwUfH7dMAZZEaq9bb+ZtdXvlSgQREbcAiIqK4ccf91Hwb6MiatYsx47tBwG4ejWCU6dCefSRXInrd2w/SK5cWclfIE9ieQ2LQWSkJ3529J669VTWVPpwFff/D+gCNpuVgYOC6NBxCHGOOJo0qUWRIo8xftxcSpZ8nBo1n2P27BVs3LAdq9VK5swZGTq0e+L+pmkyZfICRo/pA0DzFnXp3WsUdruDIUPedE+hBIDdu39j+fItFC36GI0b9Qage/dWhIbGZ5NbtIy/S8a6ddupWKE06dKlTfYe48bNo1u3VgC8XK8iXd8ZwefTl/FO1+YuKsW9UTsWEU8X3091okPH94lzOP7ST32V0E+V5+ftBxgzejYYBs+We5JBg4MS94/vp75m9JheADRvUYfevcYk9FNvuKtYiXbtO83yNXvZuKwPdruD/YfO8eX8H+j/bj3qVi+BxTCYMe97tv4Uf8eBp0s+SrtWleg+YB4NXyrDC+UeJ2uW9LRqHP+F4Z3guRw4dA6ADm2qsGDpz0RGxfLr4fP4+fmydUUw6zYfJOJGpNvK/IfUXrfezNvq9uLFqwzoNxmHIw4zLo66L75AteplmTB+ISVKFqJ6jXJUrFSaH77fR2D9nlgtFnr2eoUsWTMC8eWdOmUpo0Z3A6Bp81oE9/4MuyOOgYM7uLNoyXhb3UrqZPyRwXdbAIaxxzTNMndaZ/Kbe4NzIcP5V8Z4FHuc+z98uYrVkjxJkpp5W1sWSQ1MHO4OwWVyFJ3s7hBc6tIR70lIGy79zc/9vOm8tcdFuTsEl7J53WfH4qn6w+NHe9alyu+075Wp5ZJ6c/oIC8Mwst1h8Q3TNGMTnte8w3oRERERERER8WKumMNiN3AROAIcTXh+yjCM3YZhlDVNM8m9dQzD6GwYxk7DMHZOm6bJXERERERERCRlshhmqny4iivmsFgLLDJNcw2AYRh1gCbATGASkGTWQNM0pwHTwLsuCRERERERERGRP7kiYfG8aZqd/nhhmuZ3hmGMNE0zyDCMNC44/j3r328cmzbtJHv2zKxYOcHd4chDFBFxi0EDp3Ds6BkMw+DDj97k6TJF3R2WU6gdi4inCw29SN8+47h8+RqGYdC8eR1eaxvg7rCSGf9Ja+pUL8GlyzeoVP9TAEoUy8uo91uQPl0afj93hTd6zuLGrShsNgvjPm7FU08+is1mYcGyHYydujbxvUa+35yF3+zg5VpP8WKNksTE2Dl15hLvBH9FxI1IqlV4goG9AvH1sRIT62DI8GVs/ekofml9mDH+dQo+lgOHI441Gw/wwcgV7vqT/KuUUrdy/7ytbmd9EcLiRRsxDChS9DE++uQN0qTxdXdYTuFtdSspiysuCQk1DKOvYRj5Ex59gHDDMKxAnAuOf88aNa7J9M+HuDsMcYKhn8ykUqWnWblqLIuXjqBQ4XzuDslp1I5FxNNZrVb6BrcnZNUE5i8YztyvVnPs2Bl3h5XMvCU/07xD0kk6x33cig9GrqBywKeErN3HOx1rANDgxTL4+tqoHPApNRqNoG2LCjya789pvMo9XYCde0+x6fvfqFhvKFUCh3H85EXeDaoNwOWrt2jzxlQqB3zK233nMHnEq4n7TvzfBp5/8WOqNRxO+WcKUbNKcReU/r9JKXUr98+b6jY8/Apz53zLgkWfsGzFSOLi4li96gd3h+U03lS3kvK4ImHRGngEWJbweCxhmRXwqPtGPvtsSTJnzuDuMOQhu3HjNrt2HqJJ0/gPlb6+NjJlSu/mqJxH7VhEPF2uXNkoUaIwABky+FG40COEh192c1TJ/bjzOFev306yrHCBXPyw4xgAm74/TEDdp4H42/+l80uD1WohbVofYmId3LgZf2eDooX9OX7yInFxJpu+P4zDEf97zc5fTpEndxYA9h86S9iFCAAOHw0lbRoffH1sREbFsu3n+NukxsY62PfrGfIm7OOJUkrdyv3ztrq1OxxER8VgtzuIjIwmZ66s7g7Jabytbl3NYqTOh6s4/ZIQ0zQvAV3usvqYs48vcvbsBbJmy8SA/pP47bfTlHiyEMH925EunXfdMkpExBOdPRvOoUMnKF06ZVymd/hoGC/XKsWqdftp8FIZ8iUkD5av2ctLtUpx8PuP8Evrw3tDl3ItIdlRs8qTrN96KNl7tW7yPMtW7U62PKDu0+w7eJaYWHuS5Zky+lG3RkmmztrshJI9fCmtbuXepfa69ffPRrv29alV823SpvGlQsWnqFixtLvDconUXreS8jhthIVhGCsMw1h+t4ezjivydw6Hg0MHT9KyZR0WLxmOX7o0fD59mbvDEhHxerduRdK16zD69e9Ahgzp3B3OPenafy6vt67M+iW9yZA+DTGxDgCeeSo/DodJiUrv8UyN93m7fXXyP5odgBqVirFh68Ek79PjjTo4HA6+Xr4zyfInHs/N4N6B9BiY9E5pVquF6WPaMm3WFk6f8fxfPlNi3cq98Ya6vX79Jhs37GLN2s/YsHkykZHRrFi+1d1hOZ031K2kPM4cYTEy4d/GQG5gTsLrVkC4E48rkoS/f3b8/bPzVOkiANSp87wSFiIibhYba6dr12EEBFSlTp0X3B3OPTt64gJNX58EQOECOalTrQQATQPKsWHrIez2OC5ducnPu0/ydMnHuHAxgsyZ/BIv9wBo1ag8daqXoFHbpBMj5/XPwqyJHXmrz2xOnbmUZN2YD1ty4tRFpn65ybkFfAhSat3Kv/OWuv3pxwPky5eTbNkyAVCzVnn27jlCQGBlN0fmPN5St5LyOG2EhWmam03T3AxUNE2zhWmaKxIerYHUe7aLx8mZMwu582Tn5MnzAPz0034KP/6Im6MSEfFepmny3oAJFC70CO3bN3B3OPclR7b4OYIMw6DnW3WZOe97AM6ev0rl5+MT4+n8fCn3dAGOngin0vNFEuegAKhRuThdOtWizRvTiYyKTVyeKaMf86YH8eGo5WzffTLJMft3r0emjGnp//ESZxfvgaXkupV/5k11mydPdvb9cozIyGhM0+Tnnw6k6gnbvaluJeUxTNN07gEM4xBQzzTNEwmvCwEhpmn+6xTXJr85N7i/6dFjBDu2H+Dq1QiyZ89Cly6taNqsjkuObeDCmUs8gD0u0qXHO3ToFIMHTiE21s4jj+bio4/fctnElFaLa+fKcGc7Bu9ryyKpgYnDpcfbtfMgbdr0p2jR/FgSZu56t8crVK1azunHzlF08r9vlGDa6LZULP842bNm4OLlG3w6fhXp06WhQ5v4311C1v6SeIvR9Ol8+WxoG554PDeGYfDV4p+Y8L8NDBvUlOXf7uX77fHTdu1YO5A0vjauXLsFwM69p+g1eCE936xDt6DanDh9MfH4TdtPwtfHxv6tH3DkeBjRMfFzWnw+Zytzvv7xnspw6cib91zeh8GddWtgdfoxPIk3nbf2uCinH+PvJnz2NWtW/4jVaqFY8QJ88FEQvr4+Ljm2zcWfHd1ZtwAGxVP1h8fh+9a69Dutq/R5qrZL6s0VCYu6wHTgRMKiAkBn0zS/+7d9XZ2wcCdv+5Ln6oSFO7k6YeFu3taWRVIDV3/xcaf7SVg8DBuW9qZOs1HY7e65k7urExbupIRF6uWOhIU7uTph4W5KWKRMrkpYOP0uIUAmoCRQEAgEKgCX/nEPERERkVSgRqMR7g5BREQkxXLaHBZ/MdA0zQggI1ADmAC49ucNEREREREREUlRXDHC4o/xavWA6aZphhiG8ZELjisiIiIiIiLiNtZUfcGL87lihMU5wzCmAi2AVYZhpHHRcUVEREREREQkhXJF4qA5sAaoa5rmNSAb0NsFxxURERERERGRFMrpl4SYpnkbWPKX16FAqLOPKyIiIiIiIiIplyvmsBARERERERHxOhbNYfFANJeEiIiIiIiIiHgcJSxERERERERExOMoYSEiIiIiIiIiHkdzWIiIiIiIiIg4gcUw3R1CiqYRFiIiIiIiIiLicZSwEBERERERERGPo4SFiIiIiIiIiHgcJSxERERERERExONo0k0RERERERERJ7AY7o4gZdMICxERERERERHxOEpYiIiIiIiIiIjHUcJCRERERERERDyO5rAQERERERERcQKruwNI4TTCQkREREREREQ8jhIWIiIiIiIiIuJxlLAQEREREREREY+jOSxEREREREREnMBiuDuClE0jLERERERERETE43j0CAvTdLg7BNcxvCv1ZrP4uTsEl4kzY90dgmsZ3pQH9aaygoH39FMmprtDcCnTjHN3CC5z8bcgd4fgUv7FPnd3CC4Tfrizu0NwKcOr7j3gPf//xPOuzxci/0Rng4iIiIiIiIh4HI8eYSEiIiIiIiKSUlkM7xq1+bBphIWIiIiIiIiIeBwlLERERERERETE4yhhISIiIiIiIiIeRwkLEREREREREfE4mnRTRERERERExAms3nZX3odMIyxERERERERExOMoYSEiIiIiIiIiHkcJCxERERERERHxOJrDQkRERERERMQJLJrD4oFohIWIiIiIiIiIeBwlLERERERERETE4yhhISIiIiIiIiIeR3NYiIiIiIiIiDiB5rB4MBphISIiIiIiIiIeRwkLEREREREREfE4SliIiIiIiIiIiMfRHBYiIiIiIiIiTqA5LB6MRliIiIiIiIiIiMdRwkJEREREREREPI4SFiIiIiIiIiLicZSwEBERERERERGPo0k3RURERERERJzAapjuDiFFc1nCwjCMSkB54IBpmt+56rgiIiIiIiIikvI47ZIQwzC2/+V5J2ACkBEYbBhGsLOOeycD+n9GxQptCQjoesf1J06cpWWLvjxVqhkz/rcscfmVK9dp07ofAQFdWbfu58Tlb7/1CRfCrzg97v9q65bdvFj3LerUfoNp0xYnW3/+/EVee/U9GjV8l8CAbmzevBOA3bsOERjQjSaNe3Lq1HkAIiJu8vrrg4mLi3NpGeTOBvSfQMUK7QgI6HbH9dt/PsCz5V6hUcMeNGrYg4kTFwJ/tOX+BAR0+1tbHqq27EG2btnFi3XfpE7tzkybtuiu261Z8wPFnghk//6jAOzedZDAgC40adzjb+Ud5NHl9SbeUrfe1kd5Q3mD2lZhy4rebF7eiymjXiGNr43Kzxdh3eJ32bC0ByvmvkPBx7In2y9rlnQs+fJNTu76hKEDGyUu9/WxMn96JzYv70X7VhUSl4/8oCmlnsznkjLdK285b71NdHQMLZv3p3HD3jSo35MJny1Mts3OHQdp1rgvpUu24rs1PyUuP3nyPM2bBNOoQW/27jkCgN3uoGP7D4mMjHZZGe6H2rGkdM6cw8LnL887A7VN03wfqAO0ceJxk2nYqAbTpg+66/rMmTMw4L2OvP56gyTLQ1ZupUXLuixcOIJZX64AYOOGHRQvXohc/tmcGvN/5XA4+OCDqUz/fBArQz4jZOVWjh07k2SbyZMX8tJLFVm6bAyjx/Ti/fenAjBz5jdMmz6Q/v07MH/+twnbfk1QUFMsFk134gkaNqrOtOkD/3GbsmWLs3TZaJYuG83bbzcHIGTltoS2PJxZX64E/mjLBdWWPcSf5R3MypCJhKzcwrFjvyfb7ubN28yetZzSpYsmLps5cxnTpg+mf/+OzJ+/Goj/2wQFNfPY8noTb6pbb+qjIPWXN3euTHR8tRJ1mo6hauBIrBaDhvXKMHxIE97sPZcajUazZOVu3n2zdrJ9o6PtDBv3LUOGr0iyvHqlYvy86yTVGoyiWYOyAJR4Ig9Wi4X9B8+5pFz3wpvOW2/j6+vDjJmDWLJsBIuWDuP7bb/wy94jSbbJkzcHHw19i5frVUyy/OsF6wju347JU4P5YmZ8214w/zvqB1bGzy+Ny8pwr9SOJTVwZmuzGIaR1TCM7IBhmuZFANM0bwF2Jx43mWefLUGWzBnvuj579iyUKlUEmy3pFTI2HxtRkTHExMRitVqw2x3MmrWCDh0b3eWd3G/fvqM8lj8Pjz6aG19fH16uV4n1639Oso1hGNy8GQnAjRu3yJUr/sORzWYlMjKayKhofGw2fv89lLDQSzz3XCmXl0Pu7N/a8t3YfKxERUb/rS2vVFv2IMnLWzlZeQHGj5tLx05N8E3jm7jMZrOluPJ6E2+qW2/qo8A7ymuzWkmb1ger1YKfny/hF65jmiYZM6QFIGPGtIRduJ5sv9uRMfy8+yRRMUk/8sXaHfj5+eBjswAGAH27vcSn4791elnuhzedt97GMAzSpY9vv3a7A3usHcMwkmyTL18unngif7Iv5jablcioaKIio7HZrERE3GLTxt0ENqjisvjvh9qxZ7Ck0oerOPNYmYFdwE4gm2EYeQAMw8jAH/9Debj69SuzfsN2Orw+hM5BTZn31WoCA6t5ZAb1D+HhV8iTO0fi69z+2Qn/2/DSd95pyfIVm6hapQNBnT/kvfc6AdA5qAnBfccxbepi2rzyMmPHzKVbd5cOhpGHYO/e32jY4F06d/qQo0fjs+h/tuX36RzUhHlffUtgYFW1ZQ8SHn75b+XNQXj45STb/PrrcULDLlGt2rNJlncOakpw3zFMm7qINq/UY+yYOXTr/opL4pZ/p7pNKrX0UfcqJZc37EIEk2ZsYs+GgezfOpgbN6LY9P0R3n1vIV9N68jeTQNpFliW8dM23PN7bv7hCI/my8bqBd34fM5W6lYvwf6DZwm/EOHEktw/nbepm8MRR5NGfahSqRMvVHiKp0oXuaf9WrWuy/SpyxjQbxKdghoxdfJiOgU19NgRB2rHkho4bdJN0zQL3GVVHJD4E4JhGFlN07zqrDgeRMaM6Zk69T0Arl+/yfTpS/jss74MHDiRiOu3aNc+kDJlirk5yvsXErKVRo1q8PrrDdmz5zB9+4xlxcrxFC9eiAULhwOwY8ev5MyZFdM0ebf7CGw2G32D25MjRxY3Ry//5MkShVi/YSrp0/uxefMu3nlnGGvWTPyHtjyJiOs3ade+AWXKPOHm6O+fN7XluLg4Pv30fwwdmvxa+fjyjgRgx44DfynvcGw2K32DXydHjqyuDlnukTfVrbf1USm9vJkz+fFizRKUq/Ux129E8r+xbWka8Az1apeidefP2b3vd95+vRofBDegx8Dk8wDcicMRx5u95gJgs1lY8HlnXnt7Jh8EB5IvTxYWLtvFmo2/OrFUD4c3nbepkdVqYfHS4URE3KJbl5EcPfI7RYo+9q/75cmbgy9mDQbg99NhhIVdoVChfAT3mUBsrJ0uXZtToGBeZ4f/0KgdS0rg8nSgaZq3TdM8+ZdF610dw38xedJC3ghqSkjIVso+U5yhn3Zl4oQF7g4rGX//bISGXUp8HRZ+Gf+/XQ+7eNE6Xnop/pq8MmWKER0dy9Wrf/6yYZomkycv5M23mjNxwgJ69W5Ls+a1mT17pWsKIf9ZhgzpSJ/eD4CqVctij7UnqVuAyZO+TmjL29SWPYi/f/a/lfcS/v5/TmR361YkR4+c5rXXBlCjRkd+2fsbb735ceLkWPDX8rZg4oT59OrdjmbN63pkeb2J6vZPqamPuhcpvbxVXijC72evcPnqLez2OELW7qP8MwUpUSwvu/fFjxZZtnovz5bJ/5/ev32riiz8ZhflSucn4kYUnd6dzZuvV32YRfjPdN56h0yZ0lO+fAm2bfvlvvcdN3Y+Xbu1YO6cb2nStAY9e7Vh8qS7T2rpDmrHkhp4wvglj7885NSp84SHX6b8cyWJiorGsFgwDIOo6Bh3h5ZMqVJFOH0qlLNnwomJiWVVyDZq1CifZJs8eXLy44/7ADh+/AzR0TFky5Y5cf2yZRupWqUsWbJkJCoqGovFwGIxiPLQ2Y/lTxcvXsU04+/1vG/fUUzTJEuWP6+vTt6WDbVlDxFf3vOcPROWUN6t1KjxXOL6jBnT89PPc9mw4XM2bPic0k8/waTJAyhV6s9hrMuWbUgx5fUmqts/paY+6l6k9PKeC71G2dL58UsbP4965ReK8NvxcDJm9KNQgfhh5lUrFOXoiQv3/d6ZM/lRp9qTLFy2E7+0PsTFxWGakDaNz7/v7AI6b1OvK1ciiIi4BUBUVAw//rifgvc5KmLH9oPkypWV/AXyEBUZX7eGxSAy0jPO3T+oHXsGi5E6H67itEtC7oPp7AP07DGK7Tt+5drVCKpV7cg7XVpit8dPAtWy5YtcvHiVZk17c/PmbSwWg1mzVrIyZDwZMqQDYNzYP69/r1evMu+8/SnTpy+ha5dWzg79vtlsVgYO6kSHju8T53DQpEktihR5jPHjvqJkycepUbM8fYPbM/C9iXz5xQoMA4Z+2jVxsqHIyGiWLtnA/2YMAaBd+0A6d/4QHx8bI0f2dGPJBKBnj9Fs33GAa1dv/KUtOwBo2bIu3635kXnz12CzWkiT1pdRo3okmUhq3Niv6Na9NQD16lXinbeHMX36Urp2aemW8vwTb2vL8eUNokPHIcQ54v5S3rkJ5X3uH/f/s7zvA9CufQM6d/7AY8vrTbypbr2pj4LUX97d+35n5Xf7WLekB3a7gwOHzjF7wY+Ehl1jxvh2mHEm1yJu071//IiQutVL8HTJRxj22RoAdq4fQMb0afH1sfJSzZI07zCNI8fDAej5Vm3GTF2HaZps3PYbr7epyOblZfhywY9uK+9fedN5620uXrzKgH6TcDjiMOPiqPviC1SrXpYJ4xdSomQhqtcox/79x+jeZVTCpJq7mPjZ13yzchQQP+Jg6pQljBrdHYCmzWsS3Psz7I44Bg7u4M6iJaN2LKmB8Ufm320BGMZu0zSfudO6OPOge4Nzob/PTpzaGQ9R77cAACAASURBVFjdHYLLxJmx7g7BpQzDEwZuuYo3lRUMzx8Q99CYzs+lexTTdOnNu8SFchef4e4QXCb8cGd3h+BS3tQnx8bddncILmWz+Lk7BJcyeCJVN+aFJ75NlR8qmhd60SX15gmftlN1AxURERERERGR++f0S0IMw8h2h8U3TDPxZ+eazo5BRERERERExNVcOd9DauSKERa7gYvAEeBowvNThmHsNgyjrGmaV/66sWEYnQ3D2GkYxs5p0+7tFlkiIiIiIiIikrq4YtLNtcAi0zTXABiGUQdoAswEJgFJZnsxTXMaMA1cO4dFdHQMr74ygJgYO3aHg7p1XqBLV8+bVPNhCQ29SN8+47h8+RqGYdC8eR1eaxvg7rDkIXI4HDRr2odcubIxZeoAd4fjNN7Ulvv3G8emTTvJnj0zK1ZOcHc48hB5Y916Sx/1B08vb97cWZgwrBU5s2fANGH2wp+YPnsrfbu+yEs1SxAXZ3Lpyk269JtP+IX4W7N+PKAhtaoUJzIqhi795rP/4LnE95s3rSM9Bn7NoF71KV3yEWJjHezZf4Zeg7/Gbo8DoEL5wnzUrwE2m5Ur127R8NVJAIz9uAW1qxXn0uWbVA0c6fo/xn3wxnPXW8z6IoTFizZgGFCk6GN89MmbpEnj6+6wnELtWDyZK0ZYPP9HsgLANM3vgBdM0/wJSOOC498TX18fZn7xAcu+GcPSpaPZtm0Pe/f+5u6wnMZqtdI3uD0hqyYwf8Fw5n61mmPHzrg7LHmIZs8KoVChR9wdhtN5U1tu1Lgm0z8f4u4wxAm8sW69pY/6g6eX1+5wMHjYcirXH8FLLcfzepuKFC3sz8T/baRag1HUaDSa7zYdpNdbtQGoWaUYhfLn4Lm6Q+k56GuGD26S+F5p09jImiU9oeHXWbRiFxVeGkbVwJGkTevDK03jf6fKlDEtwwY15tW3ZlAlYAQdu81K3H/+0h207DTdtX+A/8gbz11vEB5+hblzVrNg0VCWrRhFXFwcq1f94O6wnEbtWDyZKxIWoYZh9DUMI3/Cow8QbhiGFYhzwfHviWEYpE8fPyOv3e4g1u5I1XfuyJUrGyVKFAYgQwY/Chd6hPDwy26OSh6WsLBLbN68i6bNark7FKfzprb87LMlyZw5g7vDECfwtrr1pj4KUkZ5L1y8kThC4tataI4cDyePf2Zu3opO3Cadny9/3FzupZolWfjNLgB2/fI7mTP5kStnRgAqlH+cH7YfB2D9lsOJ++/Z9zt5cmcBoEn9ZwhZu59zodcAuHTlZuJ2P+08wbXrKeOuEN527noTuyOO6KgY7HYHkZEx5MyV1d0hOY3asXgyV1wS0hoYDCxLeP19wjIr0NwFx79nDoeDpk168fvvYbRq/RKlSxd1d0gucfZsOIcOnfCa8nqDoZ/MoFev17h1K9LdobiU2rJIyuBtfVRKK++j+bJSqng+dv1yGoB+3V+ieYNyRNyIpHHbyQDk9s/M+YRkA8D5sOvk8c/MhYs3qFmlGKvXHUjynjabhWaBZRnwSfzHwUIFcuJjs7J01ptkSJ+G6bO2JiZARNzN3z8b7drXp1bNt0ibxpcKFZ+iYsXS7g5LUiirkSrvauoyTh9hYZrmJdM0u5imWSbh8Y5pmhdN04wxTfOYs49/P6xWK0uXjWHjps/Zv+8oR46cdndITnfrViRduw6jX/8OZMiQzt3hyEOwceNOsmXPTImShd0dikupLYukDN7WR6W08qZP58uM8W0ZOPSbxNEVQ8eupkz1D1m8cjcdXqn0r+9R/pkC/Lz7ZJJlwwY14cedJ/h5V/xym83CUyUeoU3Q/2jRYTo93qxNoQI5Hn6BRP6D69dvsnHDTtasncCGzVOIjIxmxfKt7g5LxCs5bYSFYRgrgLumk0zTDHTWsR9UpkzpKf9cSbZt3UPRovndHY7TxMba6dp1GAEBValT5wV3hyMPyZ7dh9m4YQdbNu8mJiaWmzdv06f3WIaP6O7u0JxGbVkk5fC2PiollddmszBjfDsWr9hNyNr9ydYvXrGbr6Z2ZPhnawgLv07ePFkS1+XNnZnQ8OvkfyQb50OvExvrSFzX6+065MiWgXZdvkhcdj7sOlev3eZ2ZAy3I2P4cecJSjyRlxOnLjm1jCL34qcf95MvXy6yZcsEQM1a5dm75zcCAiu7OTIR7+PMERYjgVHASSASmJ7wuAkcd+Jx/5MrV64TEXELgKioaH784RcKFsrn5qicxzRN3hswgcKFHqF9+wbuDkceoh49X2HT5s9Zv2Eqo0b14LnnSnnkB+OHRW1ZJGXxtj4qJZV37EctOHI8nClfbElcVjD/n6MeXqxZkmMnLwDw7YZfad6gLABlSz9GxI2ohMtBirNh65/zVrRp+hzVKz1BUM/ZmOafv2N9u/4A5Z8piNVqwS+tD8889RhHT1xwdhFF7kmePDnY98tRIiOjMU2Tn386QKHCqfd7gYgnc9oIC9M0NwMYhjHKNM1yf1m1wjCMnc467n918eJV+gWPx+GII86M48UXK1K9+rPuDstpdu86xDffbKJo0fw0bBD/wendHq9QtWq5f9lTxLN4U1vu0WMEO7Yf4OrVCKpWaU+XLq1o2qyOu8OSh0B1K+723DMFad6wHAd/O8+GpT0A+HjMKto0fY7CBXJimiZnzl+l9+BFAKzbfIhaVYqz/bt+3I6KpVv/+QBUr/wE/T9amvi+I4Y04ez5q6ya3xWAkLX7GTVpLUdPXGDj1sNs+qYncXEmcxf9zOGjYQBMGfUKFZ8tTLas6dm7aSDDP1vDV4u3u/LPcc907qZOT5UuQu26z9G8STBWq4VixQvSrLnnTpr7oNSOncuSeu/j4BLGX7PdTjmAYRwC6pmmeSLhdSEgxDTN4v+2b5x50GtmKEnNdyS5EwOru0NwmTgz1t0huJRhuOLmQ57Cm8oKBt7TT5l3v6IxVTJNu7tDECfJXXyGy47l62Nl5bwu1Gk61mXH/Kvww53dclx38aY+OTYuZdw15mGxWfzcHYJLGTyRqhvzN6dXp8oPFQ3yv+SSenPFXUK6A5sMwziR8LoA4F3/o4iIiIikcjGxDrclK0REJHVyRcIiE1ASKAgEAhUAzagkIiIiIiIiInflioTFQNM0vzYMIyNQg/jJOCcDz7ng2CIiIiIiIiJuoTksHowrLsD+475W9YDppmmGAL4uOK6IiIiIiIiIpFCuSFicMwxjKtACWGUYRhoXHVdEREREREREUihXJA6aA2uAuqZpXgOyAb1dcFwRERERERERSaGcPoeFaZq3gSV/eR0KhDr7uCIiIiIiIiLupDksHowuzRARERERERERj6OEhYiIiIiIiIh4HCUsRERERERERMTjKGEhIiIiIiIiIh7H6ZNuioiIiIiIiHgjqybdfCAaYSEiIiIiIiIiHkcJCxERERERERHxOEpYiIiIiIiIiIjH0RwWIiIiIiIiIk5gMUx3h5CiaYSFiIiIiIiIiHgcJSxERERERERExOMoYSEiIiIiIiIiHkdzWIiIiIiIiIg4gUYIPBj9/URERERERETE4yhhISIiIiIiIiIeRwkLEREREREREfE4msNCRERERERExAkshrsjSNk0wkJEREREREREPI5Hj7CwGB4dnjyAmLgId4fgMr6WTO4OwaXscZHuDsFlbBY/d4fgUnGm3d0huIzdvO3uEFzK2/opbxJ+uLO7Q3CZwmVWuDsElzq2+2V3h+AyVsPX3SGIiJtohIWIiIiIiIiIeBwlLERERERERETE4+iaCxEREREREREnsGrSzQeiERYiIiIiIiIi4nGUsBARERERERERj6OEhYiIiIiIiIh4HM1hISIiIiIiIuIEFsN0dwgpmkZYiIiIiIiIiIjHUcJCRERERERERDyOEhYiIiIiIiIi4nE0h4WIiIiIiIiIE1gMd0eQsmmEhYiIiIiIiIh4HCUsRERERERERMTjKGEhIiIiIiIiIh5Hc1iIiIiIiIiIOIHmsHgwGmEhIiIiIiIiIh5HCQsRERERERER8ThKWIiIiIiIiIiIx1HCQkREREREREQ8jibdFBEREREREXECjRB4MPr7iYiIiIiIiIjHUcJCRERERERERDyOEhYiIiIiIiIi4nE0h4WIiIiIiIiIExiGuyNI2Zw2wsIwjOcMw8iU8NzPMIz3DcNYYRjGMMMwMjvruCIiIiIiIiKS8jnzkpAZwO2E5+OAzMCwhGUznXhcEREREREREUnhnJmwsJimaU94Xs40ze6maW4zTfN9oJATjyteyOGIo1nj/rz9xohk63buOETzxgN4uuSrfLfm58TlJ0+ep3mTATRuEMzePUcBsNsddGz/CZGR0S6LXe4sNPQS7dq+T0D9dwms34PZs1Yl2+bGjdu89eanNGrYm8D6PVi6ZCMQX7fNmvSlUYNe7N1zBIiv2w7tP1TdeoAB/T+jYoW2BAR0/cft9u8/SskSTVjz7Q8AnDxxjiaNe9IgsDt79hwG4uu1ffvBHl+v6qMkNdi6ZRcv1n2TOrU7M23aomTrh37yOQ0bdKNhg27UrfsGz5ZrBcCJE2dp3PhdAgO6JD132w30uLb8epuyfLuoHau/bse4ofXw9bXy6eC6hCx4jVUL2jJxRCDp/Hzuun/e3BnZ/31XOr5aDoBsWf1YOKMlq79uR+1qjyduN3VMQ3LlTO/08twrb+qXvams4B3nraRuzkxYHDAMo33C818MwygHYBhGUSDWiccVLzRn9rcULJT3juvy5M3Bh0ODeLlehSTLv16wgeD+rzFpam++nBkCwIL566gfWBE/vzROj1n+mc1qpU+fV1mxcgzzFnzMvK/WcOzY2STbzPvqWwoXfoSly0bwxawhDB8+i5gYOwsXrCW4f3smT+3HzJnLAVgw/zvqB1ZW3XqAho1qMG36oH/cxuFwMGrkLCpUfDpx2YIFa+g/oANTp73HzBnfADB/3rcEBlT1+HpVHyUpncPh4IMPpjL988GsDJlIyMotHDv2e5Jt+vXvyLJvxrHsm3G88kp9atd+Hog/dwf078S06YOZMWMpAPPmrSYgsJpHtWX/nBlo2+oZGrSZw0vNvsBisRBQtxgfjdxIvRazeLnFl5wPi+C1lmXu+h4DelZn8/cnE18HvFiMuYt+odGrc2jfpiwANaoU4uDhC1y4eMvpZbpX3tQve1NZveG8TQmMVPpwFWcmLDoCVQ3DOA48CfxoGMYJYHrCOpGHIizsMls376VJ0+p3XJ8vX06eeOIxDEvSU8tmsxIVFU1UZAw2m5WIiFts3ribwAaVXRG2/IucubLyZIn4wVjp0/tRqHA+LoRfSbKNYRjcuhWFaZrcvh1F5swZsNksf6nbaHxsNiIibrFp4y4aNKjijqLI3zz7bAmyZM74j9vMmbOK2nVeIHu2P6c8svnYiIqMITIyGptPfL1u3LiDBg2rOTniB6M+SlKDffuO8lj+PDz6aG58fX14uV5l1q//+a7bh4RsoV79+D7Xx2YlMiqayMQ++SYbN26nYcM7nxPuZLUapE1jw2o18EtrI/ziTW7eiklcnzaNDdO88761qz3OmXPXOXr8cuIyuz0Ov7Q++PpYiXPEYbUavN66LFO/3O7sotwXb+qXvams3nLeSurmtLuEmKZ5HWiXMPFmwYRjnTVNM/yv2xmGkdU0zavOikNSv+FDZ/Nur1bcvhV5X/u1al2b/sFTiImJZdD7HZg6eSmdghpgsehuv57m3LkLHDp0kqdKP55kees2L/L2W8OpViWIW7cjGTXqXSwWC61av0j/4AnExMQy+P3OTJm8mE5BjVS3KUR4+GXWrf2JL2d9yID9ExKXt279EsF9xxETE8v7H7zJ5EkLCQpq6vH1qj5KUoPw8MvkyZ0j8XVu/xz8su+3O2577twFzp0N5/nnnwKgdZt69O07hpiYWD54/y0mTVpAUFAzj2vL4Rdv8vmsnWxb3ZmoaDvbfjzFtp9OAzB8yItUq1SQoycu8/HoTcn2TefnQ1D78rz2xtd0eu3ZxOXLVx9i7Cf1adXkKYaN28IrzZ9machBoqLsyd7Dk6W2fvmfpKayesN5K6mf029rappmBPDLP2yyHnjG2XFI6rR5426yZctMiRIF2bH94H3tmydvDmbOeg+A30+HER52hYKF8tGvzyRiY+2807UZBQrmcUbYch9u3Yqie9dRBAe3I0OGdEnWbdv2C8WK5WfmF4P4/fdwOnX4kLLlipE3bw6+mDUEgNOnwwgPu0yhQvkI7vMZsbF2unRtQYGCdx6eL+439JP/0bPXa8k+FOXNm5NZsz8C4PTpUMLCL1Oo8CP06TOW2Fg7Xbu2omDBfO4I+a7UR4k3WhWylTp1K2C1WoH4c3f27E8AOH36POFhlylc+BH69B4df+52a+MR526mjGmoVe1xqtafTsSNaCYMD6DBy8X5ZtUh+gz5FovFYEjfmtSvU4xFyw8k2bfbGxWYMWcXtyOTXvV842YMHbouSXz/N9qX540e3/DJwDpkzpSWz2fvYM++UJeV8b9KTf3yv/Gmsv5VSj1vJfVzesLiHujOtPKf7dlzhI0bd7F1y16iY2K5dTOS4D6T+HT4W/f1PuPHLqRLt+Z8NWcNjZtWJ1++HIwbu5BhI952UuRyL2Jj7XTvNop6AZWpXee5ZOuXLdlIx04NMQyD/Plzk++RXJw4cZ6nnvpzJMb4sfPo2q0lc+espknTmuTLl5OxY+cxfMQ/T7Yl7nPgwHF69hgFwLVrN9iyZRdWm5Vatf5sA2PHzqVbt9bMmb2Spk1rkS9fLsaOmcuIke+6K+w7Uh8lqYW/f3ZCwy4lvg4Lv4S/f/Y7brtq1RYGDnrjjuvGjplDt+6vMHv2Spo2q0O+fLkYM3o2I0f1dErc96Pic/k5e/46V67Gj4Zas+EoZUvn45tVhwCIizNZseYwQW2fTZaweLpkHl6qVZTg7lXIlDENcXEm0TEOZi/Yk7hNl84vMPHznwl4sRg7955j9dojTB4VSLu3F7uukP9RauqX/01qKqs3nLcpgaFvuw/EExIWd7kSUOTfde/Rku49WgKwY/tBvpgRct9fBHZsP0TOXFnJXyA3kZHRWCwGFouFqMiYf99ZnMY0TQa9N4VChfLRrl39O26TJ08OfvppP2XLFefSpWucOnmeRx/Nlbh+x/aDCXWbJ7FuDYtBlGa39mjr1k9NfN4veDzVqpVL8kFx+/YD5MqVjQIF8hIZFYPFYsFisRAZ5Xn1qj5KUotSpYpw+tR5zp4JI5d/dlaFbGXkqF7Jtjtx/CzXI25RpkyxZOv+eu5GJbZlgygPOXfPh0XwdKk8pE1rIyrKToXy+dl/MIz8j2bh9JlrANSqWpjjp64k27dFh/mJz7sFVeDW7ZgkyYoCj2Uhd66M/LzrDMWL5iQ6IgoTk7RpPeGj+L9LTf3yv0lNZfWG81ZSv5TRS4rcpwnjF1GiZEGq1yjLgf3H6dZlDDcibrN54x4mfbaYZSuHA/FfiqdNWcbI0V0AaNa8BsG9J2F3OBg4uP0/HUKcbPfu31i+fAtFiz5G40a9AejevRWhofG/FLRoWYc33mrCgH6TaBjYE9OEHj3bkDVrJiC+bqdOWczI0fG/djRrXou+vT/D4XAwcLDm/XWnnj1GsX3Hr1y7GkG1qh15p0tL7Pb467lbtnzxH/c1TZMpUxYxenT8rzrNm9emd++xOOwOBg8JcnrsD4v6KElpbDYrAwcF0aHjEOIccTRpUosiRR5j/Li5lCz5ODVqxn+hC1m1hXovV8b420+KpmkyZfICRo/pA0DzFnXp3WsUdruDIUPedHFp7uyXA2F8u+4IK756FbvD5ODhcOYv3secac3JmN4XDIPDRy4w8JN1ANSsWphST+Zm7OTv//W9e75dmVETtwKw4tvDTBnTgDfal7+nfV3Bm/plbyqrN5y3kvoZ5t2mOnZVAIaxxzTNu9wf6ohGX6RSMXER7g7BZXwtmdwdgkvZ4+5vYsGUzGbxc3cILhVnpqxJ4h6E3bzt7hBcytv6KW9ietFA1sJlVrg7BJc6tvtld4cgTmIYVneH4FIGT6TqiyZ2XgpJlR1xuRz1XFJvTh9hYRhGtjssvmGa5h+zEtV0dgwiIiIiIiIikrK44r40u4GLwBHgaMLzU4Zh7DYMo6xpmkkuBDQMo7NhGDsNw9g5bdoCF4QnIiIiIiIi8vBZUunDVVwxh8VaYJFpmmsADMOoAzQBZgKTgCRT/5umOQ2YFv9Kl4SIiIiIiIiIeCNXJCyeN02z0x8vTNP8zjCMkaZpBhmGkcYFxxchIuIWQwZO5+jRsxiGwQcfdebpMkXcHZY8JA5HHM2bBeOfKxuTpgS7Oxx5SEJDLxHcdxyXL18Dw6B589q89lqAu8NyGvVTkhr07zeOTZt2kj17ZlasnODucO5q2OC6VK9SmMtXbvNSsy8ACO5elZpVChEbG8fps9foM/hbbtyMJkvmtEwcEchTJXKzePmvDBm2Psl7vdG+PKFhNzgffoOBvapTrEhOuvVbyep1R5JslyG9L2sWt2ftxmOJ7/HV9BbkypGeqOj4OYLavrmIy1c9cw4db+qTvamskHLOW/FOrkhYhBqG0Rf+z959h0dRtX0c/55NISFASOhNICAKKEWagoB0pfcmTVFRqdKr2AsKSm8qKiqCvNIEhQceiqhItYAISlMw9BJI3828fyRG8tBUMluyv4/XXmZnzs7cNzM7O3vvmTP8eb+nTsAJkzqaTIob1i/Cqy/Np9a9FZk0eRDJSU6vvPWU/Hvz568iKqoIsZf8Z8BPfxAQ4GD4iF6UL1+K2EvxtGs3hJo1K1G6dDFPh2YLHackK2jTtgEPdmvOyBFveDqU61q8Yg/vL9zF68//NXDl5i2HeW3qJlwuixED6vDkwzV4dcomEhNdvDHjK8qUzkuZUnmvWFbte0rQf8QKQkOCGD7+cx7pUe2q63zqyVps23n0yuljVvLjTycyLzmb+NMx2Z9yBd9534p/csflJ12BosDStMctadMCgI5uWL/4uYsX49ix/Wfatr8PgKDgQHLlCvNsUJJpjh8/w6aNO2nXXuP3ZjX580dSvnwpAMJyhFKqVFFOnDjj4ajsoeOUZBXVqt1BeHgOT4dxQ9t2HuX8hYQM0zZvOYLLlXo18q4f/6BggdQ84hOS2f7dMRITr7xTUo6wYIKCAjh7Lp5j0TH8/MtpUlKuvKL5jrIFyJsnjC+/OZzpubiLPx2T/SlX8J33ra8yxsqSD3exvYeFZVmngf7XmP2r3esXOXb0JBGRORk7ejb79/1GuXIlGTG6O9mzh3g6NMkEr7z8LkOGdiM2Vr0rsrJjR0+yd+8hKlYs4+lQbKHjlIh36dDqTj5b8/MN29WqUZyvt/523TbGwOjB9zF4zEpq1Sh+xfwJz9yPK8Xii3X7mTZ3y7+O2Z2y+jH5cv6Uq4g3sq2HhTFmhTFm+bUedq1X5H+5XCns/ekwnTo35JNPXyI0ezbenutf92rPqjas30FkZDjly0d5OhSxUWxsPAMGvMrIUQ+TI0d2T4djCx2nRLzHk71r4HSlsGzV3hu2rVOzBBu/OnjdNt06VmbD5oMcP3npinlPjV7JAx3fo9PDC6hWuShtmpf713G7iz8ck//kT7mKeCs7e1i8nvb/tkBB4IO0510A779QT7KMAgUiKVAgkgoVSwPQqHF1fRHIInbt2seG9dv5ctMuEpOSiL0Uz4jhU3h1wgBPhyaZJDnZycABE2jRog6NG9/j6XBso+OUiHdo16I89euUolufRX+rfcU7CjHupbXXbXNXhUJUq1yUbh0rkT00iKCgAOLik5gw5UtOnEotYsTGJbP8871ULF+IJZ/9dNN52MVfjsngX7mKeDPbChaWZW0EMMZMtCyr6mWzVhhjttu1XpH/lTdfbgoWysOhQ39QsmRhvt2yh1Kli3g6LMkETw3uylODuwKwdese3n1nhYoVWYhlWYwdO52oUkXp9VArT4djKx2nRDyvTs0SPNarOl0e+ZiEhCvHq/hft0bl4cDhs1cds+JyT41Zlf53uxblubNcQSZM+ZKAAEOunCGcOx9PYKCD+nVK8dW3R246D7v40zHZn3IV+xlPB+Dj3HGXkDBjTJRlWQcBjDFRgEYSE7caNaYHI4fNIDnZSdFi+Xn+xT6eDklEbmDnzr0sX7aBMmWK06b1UwAMeqobdetW8XBk9tBxSrKCwYNfY9vW3Zw7F0PdOg/Rv38X2ndo7OmwrjD55WbUqFKMiNyhfPVFHybP+orHH6pBcHAA78/sAMB3P/7B2BdTe09sWvlo+gCbjeqVpueTi6lbqySbvjqUvswK5Qoyc1IrwnOF0KBOKQY+XpP72797zRiCgwJ5d3o7ggIDcAQYvvr2CB9/+oOted8Mfzom+1Ou4DvvW/FPxrLsHeHTGNMEmAv8eYFfCeAxy7LW3PjV+903/Ki4VVJKjKdDcJtgRy5Ph+BWzhT/Gfwy0BHq6RDcKsW68S+OWYXTivN0CG7lb8cpf2LhP6dSpSq79zKq92e2Z8i4zzl1Otat6/3Trzub3riR+CRjAjwdglsZbsvSnRC+O/NZljwQV8rT3C3bzR09LHIBdwAlgZZATeC0G9YrIiIiImKLHk8s9nQIIiJZnm13CbnMOMuyYoCcQH1gGjDTDesVERERERER8RhjsubDXdxRsHCl/b8ZMNeyrJVAsBvWKyIiIiIiIiI+yh0Fi2PGmNlAJ2CVMSabm9YrIiIiIiIiIj7KHYWDjsBqoIllWeeBSGCYG9YrIiIiIiIiIj7K9kE3LcuKAz697Hk0EG33ekVERERERETEd7njLiEiIiIiIiIifidL37PVDTSWhIiIiIiIiIhkGmNMMWPMemPMT8aYPcaYgWnTI40x/zHG/JL2/4jrLUcFCxERERERERHJTE5giGVZ5YC7gb7GmHLASGCdZVm3AuvSnl+TChYiIiIicQAGiQAAIABJREFUIiIikmksy4q2LGtn2t8Xgb1AEaAV8F5as/eA1tdbjsawEBEREREREbGBI4sOYmGMeQx47LJJcyzLmnONtiWAysC3QIG0G3EAHAcKXG89KliIiIiIiIiIyN+WVpy4aoHicsaYHMD/AYMsy4ox5q8KjmVZljHGut7rdUmIiIiIiIiIiGQqY0wQqcWKDy3L+jRt8gljTKG0+YWAk9dbhgoWIiIiIiIiIpJpTGpXireBvZZlTbps1nKgZ9rfPYFl11uOLgkRERERERERsUEWHcLi76gFdAd+NMZ8lzZtNPAKsMgY0xs4AnS83kJUsBARERERERGRTGNZ1mauXa9p8HeXo0tCRERERERERMTrqGAhIiIiIiIiIl5Hl4SIiIiIiIiI2MD48SAWmUE9LERERERERETE66hgISIiIiIiIiJeRwULEREREREREfE6KliIiIiIiIiIiNfRoJsiIiIiIiIiNtCYmzdHPSxERERERERExOt4dQ8LC5enQ3AbQ4CnQ3CrABPi6RDcxp/2YwCH8erDitwE40f35QoyYZ4OQSRTuFISPB2C2xzY1czTIbhV4fIfeDoEtzm6u4unQ3Arh3d/RRNxK/WwEBERERERERGvo/KdiIiIiIiIiA38p3+qPdTDQkRERERERES8jgoWIiIiIiIiIuJ1VLAQEREREREREa+jMSxEREREREREbODQIBY3RT0sRERERERERMTrqGAhIiIiIiIiIl5HBQsRERERERER8Toaw0JERERERETEBhrC4uaoh4WIiIiIiIiIeB0VLERERERERETE66hgISIiIiIiIiJeRwULEREREREREfE6GnRTRERERERExAbGWJ4Owaeph4WIiIiIiIiIeB0VLERERERERETE66hgISIiIiIiIiJeR2NYiIiIiIiIiNjAeDoAH6ceFiIiIiIiIiLidVSwEBERERERERGvo4KFiIiIiIiIiHgdjWEhIiIiIiIiYgOjQSxuinpYiIiIiIiIiIjXsa1gYYwZYIwpZtfyRURERERERCTrsrOHxfPAt8aYL40xTxpj8tm4rusaPWoqNe/pSYvmA64637IsXnhhLo0bPU7LFgPZs+cAAAcPHqNt28G0bDGQXbt+BsDpdPFQr6eJj090W/xybdHRp+nV42maNxtIi+YDmf/+Z1e0OXjwKF06jaLinZ145+1l6dPPnr1At65jaNliEGvXfps+ve+Tr3DyxFm3xP9P+dO+PGb0NGrV7EWLFgOvOn/rt7upVrUbbVoPpk3rwUyfvghI3a4Pdh1NixYD/2e7vuy129Xf+NN+DP6Xr2RN0dGn6dXzWVo0f4qWzQcz//1VV223dese2rYZRsvmg+nZfTwAZ8/G0O3BcbRqMYR1a7emt+3XdwInT3rncdkf3reP9ajJhmUDWL90ADNe60i24NQrxUcOaMTmlU+xaflAej94z1VfW6RQOB/P6cWm5QPZuHwgRQvnBmD6qx1Y92l/Rg1slN52UJ/7uL9+WfsT+pv87dxRxNfZWbA4CBQltXBRBfjJGPOFMaanMSanjeu9Qpu29Zn71tPXnL9p0w6OHI5m9ZqZPPf8kzz7zCwAFi5czZjRjzBn7jjeeWcpAAsWfE6LlnUJDc3mltjl+gIDAhg+ohefrZzMxx+/wkcffsGvv/6eoU14eE5Gj+3NQw+3zDB95Web6di5MQsXvcr891YCsP6/2yhbtiT5C0S6LYd/wp/25dZt6jFn7rjrtqlSpSxLlk5iydJJ9O3bEUjdrp06N2HRogm8/17qSYi3b1d/40/7MfhfvpI1BQYEMHx4d1Z89gYLFr7Igo9W8+uvRzO0iYmJ5fnn3mLa9BEs/2wSk94cDMCqlZvp1KkRHy96Kb3QsX79dsqWLUH+/N55XM7q79uC+XPR+8F7uL/jDOq1nkKAw0GrpnfSqfVdFC4YTu3mb1Kn5WSWfv7DVV8/5aX2zJj3JXVaTuaBzjM5czaWsmUKEJ/gpEHbqVS8oyg5c2Qjf96cVK5QjC/+u9fNGV6bv507iuc5sujDXexcl2VZVoplWWssy+oNFAZmAPeTWsxwm2rVyhMenuOa89et20qr1vdhjKFSpduIiYnl5MmzBAUGEJ+QSHx8IkGBgcTEXGL9+m20bl3PjdHL9eTLH0G58lEAhOUIJapU0Ssq3HnyhHPnnaUJDMw4xmxQUAAJ8YkkJSXjCHDgdLp4//2V9H6ktdvi/6f8aV+uVq08ucP/eW0z8LLtGpC+XT+j9yNtbIhS/g1/2o/B//KVrCnD521YKFGlilzxebvys800bFiDwoXzAqmfvwCBgYHEJyRl+Lyd//4qHu7dyr1J/AP+8L4NCHAQEhJEQICD0JAgTpy8SM/ONZg0679YlgXAmbOxV7yuTKl8BAY62PRNaq+SuLgk4hOScTpTCA0JxBhDUKADV4rF8P4NeH3aOrfmdSP+du4o4uvsvEtIhvFQLctKBpYDy40x2W1c7z924sRZChXMm/68YME8nDhxlq4PNmXEiDdJSnLy3LNPMGPGIvr0aY/DobFKvdGxoyfZu/cQFSre+rfaN2tem2FD3+STRWsZPLQbCz76gpYt63jVLyD/lL/ty999t4/WrZ4if/5Ihg3vya233kLz5rUZOvQNFi36D0OGdk/brt71y5Zcn7/tx/6Wr/i+Y8f+/LwtnWH64cPROJ1OevV4htjYeLp1b0qr1nVp1vxehg+dzOJFaxk85EE+XrCaFvq89ajjJ2OY9e5mtq8dRkKCk41f/8LGr39lxmudaHV/BR5oUI4z52IZ+9JnHPrtTIbXRhXPy4WYBN5+syvFikbw5TcHePGN1fxy8BRnzsWyZnFfFi/fRclb8mCM4ce9f3goyxvTuaOI97OzYNHpWjMsy4r7829jTIRlWedsjONfK1w4H/PnvwjAkSPRnDh+hlKlijF82BskJzsZMLArJUsW8XCUAhAbG8/AAa8xatRD5Mjx9+phOXOGMWv2GAAuXLjEW3OXMGXqcJ4eN5OYC5fo9VBLKlW+zc6w3SYr7svlykex7r+zCQsLZePGHfTr9yqrV08nZ84wZs8eC6Ru17lzP2Xq1BGMGzcjbbu2onIW2a7+Jivux9fjb/mKb4iNTWDQgImMHNnris9bl8vFT3sO8fa8cSQmJtG181gqVryVEiULM3P2KODPz9ulTJ46jKfHzSImJpZevVpQqXIZT6ST6XzlfRueK4Qm9ctSo/HrXLiYwNxJXWjXvCLZggNISEzm/k4zaNqwHG+80JbWPeZmeG1AoIMaVUrQqP00jkVfYPbETnRqfRcLPt3B06/8NbbJe9O7M/yZpQx87D7K3VaQTd/8yoeLt7s71WvSuaOIb7Ct3GtZ1v6/2dTj/cQKFIgk+vjp9OfHj5+hwP9ch/bmGx8wcNCDzJ//Ge07NGLosJ5Mn7bQ3aHKVSQnOxk04DWat6hNo8Z3/6tlzJrxCX36tGPVys3cddftvPRKf5/cvv60L+fIkZ2wsFAA6tatgjPZyblzMRnazJzxCY/3ac/KlZupcldZXn5lgE/m6m/8aT8G/8tXfFdyspNBAyfSrEVtGjWuccX8AgXzUOveimTPHkJERC6qVi3Lvn1HMrSZNfP/eOzxtqmft1Vu56WX+zJ9+ifuSiHT+Pr7tvbdpfnt6DnOnIvD6Uxh1do9VK1cnOjjMaxa+xMAq9b+RNkyBa94bfTxGPb8HM1vR8/hcqXwxbq93FmucIY2TeqV5Yc9xwjLHkzxYpH0GfIxzRvfQWhIkFvyuxGdO4r4Dm/on2Zu3MRe9etXZ9nSDViWxXff7SNnzrAMg0Bt3bqb/PkjKVGiMAnxiTgcBofDkJDgXaM9+yPLshg3dgZRpYrS66GWN37BVRw+/AfHT5yheo07SEhI3b7GGBISkzI5Wvv507586tS59Gtsf/jhFyzLInfuv8a8OHz4D05ctl2ND29Xf+NP+zH4X77imyzL4umxs4iKKkKvXs2v2qZ+/ars3PkzTqeL+PhEfvjhV6Ki/upNcORwdOpxuXp5EhKScBgHxhgSE3zvuOzr79tj0eepUrFYegHh3rtL8cuBk3z+35+oVT11fId7qpXk4JHTV7z2u91HyZUrhDwRqb0SatWIYv+Bk+nzAwMdPNqjJjPe+ZKQkKD0z2qHwxAUFGB3ajekc0dxN2Oy5sNt/35/HkQ8xRiz07Ksu642z2JvpgQ3ePBEtm3dzblzMeTJk5v+/TvjdLoA6NzlfizL4vnn5vDllzsJCc3GSy8N4M47U6/LtCyL3g8/w6Q3hpI7d04OHPidYUPfwOl08cwzj3NXlcy5TZPB8wdwd3JZmXNA37FjL90fHEuZMrdg0q4PHfRUV6KjUz9gO3duwqlT5+jYfjiXLsXjcBiyZw9hxcrJ6d3/nhr0OgMHdaVEicKcOXOB/n1f5eKlWPr370zjJle/ndc/4TCZt219YV+2rJRMWc6QwZPYum03589dJE+ecPpdnmvnJnz4wSoWfLyawAAH2UKCGTniISrfdXv66zNu1/P06/sqFy/FMSCTtiuAw3jHL0XuYuHKlOX4wn6cmXwhX3/7DPInzpT4TFnOjh0/06Pb02mft6lnqoMGdUn/vO3UuTEA77y9nCVL1uMwDtq1r0+Pns3SlzH4qUkMHNiF4iUKcebMBQb0e42LF+PoN6Ajjf/lr9yXC3AE3/Qy/uQL79vC5T+4qdcP7duAVvffidOVwu69fzDk6SWEhAQx/dWOFCkUTmxcEiOeW8ZP+45TsXwRunesztDxSwCoc08pxg9rijHww09/MOyZpSQnp/77PNq9JhcuxrNo6S4AZrzWkdtLF2Ddl/t5cdLqfxXr0d1dbirXy/nCuWOAybx92TeU8fgP2HY6cmmFZ79w26R4jhZu2W5+UbDwBf52sphZBQtfkJkFC1+QWQULX6CChWQV/vYZ5E8yq2DhCzKzYOELbrZg4Usys2DhC1SwyFpUsLg5uiRERERERERERLyOnXcJAcAYE3mVyRfTbnMK0MDuGERERERERETcTb/O3xx39LDYCZwC9gO/pP192Biz0xhTxbKss5c3NsY8ZozZbozZPmfOIjeEJyIiIiIiIiLexvYeFsB/gMWWZa0GMMY0BtoB84AZQIb7YlmWNQeYA+4dwyI6+hQjhk/mzJnzGGPo2LExPXq2cNfqxQ1cLhcd2o+gQP5IZs4e7elwbONv+3JiYhLdu40lKSkZpyuFJo3vof+Azp4OSzKBP+3L/pSrZH2NGvQlLCwER4CDwIAAFi1+xdMh2cKX3reTnm9Lo7q3cfpsLPVaT0mf/nDXu3moy924UlJYu2kfL0xcTWCgg4nPteHOsoUJDHDwyfJdTH1rU/prXn26FYtX7KJAvlwM7VufW6Py0bTzLL7fcwyAoKAAJoxvRcXyRUixLMa9vJJvth3KEM+707pRvGhkhli8kb+cO4p4M3cULO62LOvRP59YlrXGGPO6ZVl9jDHZ3LD+vyUgIIARIx+ifPlSXLoUT7t2Q6hZqxKlSxfzdGiSSea/v5JSUUW4dClrD0Dmb/tycHAQ8959lrCwUJKTnXR7cAy161SmUqXbPB2a3CR/2pf9KVfxD/PeG09ERC5Ph2ErX3rfLlq6k3kfbWHKy+3Tp9WsXpIm9cvSoO1UkpJd5IkMA6BFkzsIDgqkfpuphIYEsXH5QJas+oGjf5wH4K6KxRj1wnJKlchL74EfMWF8qwzrerB9VQDqt5lKnsgwPprVk/s7zUy/vWnThuWIjfONwdf95dxRxJu545KQaGPMCGNM8bTHcOCEMSYA8JrbCeTPH0n58qUAyJEjlFJRRTlx4oyHo5LMcvz4GTZu3Em7Dg09HYrt/G1fNsYQFhYKgNPpItnpxLjz5tBiG3/al/0pV5Gswpfet1t2HObchbgM03p2qsG0tzaRlHY70jNnYwGwLMiePZiAAAch2QJJSnZxKTYRgFuj8nHw8GlSUix+OXiKA4dPX7GuMqXy89W3B9OXeeFiAhXvKAKkLrdPz1pMnr3etlwziz+dO4q9jMmaD3dxR8GiK1AUWJr2uCVtWgDQ0Q3r/8eOHj3B3r0HqVixjKdDkUzyykvvMHRodxx+9kXWX/Zll8tFm9aDubfWQ9SsWTHL5+uP/GVfBv/KVbImY+DR3i/Sod0IFi1a6+lw3MIX37dRJfJSo0oJVi54nE/ffSS9qPDZmt3ExSXx/YaRbF87nFnvbub8hdQeBvVrl2H95l+uu9yf9h2ncb3bCQhwUKxIBBXKFaZIwXAARvRvyKx3vyIuPvm6y/AG/nruKOJtbC9YWJZ12rKs/pZlVU579LMs65RlWUmWZf1q9/r/qdjYeAYMeJVRo3uTI0d2T4cjmWDD+u1E5gmn/B2lPB2KW/nTvhwQEMCSpZNYv2EuP/7wK/v3H/F0SJKJ/Glf9qdcJeua/+HzLP70VWbNGc2Cj1azfdtPng7JVr76vg0McJA7PJRmXWbx3MQvmDMxdfynyncWJSUlhUr1XqF6k9fp07MWtxSNAOC+WreyfvP+6y53wac7iD4RwxeLnuS5kc3Y/t1vuFwplL+9EMWLRfL5Ou/fH/z13FHEG9k2hoUxZgVwzUEzLctqade6/63kZCcDBrxKixZ1adz4Hk+HI5lk586fWf/fbWzauJPEpGRiL8UxfNhkJrw20NOh2cZf9+VcucKoXuMONn+5izJlins6HMkE/rQv+1OukrUVKJB6R/s8ecJp2LAaP/74K1WrlfNwVPbw5fdt9IkLrFq7B4DvfjxKSopFnojstGlWkfWbf8HpTOHM2Vi27fqNiuWLcOr0JXLlDOHEqYvXXa7LlcL4V1elP1/+wWMcPHKau6uWpGL5ImxdM5SAAAd584Txf/N60+6ht23N89/wx3NHEW9l56Cbr6f9vy1QEPgg7XkX4ISN6/1XLMti7JhplIoqykMPtbrxC8RnDB7SjcFDugGw9dvdzHtneZb+wPG3ffns2QsEBgaSK1cYCQmJfPP19/R+pI2nw5JM4E/7sj/lKllbXFwClmURFhZKXFwCX3/1A48/2f7GL/RBvv6+/WLdXmpVj+LrrYeIKp6HoKAAzpyL41j0eWrViGLxiu8IDQ2iSsVizJ3/FTWrR/HV1oM3XG5oSBAYiI9Pps49pXC5Uth/4BT7D5zi/YVbAShaODfzZ/TwymIF+N+5o9hLFxXdHNsKFpZlbQQwxky0LKvqZbNWGGO227Xef2vnjr0sW7aBMmWK07rVIACeGtyNunWr3uCVIt7F3/blU6fOMWrkVFyuFFKsFO6/vxb16mXNXP2NP+3L/pSrZG1nzlxgQP/U36xcThfNmt9L7dqVPByVPXzpfTvjtY7UrBZFZO7s7Fg3nNenr2PBkh288Xxb1i8dQHKyi4Fj/g+AeQu+5c0X2rJh2QCMMXy8ZAd795+gW4fqfLZmd/oyH2hQjhdGNydPZBjzZ/Rgz75oujz2Lnkiw1gwpxdWikX0yRj6j1zsqbRFJAswf95iyLYVGLMXaGZZ1sG051HASsuyyt7otRZ77Q3OixgCPB2CW7ks37idVWZwGP/atpblNTf/sZ3DBHk6BLeycHk6BLGJv30G+RNniv/cjjHAEezpENyqcPkPbtwoE635pC9Nu8zE6XT/5/zR3V3cvk5PCjD+tS9DmSzdCeFo7Ios+Z22aFgLt2w3Oy8J+dMgYIMx5s8+ZCWAx9ywXhERERERyQSNO0z3dAgi4ofcUbDIBdwBlARaAjWBK2/aLCIiIiIiIiKSxh0Fi3GWZX1ijMkJ1Cd1MM6ZQA03rFtERERERETEIxxZ+oIX+zncsI4/L3puBsy1LGsl4G8XZomIiIiIiIjIP+COgsUxY8xsoBOwyhiTzU3rFREREREREREf5Y7CQUdgNdDEsqzzQCQwzA3rFREREREREREfZfsYFpZlxQGfXvY8Goi2e70iIiIiIiIinqQhLG6OLs0QEREREREREa+jgoWIiIiIiIiIeB0VLERERERERETE69g+hoWIiIiIiIiIPzLG8nQIPk09LERERERERETE66hgISIiIiIiIiJeRwULEREREREREfE6GsNCRERERERExAbG0wH4OPWwEBERERERERGvo4KFiIiIiIiIiHgdFSxERERERERExOuoYCEiIiIiIiIiXkeDboqIiIiIiIjYwGjUzZuiHhYiIiIiIiIi4nVUsBARERERERERr6OChYiIiIiIiIh4HY1hISIiIiIiImIDDWFxc9TDQkRERERERES8jlf3sIh3nvV0CG6TlBLj6RDcKldQcU+H4DaWZXk6BDdTHTmrMgR4OgSxiTMl3tMhuE2C65ynQxCb5HAU9nQIbhW9p6enQ3CbktOjPR2CW+17IoenQ3CrYP2ELteh3UNEREREREREvI5X97AQERERERER8VXqIXBz9O8nIiIiIiIiIl5HBQsRERERERER8ToqWIiIiIiIiIiI19EYFiIiIiIiIiI2MLqB3k1RDwsRERERERER8ToqWIiIiIiIiIiI11HBQkRERERERES8jsawEBEREREREbGFBrG4GephISIiIiIiIiJeRwULEREREREREfE6KliIiIiIiIiIiNdRwUJEREREREREvI4G3RQRERERERGxgdGgmzdFPSxERERERERExOuoYCEiIiIiIiIiXkcFCxERERERERHxOhrDQkRERERERMQGxqiPwM3Qv56IiIiIiIiIeB0VLERERERERETE66hgISIiIiIiIiJeR2NYiIiIiIiIiNjCeDoAn6YeFiIiIiIiIiLidWzrYWGMCQY6A39YlrXWGNMVqAnsBeZYlpVs17pFRERERERExLfZeUnIvLTlZzfG9ARyAJ8CDYDqQE8b1y0iIiIiIiIiPszOgsWdlmVVMMYEAseAwpZluYwxHwDf27jeq7oYE8ezT8/jwK/HMMYw/vmHqFip9BXt9vx4iJ4PvsjLrz1OoyZVOXwomtHD5+B0uhgzvgcVK5XG6XTRt88k3pw2gNDQbO5O5aqeH/cRX236iYjIHCxYMhKACxdiGTv0Pf744yyFC0fy4uu9yBWePcProv84y4hB75CSkoLTmULHrrVp27EWSUlOhg14i5MnztOu072073wvAC89s5C2HWtye7libs/xasaMnsqGDduJzBPOihVTrpi/bt23TJm8AIfDEBAQwKjRD1OlSjkOHTzG0KGTcDpdPPPs41SufDtOp4tHH32OGTNGe812/V/+lK8/5SqSVURHn2bUyOmcOXMeg6FDx4Z079H0inZbt+7hlZffxZnsIiIiJ+/Nf5azZ2MY0P81LsbEMWBgJxo0rA5Av74TeHr8I+TPH+nudG7o8KHjjBo6N/35saOnebxfC7p2b5g+7f13VvP5yq0AuFwpHDoYzdovJ5LiSmHIwJlcuhjPE/1bUa9BJQAG95/BqHFdyZc/t3uTuQF/ylWypi+71+BSspMUC5wpFq0+2cnUxmWJikg9N84VHEhMkpNmC3dkeF1U7lCmNimX/rxYrhDe+PYw8344xoh7SnLfLZH8dPoSQ9btA6B1mfxEhAQx74dj7kvuGhITk+jV/TmSkpy4nC4aNalB3/7tM7SJ/uM0Y0bN4uLFWFyuFAYN7kydupXZtXMfzz/7DkFBgUx4vR/FSxQiJiaWoU9NYdbcETgcGlng7zIaw+Km2FmwcKRdFhIGZAfCgbNANiDIxvVe1YSXP6LmvXfy+pt9SU5ykpCQdEUblyuFyZM+4e6a5dOnLV60kWEju1C4SF4mvLyAiZNL88nC9TRrfo9XffFp3qoGHbrU5tkxH6ZPe//tdVStUYaejzTkvbfW8v7ba+k3uGWG1+XNl4u3PhhEcHAgcXGJdG3zCrXvu4O9e36nYuUoej3akEe7T6Z953vZv+8YKSkpXlOsAGjdpj5dH2zKyJGTrzr/7rsrUL9+dYwx7Nt3mKcGvc6qz6excOFqRo/pTZEi+XnpxbepPPV2Pl7wBS1b1PWq7fq//Clff8pVJKsIDAhg+PDulCsfRWxsPB3ajeSemhUoXbpoepuYmFief+4tZs8ZQ+HCeTlz5gIAq1ZuplOnRjRsVIMn+rxMg4bVWb9+O2XLlvDKYgVAiZIFWfB/44DUc4gH6o+gXoPKGdr0eLgJPR5uAsCmDd/z4fvrCA8PY8EH/6V9xzrUa3gXA5+YSr0Gldi04Xtuu72YV36B96dcJevquvR7ziU405/3X7M3/e8xtaKISXRd8ZqD5+PTixgOA1t63cOaQ6fJGRzAHfly8sDCHbxSrwy3RYZx+EI87csWpNeKH+1P5m8IDg7i7XljyR4WQnKyk57dnuXe2hWpWOnW9DazZy2hyf016NSlEQd+PcqTfSZQZ11l3pu3ihmzh/PHsVMsWriOYSO6MWfWUh7t00rFCnErO/e2t4Gfge+AMcAnxpi5wDbgYxvXe4WLF+PYuWM/bdrVBiAoOJCcubJf0e7jD9fSoFEVIiNzpU8LCgwgISGJhIQkAoMCuBgTx6YN39O8VU23xf93VK5a6oreE5vW/0izVtUAaNaqGhvXX3nwDAoKJDg4tW6VnOQkJcUCIDDQQUJCEk5nClZa2znTVtGn35W/lHlStWrlyR2e85rzw8JCMSa1qhkXl0DanwQGBZIQn0R8fCKBQYHExMSyfv02WrW+zw1R/3v+lK8/5SqSVeTLH0G58lFA6ns0qlQRTp44m6HNys8207BhDQoXzgtAnjzhAAQGBhKfkERSUjKOAAdOp4v576/i4d6t3JvEv7R1y88ULZaPQoXzXLPNF6u20aRp6udyYNr5RXJSMo4Ag9Pp4qP569K/8Hszf8pV/EfTUvlY8cvJ67apVTSCIxfiOXYxkRQLAh2pJx8hgQ6SU1J4rHJR3vvhGM4U67rLcRdjDNnDQgBwOl04k13p506Xt7l0KR5I/c6UL38EkPa+jU/7DhQYwO+/neC3n1rbAAAgAElEQVR49BmqVS+HiDvZ1sPCsqw3jDEL0/7+wxjzPtAQmGtZ1tY/2xljIizLOmdXHAB/HD1NREROxo95h/37fqds+eIMH9mV0Ox//dp68sQ5/rtuJ3PnDWfP2Hnp0zt2qc+40W+RlORk7PgezJm1nN6PNvOJyuLZMxfJmy/1RDBP3lycPXPxqu1OHD/H4Cfn8Pvvp+k/uCX58ocTEZmDz1dsp/eDb9CtV302rd/NbWWLki9/uDtTyBT/+c8W3pj0AWfPXmDmrDEAdO36ACNHTCYpKZlnn3uCmTMW0adPe5/YrjfiT/n6U64ivubYsZPs3XuIChUzXn55+HA0TqeTXj2eITY2nm7dm9KqdV2aNb+X4UMns3jRWgYPeZCPF6ymRcs6PtMzas3nf31Bv5r4+CS+2byHEWO6AHB/s+qMGf4Wn37yJQMGt+WTjzfStMXdhIYGuyvkf82fcpWsw8Li/ZYVsCxYsCeaBT9Fp8+rXiic0/HJHL4Qf91lNL/1r6JGbLKLDUfOsrJTFb4+eo6LSS4qFsjF1O2/2ZrHP+VypdCp/Rh+++04nbs0vuKY/GTfdjz2yCt89OEa4uMTmPvOaAAeeawlY0bOJFtIMC+9+gQTJ3xI/4EdPJGC+Dk7LwnBsqw/Lvv7PLD4Ks3WAXfZGYfT5eLnvUcYMaYrd1YoxYSXP+Kdt1bSd0Db9DavvbKAgYM7XPGlplDhPLz17ggAfjtygpMnzlEyqhBjR84lOdnJk/3bULxEQTvDzxTGmGteP1WgYAQffjqCUycvMHzg29RvVIk8eXPy/IQeADiTXQx4fBavTenNmxOWcPz4eZq2qEadene4M4V/rVGju2nU6G62bdvDlCkLmDfvWQoXzsf7818A4MiRaI6fOENUqaIMH/4myclOBgzoQsmSRTwc+b/jT/n6U64iviQ2NoFBAyYycmQvcuTI2PvP5XLx055DvD1vHImJSXTtPJaKFW+lRMnCzJw9CoALFy7x1tylTJ46jKfHzSImJpZevVpQqXIZT6RzQ8nJTjZu+J5+g9pcs82XG76nYuVShIeHAZAzZyhTZvYHIOZCLO++tZrXpzzO8+PnczEmjm49G1KhUim3xP9P+FOukrV0+PQ7TsQmkSc0iPktK3DgXBxbo1MvSWtRJv8Ne1cEOQwNS+TltW8OpU+bvet3Zu/6HYBX6pXhjW8P06lsQWrfEsHPp2OZtsPzxYuAAAeLl7xMTEwsg/q/wS/7f+fWMn9d3r1q1de0blOHng8147td+xk9YiZLlr/K7WVL8OHC5wDYvm0vefNFYFkw9KkpBAYFMHR4N/Lm9b0fMsX3eMNPjraPQlKgQCT5C0RwZ4XUD8OGjavy896MB5Cf9hxm5NBZNG00jLVrtvPyC/NZv25nhjbTp3zKkwPasuDDtbRuV4eBQzowe8Zyu8P/1yLz5OT0qdQD8elTF4jIk+O67fPlDyeqdEG+23kgw/TFCzfTtEVVdn9/hBw5Q3nxtZ589N562+K2S7Vq5Tn6+wnOnYvJMP3NNz9k4MCufDD/M9q3b8jQoT2YMX2Rh6LMPP6Urz/lKuLtkpOdDBo4kWYtatOocY0r5hcomIda91Yke/YQIiJyUbVqWfbtO5KhzayZ/8djj7dl1crN3FXldl56uS/Tp3/irhT+sa++3M3tZW8hT95c12yz+vPtNGla/arz3pq9kocfe4AvVm2j0l2lefbFXsye8Zld4d4Uf8pVspYTsanj152JT2b1wdNULJB62WmAgfuj8vLZDQoW9xWPZM+pi5yOT75iXrm8qefYB8/H0bR0Pvqt3sst4aGUCA/N5Cz+vVy5wqhWvRxfbc5474MlizfQ5P67AahUuQyJiUmcO/dXr2zLspgzaymPP9GGmTP+j8FDu9CufX0++uALt8bv20wWfbiHNxQsbL/IK2++cAoWjOTwodSuX1u3/ERUqcIZ2qxcM4FV/3mNVf95jYaNqzJqbHfqNfir48f2bfvIly83xYsXICE+CYfD4DAOEhIS7Q7/X6t93x2sXLYNgJXLtlGn3p1XtDlx/Hz6AKQxF+L4ftchipfInz4/5kIcX23cQ9OW1UhISErtqWEgMfHKg7U3OnIkGstK3cX27DlAUlIyuXP/NS7C1q27yZ8/khIlChOfkITD4cDhcBDvxdv1evwpX3/KVcRXWJbF02NnERVVhF69ml+1Tf36Vdm582ecThfx8Yn88MOvREX91evpyOFoTpw4Q/Xq5UlISMJhHBhjSLzKYNneYvWqbdx/nUskLl6MZ+f2/dxXr+IV8347coITJ85TtfptqecXxoAxJCZ6Z77+lKtkHaGBDsKCAtL/rl0sgn1nYwGoVSyCA+fiOB57/f2wxa35WX6NosbgGiWY9O1hAh0mdb8GUiyL0EDPftU6ezaGmJjUPBMSktjyzY+ULJnxO1DBwnnZsmU3AAcPHCMpMTnDeH7Ll31J7TqVCM+dI+07kAOHwxAfr/etuIetl4R4kxGjH2T0iDk4k10UKZqPZ194mE8WpvYS6NCp3nVfa1kWb81ewauvPw5A2w51GTNiDi5XCqPHdbc99r9j7PD32LntAOfPX6J5g/E81vcBevZuyOih77J8yRYKFYrkxYk9Adi75zc+XfQ1Y57tzOGDJ5jy+lIwBiyLB3vWo3SZvw5kb89aTa/HGuFwOLi71u0s/ngzXdtOoG0H7xh0dMjgiWzdtofz52K4r+4j9OvfGaczdfTnzp3vZ82ab1i2bANBgQFkyxbMpDeGpA82ZFkWs2YtZtKkIQB07NiIYcPexOV0Mf6ZPh7L6Xr8KV9/ylUkq9i5cx/Ll2+iTJlbaNtmGACDBnUhOvo0AJ06N6ZUqaLce28l2rQeisM4aNe+PreWuSV9GZMnL2DgwNSxD5o2q8WAfq/x1tyl9BvQ0f0J/Q3xcYl8+81eRo/vlj5t8cKNALTvVBeA9et2cXfNchnGzvrT9CnL6DsgdWDR+5tWY8iAmbz79hc83q/lFW09zZ9ylawlb/ZgZj+QehfAAIdh+f6TbPotdQi9FqWvLETkzx7MK/XL8PBnqV/kQwMd3FssgjEb9l+x7EYl8/DjyYucjEv9Ar/39CU+71yFn8/EsvdMrJ1p3dCpU+cZO2omLlcKVopF4/vvpm69u5g25RPK3xFFvfpVGDb8QZ55+i3mv/c5xhheePnx9POp+PhEli3ZxOy3RgLQo1dTnugzgaCgAF59vZ8nUxM/Yv78hdJjARizy7KsylebF+f8yjuG2HWDpJSYGzfKQnIFFfd0CCI3zWH8puYrWZwz5foDzWUlCS5bx/kWD8oRVPjGjcQnlZwefeNGWci+J65/GXdWE+yo4r7rCzzgQtLqLPmdNjy4iVu2m+1n28aYq908/aJlWX9eU9DA7hhERERERERE3M0YbxiFwXe5419vJ3AK2A/8kvb3YWPMTmNMFcuyMtyg3RjzmDFmuzFm+ztzl7khPBERERERERHxNu7oz/wfYLFlWasBjDGNgXbAPGAGkGEIccuy5gBzwP2XhDRtNIywsBAcDgcBgQ4+WjTenat3uwXvb2DZp1swBkrdWohxz3clW7YgT4dlizGjp7Jhw3Yi84SzYsUUT4djK3/KFfwvX5GsIDr6NKNGTufMmfMYDB06NqR7j6aeDss2F2PieH78fH799RgGw/jne2Tp23j6W77iWxwGlne4i+OxSTyycjev1b+NGkXCuZjkAmDoup/Zezp17IlAh2FJ+8oMXLOXqU3KpS+jWK4Q3vj2MPN+OEbZvGG8WLcM2QIdOFMsnt74C9+fvHjVdXurxMQkenV/jqQkJy6ni0ZNatC3f3tPhyUCuKdgcbdlWY/++cSyrDXGmNcty+pjjLlyRCYPmzNvOBEROW/c0MedPHGehR9t4uOlIwkJCWb0kHf5z+c7ad76ylvQZQWt29Sn64NNGTlysqdDsZ0/5Qr+l69IVhAYEMDw4d0pVz6K2Nh4OrQbyT01K1C6dFFPh2aL115ZyD21yjPhjT4kJztJyOKj6/tbvuJbHqpQlF/PxZEj+K+vQS9/fZDPD5y+om3VQuHsiI7h4Pl4mi3cAaQWPLb0uoc1h1Lbj7onisnbjrDxt7PcVzySkTWj6LL0+yuW5c2Cg4N4e95YsoeFkJzspGe3Z7m3dkUqVrrV06GJuOWSkGhjzAhjTPG0x3DghDEmAEhxw/rlGlzOFBITk3E6XSQkJJE3f7inQ7JNtWrlyR2e9QtR4F+5gv/lK5IV5MsfQbnyUQCEhYUSVaoIJ0+cvcGrfNPFi/Hs2vELrdvVAiAoKJCcubJ7OCr7+Fu+4lsKhgVTr0QkC386/rfa170lgg2/ZTw21SoawZEL8Ry7mHqbdAvIEZx6y9ScwQGciPW926cbY8geFgKA0+nCmexKv1OIZAaTRR/u4Y4eFl2B8cDStOdfpU0LALzqHmXGGJ58dCLGGNp1qEu7jvd5OiTb5C+Qmwd71aNVo2fJFhJEjXtu5+6at3s6LBER8TPHjp1k795DVKhY2tOh2OKPY6eJiMjJM2Pf45d9R7m93C0MG9npqrf9zAr8LV/xLU/fW5pXvj5IWFBAhulDa5RkQNXifHX0PBO+OUhSSupV6fcUyc3kbUcytG1+az5WXHYb1Oc2H+C9FncyumYUDmNo/+ku+xOxgcuVQqf2Y/jtt+N07tI4yx6TxffY3sPCsqzTlmX1tyyrctqjn2VZpyzLSrIs61e71/9PzJs/igWLn2HarKdYuOC/7Ni+z9Mh2SbmQhyb1u9myRdPs3Ldc8THJ/L5iu2eDktERPxIbGwCgwZMZOTIXuTIkTV/hXc5Xfy89zfad6rLR4vHEhqajXlvf+HpsGzjb/mK76hfPJLT8UnsPnUpw/QJWw7R4KNttPpkJ7lDAulz1y0AFAgL5nyCkwTnXx3CgxyGhiXysurXU+nTut1RiBc2H6DW+9/ywlcHeKXebe5JKJMFBDhYvORl1q6fxu4fD/DL/t89HZIIYGPBwhizwhiz/FoPu9Z7M/IXiAAgMk8u6je8iz0/HvJwRPbZtmU/hYtEEhGZg8CgAOo1rMCP32fdfEVExLskJzsZNHAizVrUplHjrDl+EkD+ghHkLxDBnRVKAtCw8V38/NNvHo7KPv6Wr/iOKoXCaVgyL192r8HUJuWoWSQ3bzS8nVNxqWOsJKVYfLL3OBULpF5mWveWSDb9nvFykPuKR7Ln1EVOxyenT2t7W0G+OJg6nsXKX0+lv95X5coVRrXq5fhqs2+NwyFZl509LF4HJgKHgHhgbtrjEnDAxvX+K/FxicTGxqf//c3XeyhVuoiHo7JPgUK52f3DERLik7Asi23f/kKJkgU8HZaIiPgBy7J4euwsoqKK0KtXc0+HY6u8ecMpUDCCw4dSr5nfuuVnokoV8nBU9vG3fMV3vLblEDXf20Lt+d/Sf/VPfH3sPE+t/Zl82YPT2zSOysv+M6l3CKl7SyQbjmQsWLS4NT/LL7scBOBkbCI1CqeOA1ezaG4On4+3OZPMd/ZsDDExqXknJCSx5ZsfKVmysIejyjpMFv3PXWwbw8KyrI0AxpiJlmVVvWzWCmOM1117cObMBQYPmAakXsP1QLMa1Kp9p4ejss8dFUpQv1FFenR8nYBAB2VuL0rrDjU9HZZthgyeyNZtezh/Lob76j5Cv/6dad++oafDsoU/5Qr+l69IVrBz5z6WL99EmTK30LbNMAAGDepCnbp3eTgyewwf3ZmxI94mOdlFkWJ5eeb5np4OyVb+lq/4tjcb3U5kaBAGw97TlxizcT8OA8XDQzh4WfEhNNDBvcUiGLNhf4bXj9qwn6fvLU2gw5DoSmH0/8z3BadOnWfsqJm4XClYKRaN77+buvWy5vFYfI+xLMveFRizF2hmWdbBtOdRwErLssre6LVxzq/sDc6LJKXEeDoEt8oVVNzTIYjcNIdxx7jFIvZzpvjeL4L/VoLrnKdDEJvkCNIvwllVyenRbl1f1UK5aF2mAGM3/uLW9f5p3xM5PLJeTwl2VMnStyS5mLwuS36nzRnUwC3bzR1n24OADcaYg2nPSwCPuWG9IiIiIiIi/8j26Bi2R/vXj4ki3sodBYtcwB1ASaAlUBM47Yb1ioiIiIiIiIiPsv22psA4y7JigJxAfWAaMNMN6xURERERERHxGE8Pjunrg266o2DhSvt/M2CuZVkrgeDrtBcRERERERERP+eOgsUxY8xsoBOwyhiTzU3rFREREREREREf5Y7CQUdgNdDEsqzzQCQwzA3rFREREREREREfZfugm5ZlxQGfXvY8GnDvvYlERERERERE3E4XF9wM/euJiIiIiIiIiNdRwUJEREREREREvI4KFiIiIiIiIiLidWwfw0JERERERETEHxljPB2CT1MPCxERERERERHxOipYiIiIiIiIiIjXUcFCRERERERERLyOxrAQERERERERsYXGsLgZ6mEhIiIiIiIiIl5HBQsRERERERER8ToqWIiIiIiIiIiI11HBQkRERERERES8jgbdFBEREREREbGB0aCbN0U9LERERERERETE66hgISIiIiIiIiJeRwULEREREREREfE6GsNCRERERERExBbqI3Az9K8nIiIiIiIiIl5HBQsRERERERER8TpefUlIkCO7p0Nwm5CA3J4Owa2SUmI8HYLbhAREejoEEfmHUqxkT4fgVg7j1acDmSpHUGFPh+BW/rQvJ6fEejoEtwowwZ4OwW0OPJnX0yG41W0PfOvpENzqly+qeDoE8WL+c4YiIiIiIiIi4kYG4+kQfJouCRERERERERERr6OChYiIiIiIiIh4HRUsRERERERERMTraAwLERERERERERsYozEsboZ6WIiIiIiIiIiI11HBQkRERERERES8jgoWIiIiIiIiIuJ1VLAQEREREREREa+jQTdFREREREREbKFBN2+GeliIiIiIiIiIiNdRwUJEREREREREvI4KFiIiIiIiIiLidTSGhYiIiIiIiIgNjPoI3BT964mIiIiIiIiI11HBQkRERERERES8jgoWIiIiIiIiIuJ1NIaFiIiIiIiIiC2MpwPwaephISIiIiIiIiJeRwULEREREREREfE6KliIiIiIiIiIiNfRGBYiIiIiIiIiNjBGY1jcDPWwEBERERERERGvo4KFiIiIiIiIiHgdWy8JMcZEAW2BYoAL2A98ZFlWjJ3rFRERERERERHfZlsPC2PMAGAWEAJUA7KRWrjYYoy5z671Xk1iYhKdO46hbevhtGo+lGlTP7lquy8+/4aWzYfQqvlQhg+dAsChQ3/Qsd0o2rQazne79gPgdLp45KEXiI9PdFsO/8SY0dOoVbMXLVoMvOr8ixdjeeLxl2jd6imaNx/Ip/+3DoBDB4/Rru1QWrV8il279gGpuT700DNemyvAAw2H0K7VGDq2GUeXDuOvmL9t615qVX+cjm3G0bHNOGbNWArA2bMx9Oz2Am1bjua/a3ektx/Y901OnjzntvhFJGvzt2Oyv+XrT/xp2+rcMaOstG2zeq4li+Zi+fRW6Y9d/9eNXq3LAdC9ZVm+mNuWVbPbMLx31Wsuw+EwLJvWijnPNkyfNnF4XVbMbM3gXlXSpz3ZpSIN77nFvmREsLeHxaNAJcuyXMaYScAqy7LuM8bMBpYBlW1cdwbBwUG8M28c2cNCSE520qPbeGrXrkTFSremtzlyOJq35i5j/ofPEh6egzNnLgDwycK1jBzdk8JF8vHKS+/xZuXBLPz4PzRveS+hodnclcI/0rpNPbo++AAjR0656vyPPvycUqWLMnPWaM6evUDTB/rTvEUdFi5cw+gxvSlSJB8vvfgOlacO5+MFX9CyRR2vzfVPb707koiInNecX7lKGabNHJxh2ucrt9ChYz0aNKpKv8cnUb9hFTas38XtZYuTP3+E3SGLiJ/wt2Oyv+XrT/xp2+rcMaOstG2zeq6HjsbQsu8yILXwsPmDTqz5+gg1KhSkwT3FafnkUpKSU4gMD7nmMnq2LseB38+TI3sQALeVjCAhyUmLJ5by7ktNyJE9iNCQQCrelo8ZC753S16+TYNu3gy7x7D4syCSDcgBYFnWb0CQzevNwBhD9rDUN6XT6cKZ7OJ/B2td/Ml/6dylMeHhOQDIkyf8/9u77zgp6vuP46/P3SFVQDoKiCgawB8gLcQoUiwgBhsS1FgSFU0UsCLEqDH+0lRsscSOiRULPzUmBDU2rAgCgkYFAirCAUcNcFz7/P7Y4TwQhLvbmdnbeT953OO2zM583nx352a/+53vApCXl8vmwi0Ubt5CXl4u69dv5LVXZzLs+H5RRqiU3r270LjRzt+8mxkbN27G3dm0qZBGjRqQl5dLXq1cCjdvYfPmIvJqpbK++uoHHH9C/+iKj1CtWrkUFhZRXFRCTk4OJSWlPPqXaZx9zrFxlyYiWSRp++Sk5U2SJLWtjh23lU1tm6Ssh3ZvzRfLNvD1io2cdlwn7p08l6LiMgBWryvc4WNaNatH/95tmTz1s/LbSkrKqLNHHmaQl5dDWZkz9owe3PbIh5HkkGQLc4TF/cAMM3sPOBz4I4CZNQdWh7jdHSotLWPE8Al88cVyTj31aLp267jN/UuWLAPgJ6ddQ1lpGb+4aDiHHd6dU087hgnj76S4qIRrrjuXe+5+lvPOP4GcnJo7X+nppx/LL37xe/r1O4dNGwuZePOl5OTkcNppQxh/5e0UFRVz3W8u4O67JnP++SdnflaDC869ETMYPmIAw0cM+NYic2cv4JQTf0Xz5o259IqRHNCxDUOG/oAJV/yZZ556jYsvHcGTj7/CccMOzahechHJflm3T96FpOVNkmxrWx07fiPb2va7ZFPWoUd04G+vLQJgv30a0qtLSy49qydbikr4w/0z+OizVd96zFXnf58bHphB/XrffL688Mt1rF5XyHN3HM//vbKQffduSE6O8fGCgsiySHKF1mHh7reZ2ctAJ2Ciu/87uH0lUN7FbGZ7uXvoEwbk5ubwzJQ/sn79RsaOnsjnn31JxwPblt9fUlLKkiXLeejha8jPX81ZZ/yaKc/dSOu9mzHpL6l5Eb5Yspzlywvo0GEfxo+7g+LiEkaPGUH7/fYOu/y0mj79Q77XqT2THr6OL75Yzjk/u45evTqz997N+ctfrwdSf4SX5xfQYf82jBt3G8XFxYwZcxr7ZWDWSY9cRcuWTSgoWM8F597Afh1a07PX98rv79S5PVNfvpl69evw5utzuGT07bww9Qb23LMed/w5dZrI+nUbefD+F7nl9jFcd82DrF+/kTPPHkK37gfEFUtEEiLb9sm7krS8SZJtbatjx29kW9t+l2zJWisvh4F923HTQx8Aqedzoz1rM/ziF+h6YDNu++UABp697dwsA/q0pWBtIfMXFNCna6tt7vvtPe+VX77n10dy9e1v8fOR3fhehya8NWvpNiMyRNIp1C5Bd5/v7k9v7azYiVfCrGF7DRvWp0+fLkyfPnub21u2asqAgT2pVSuPNm1a0L59a5YsWb7NMrfd+gRjxv6YRx+ZysnDB3LZ5adz913PRFl+Wjw75V8cdVRfzIx9921NmzYtWLRo6TbL3Hrro4wdexqP/PVFhg8/kssvP5O77nwypoq/W8uWTQBo2rQhAwf1ZN7cRdvc36BB3fJhnYcf0Y2SklLWrNmwzTL3/Pk5zj3/R/zj7+9ySI8Duf53o7j7zinRBBCRRMu2ffKuJC1vkmRr2+rYMXvbdkeyJWu/Xm34eEEBBWtTp34sX7WRaW8tBmDuZ6vwMv/WPBY9urRgUN92vPrwKdw6vj99u+3NTeO2PZVpUN92zFtQQL26tWjXek/G/u5VBh/enjq1cyPJVRMZOVn5E5VMGMMU+iwkq1evZ/36jQAUFhbxzjtzv9UDOmhQL2a8/zEAa9asZ/HiZbRt06L8/hnvf0yLFnuxb/vWFG7eQk6OYTnG5s1FYZefdq1bN+fdd+YCsGrVWv7zn69p27Zl+f3vvz+fFi2a0L793mwuTGXNyclhc2HmzIC81aZNW9i4cXP55XfenscBHdtss8yqlWtxdwA+mruQsrIyGjduUH7/ksXLWbF8Nb37dKKwsAjLMcxgS2HNa1sRqXmyaZ+8O5KWN0myqW117LitbGrbXcmWrMf1/+Z0EICX315C326tAWi/T0Nq1cr51jwWEx+ayeFnPMmAs57i4j+8xrtzvubyG94ovz8v1zj7xC7c99Rc6uyRhwe35+TkUCtPHRYSDtv6Ri62AsxmuXuPHd1XXPZhWor79NMlXDXhbkpLy/CyMo4Z/AN+fuHJ3HH7ZLoc3IEBA3vh7tz4x78yffoccnNyOO/8Ezl26KEAuDvnnfM7Jt48lkaNG7Bw4VLGX/EnSkrLuPrac+jR46Bq15hre1R7HVtddunNvD9jHmvXbKBp00ZcNHokJSWlAIwceQwr8lczYcKfWLlyDY5z3nknMWzYEUAq6znnXMfNN19G48Z7snDhV1xxxS2UlpRx7a9H0aNHp7TUWFS2YdcL7YavvlzBJWNSszyXlJRy7NAfcN4Fw5j8xL8AGDFyII8/+hKTn/gXeXm51K69B5dfeSrdD/nmPNQrLrmDi8YOZ9/2rSgoWM8lo29jw4ZNXDj6JI48une1a6yT26Ta6xCRaJV5cdrWVRP2yemU6XlzLNJ5v2OXpOdyqaevI0DHjpnVtulUE7IeNOS9XS/0HerWzuP1v45g4NlP8d9NqX1Arbwcfn/pYXTq0JTiklL+cN8M3p2zjBZN6vLbiw/jvGte2mYdfbq24tyTD2bUtS+X33b2CZ1Zv7GIZ19aAMAt4/vTcd/GvD7jK2588IMq1/v51J9l9ddopOs9baaplXNIJO2WiA6LmiCdf3RqgnR1WNQE6rAQqXnS+SZPMos6LLJXOjssaoKkHTsmSXU7LGoadVjUTFF1WIT5LSG7K6ufoCIiIiIiIpJUej7DQM8AABQCSURBVLtbHaF3WJjZjj5e3uBe3uU/KOwaRERERERERKRmiWLSzVnASuAz4PPg8mIzm2VmPd19dcWFzWyUmX1gZh/cf2/Nm0VZRERERERERKovilNCXgKedvd/ApjZ0cDJwEPAXcD3Ky7s7vcC90K05/ssW7aKX46/i4KCdRjG8BEDOePMY6PafOSu+uUdvPbaBzRp2ogXXrgt7nJCt2VLET8983cUF5VQUlLKUUf35hejT4q7LBERIHn75KTlTZIkta2OHbNbNuTNyTGm3D6M/IKNjLr2ZSaOO4KDD2xGSUkZcz9dydW3v0VJaertVl6u8fRtP+KEi57n1YdPYeOmYsrKnJJS56QxzwMw+ieHMGLwgawJvl1k4qSZvD7jq9jySTJEMcKi79bOCgB3nwb8wN3fBWpHsP3dkpebyxXjzuD5v03ksSev54nHprFwQfa+AE84cQD33nd13GVEZo89anH/g+N5asr/MvnZ63lr+kfMnbMg7rJERIDk7ZOTljdJktS2OnbMbtmQ96wTOrPwy7Xl159/dSHHnPsMQy+YQp3aeYwY/M231fTs0pKZ81eUXz/jyn8w7MLnyjsrtpo0ZT7DLnyOYRc+p86K3WRZ+i8qUXRYLDOzK81s3+BnHJBvZrlAWQTb3y3NW+xF5y77AVC/fl067L8P+fmrd/Gomqt37y40brRn3GVExsyoV78OkPr609TXV2kCHBHJDEnbJyctb5IkqW117JjdanreVs3q0b93WyZP/az8toodDHM+XUnLZvXLr/fr1YY3PlAHhGSeKDosTgPaAP8X/LQLbssFRkSw/UpbunQFn3yymK7dDoi7FEmj0tIyRpx4NQMOG03fQ7vQtdv+cZckIiIiWUDHjpJprjr/+9zwwAzK/Ntn2OflGicM2p83K3RQfL9ba96buwwAd3jod8cw5U/D+PGQg7Z57E+GdeKFu0/g95ccRsMG+mpdCV/oc1i4+ypg9E7uzrgx+Zs2FnLJmFu4cvxZNGhQL+5yJI1yc3OYPOV61q/fyCVjbufzz7+iY8c2cZclIiIiNZiOHSXTDOjTloK1hcxfUECfrq2+df+vLzqUGR/l88H8fABaNq3Hug1bKNxSCsCpl71IfsEmmjSqw6TfD2bRl2uZMS+fx/72CXc+Nht35+IzezLhvD5MuGV6pNkkeULrsDCzF4CdTprp7sPC2nZVFReXcPHYmxn6o8M46ug+cZcjIWnYsD69+3Ti7TfnqsNCREREqkzHjpKJenRpwaC+7TiiTxtq18qlQb09uGlcPy6/4Q0uOr07TRrV4cLbXylf/vBe+/DmzKXl1/MLNgGwel0hL729hK4HNWfGvHwK1haWLzN56qfce91R0YWSxApzhMVNwe+TgFbAI8H1U4H8ELdbJe7ONb+6hw4d9uGss4fGXY6k2erV68nLy6Vhw/oUFhbx7tvz+em5amcRERGpGh07Sqaa+NBMJj40E4A+XVtx7skHc/kNb3DK4AM5vOc+nDl+KhXPFOnXqw23PjwLgLq188jJgY2bS6hbO4/DeuzNHY/OBqB5k7qsXL0ZgKMO3ZfPFq+JNlgNZaZ586ojtA4Ld38dwMwmunuvCne9YGYfhLXdqvpw1qe88PybdDywHSefeCUAYy8eSb8jDom5snBcdunNvD9jHmvXbKD/Eedy0eiRDB9+ZNxlhWbVyrX8asJ9lJWVUVbmHD24D0f07x53WSIiQPL2yUnLmyRJalsdO2Zv20J25v3N6EP5Ov+/PHXLcQBMe2sJdz0xh31bN2TRV+sAaLZXXe68ZhCQmuvihVcXlY++GHdObzp1aIIDS/P/y9W3vxVLDkkW8x1MxJLWDZh9Agx190XB9Q7Ai+7eaVePLS77MNziMkiuJWvSmqKyDXGXEJk6uU3iLkFEKqnMi+MuQUKSY7XiLiFSSXoul3pR3CVEKmnHjkly0JD3IttWzy4tOX7g/lzzp7cj2+b2Pp/6s6weglDqc7PyPW2udY2k3UKfdBO4GHjNzBYF19sDoyLYroiIiIiIiOzEzPn5zJyfcWfri5SLosOiIXAwsB8wDDgUWBXBdkVERERERERilBN3ATVaFP97V7v7emBPYCBwB3B3BNsVERERERERkRoqig6L0uD3UOA+d38R0El3IiIiIiIiIrJTUXRYLDWze4AfA383s9oRbVdEREREREREaqgo5rAYAQwGbnL3tWbWGrgigu2KiIiIiIiIxMbI6i9BCV3oHRbuvgl4tsL1ZcCysLcrIiIiIiIiIjWXTs0QERERERERkYyjDgsRERERERERyThRzGEhIiIiIiIikkCaw6I6NMJCRERERERERDKOOixEREREREREJOOow0JEREREREREMo46LEREREREREQk42jSTREREREREZEQmGnSzerQCAsRERERERERyTjqsBARERERERGRjKMOCxERERERERHJOJrDQkRERERERCQUGiNQHfrfExEREREREZGMow4LEREREREREck46rAQERERERERkYyjDgsRERERERGREFiW/tut7GaDzexTM1tgZuOr8v+nDgsRERERERERSRszywXuBIYAnYFTzaxzZdejDgsRERERERERSac+wAJ3X+TuRcATwPGVXYk6LEREREREREQknfYBvqxw/avgtkrJS1s5IaiVc8junRyTZmY2yt3vjWPbUYsra53cqLeYorbNXknKm6SsEE/enFj++qQkqX2TlBWS9VxOUlZI1nM5SVkhnryfT+0S5ebKJa1to3NgjHun8JjZKGBUhZvuDeP5oxEWOzZq14tkjSRlhWTlTVJWSFbeJGUF5c1mScoKycqbpKyQrLxJygrJypukrFJN7n6vu/eq8LN9Z8VSoG2F622C2ypFHRYiIiIiIiIikk4zgI5mtp+Z7QGMBJ6v7Eoy+pQQEREREREREalZ3L3EzC4C/gnkAg+6+/zKrkcdFjuWpHO3kpQVkpU3SVkhWXmTlBWUN5slKSskK2+SskKy8iYpKyQrb5KySgTc/e/A36uzDnP3NJUjIiIiIiIiIpIemsNCRERERERERDKOOixEREREREREJOMkusPCzPqb2d+q+NgHzWyFmc1Ld13pEkY+M2tiZi+Z2efB773SU231hZT3FDObb2ZlZtYrPZVWX0hZbzSzf5vZXDObYmaN01Nt9YWU9/og62wzm2Zme6en2uoJc79kZpeZmZtZs+pVmT4hte2vzWxp0LazzezY9FSbPlXNbWZtzexVM/s42DeNDaO+dKtG3jpm9r6ZzQnyXhdGfelWned18PhcM/uwOuuISjVfw4vN7KPgdfpBumtLt2pmbWxmTwd/Zz8xsx+ku750q8br9qAK+9/ZZrbezC4Oo8Z0qmb7XhLso+aZ2eNmVifd9aVTNbOODXLOrwntKjVfojssqmkSMDjuIkI0iR3nGw+84u4dgVeC69lgEjvOOw84CXgj0mrCNYkdZ30JONjduwKfAROiLCpEk9hx3hvdvau7dwf+BlwTaVXhmMRO9ktm1hY4GvgiyoJCNomd74dvcffuwU+1JnvKMCXAZe7eGegLXGhmnWOuKUxbgIHu3g3oDgw2s74x1xSFscAncRcRkQHB6zRjPhQIyW3AVHf/HtCNLG5fd/906/4X6AlsAqbEXFZozGwfYAzQy90PJvVtCCPjrSocZnYwcB7Qh9Tz+DgzOyDeqiTbZfW3hJhZfWAy0IbUzuN6YB1wK6md5/QKyzYHHgP2Bt4BjgJ6uvuqHa3b3d8ws/Yhlr9LMeU7HugfXH4YeA24srpZdkcced39k2B9aUyyazFlnVbh6rvA8DRE2S0x5V1f4Wp9IJIZiGPcL90CjAOeS0eO3ZXt++GdCSu3uy8DlgWXN5jZJ8A+wMdh5tmVEPM68N/gaq3gJ/bZwsN8XptZG2Ao8Fvg0vBS7J4ws2aasLKaWSOgH3A2gLsXAUUhRtktEbXtIGChuy9Je4BKCjlvHlDXzIqBesDXIcXYLSFm7QS85+6bgse+TuqDvRtCCyOJl+0jLAYDX7t7t6DHcypwH/AjUj2+rSosey3wL3fvAjwNtIu62CqII1/L4IAZYDnQsorrqYpsb8+K4s76M+AfaVjP7oolr5n91sy+BE4nuhEWkWc1s+OBpe4+p1qVV01cz+WLglN+HrR4Tl0LPXfQWXMI8F76yq6y0PIGp0fMBlYAL7l7Vucl9YZiHFCW9qqrJsysDkwzs5lmNir9pVdaWFn3A1YCDwWn+twfvKGMWxT755HA4+kruVpCyevuS4GbSI1gXAas2+5DoDiE1bbzgMPNrKmZ1QOOBdqGEUBkq2zvsPgIOMrM/mhmh5P6g/Efd/88+NTmkQrLHgY8AeDuU4E1kVdbebHmC7YR5Sdd2d6eFcWW1cyuIjXs/NHqrKeSYsnr7le5e1tSWS+qcvWVE2nW4IDil8R3ykscbXs3sD+pUwiWAROrWnw1hJrbzBoAzwAXbzdaKC6h5XX3Uk8NLW8D9AmGJMctlLxmdhywwt1nhld6pYX5XD7M3XsAQ0id3tQv/eVXSlhZ84AewN3ufgiwkcw4pTbs/dQewDDgqbRXXjVhvW73IjUCeT9SoxTqm9lPQsqwu0LJ6qmRx38EppHqBJkNlIYTQSQlqzss3P0zUn8gPgL+l9ROM2vElC/fzFoDBL9XRLBNIPvbs6K4sprZ2cBxwOnBH7RIZEDbPgqcHMWGYsi6P6kDlTlmtpjUm75ZZtbqOx+VJnG0rbvnB29yy0h9otQn7G3uoIbQcptZLVKdFY+6+7PpWm91RNHO7r4WeJUMmD8qxLw/BIYFr9UngIFm9sh3PyRcYbZt8Mk07r6C1BwHkb9Wt6snrKxfAV9VGB30dLCdWEXwuh0CzHL3/DSvt0pCzHskqc6Ale5eDDwLHJqmdVdJyK/bB9y9p7v3I9W58Vm61i2yI1ndYWGpWf83ufsjwI2kdh7tzWz/YJFTKyz+FjAieNzRQMZ8+8XOxJTveeCs4PJZRHg+fLa3Z0VxZDWzwaSGIQ/z4NzEqMSUt2OFq8cD/67Keqqw3UizuvtH7t7C3du7e3tSB8493H15NWLstpjatnWFqyeSGsIaqbByW2pCnQeAT9z95jBqr4oQ8za34BuLzKwuqXOrI3mtfpew8rr7BHdvE7xWR5Iaph3rJ7Uhtm19M9tz62VSkwLH+s1rIbbrcuBLMzsouGkQMc87A5Hsn08lc04HCTPvF0BfM6sX7KMHEfOkqmG2rZm1CH63IzV/xWPprV5kW1k96SbwP8CNZlYGFAM/B5oBL5rZJuBNYM9g2euAx83sDFITziwHNuxsxWb2OKnJJ5uZ2VfAte7+QFhBdiKOfH8AJpvZOcASgh1cRCLPa2YnAn8Cmgfbme3ux4SSbltxtO0dQG3gpdTfW9519wtCyLYjsTyXg4PHMlLP5WzOGqc48t5gZt1JnbK2GDg/hFy7ElbuHwJnAB9Zal4HgF96/N+EElbe1sDDZpZL6kOWye6eCV/1GdrzOgOFlbUlMCX4e5MHPBYMR49TmO06GnjUUqdJLAJ+Gk6ESglz/1yfVAdjHPvfnQklr7u/Z2ZPA7NInVL7IXBvmEF2Q5jP5WfMrGmw3guD0W8iobEIR31nNDOrDZS6e4mlvhv77uCc2ayQ7fm2l6S8ScoKycqbpKyQvLxbJS238mZvXmXNzqygvGRx3iRllZop20dYVEY7UiMHckh91dR5MdeTbtmeb3tJypukrJCsvEnKCsnLu1XScitv9lLW7KW82StJWaUG0giL7xAMd3plB3cNcveCqOtJt2zPt70k5U1SVkhW3iRlheTl3SppuZW3XNblVVYgC7OC8laQdXmTlFUynzosRERERERERCTjZPW3hIiIiIiIiIhIzaQOCxERERERERHJOOqwEBERqSQzKzWz2WY2z8yeMrN61VjXJDMbHly+38w6f8ey/c3s0CpsY7GZNatqjSIiIiJxUIeFiIhI5W129+7ufjCpWdUvqHinmVXpW7jc/Vx3//g7FukPVLrDQkRERKQmUoeFiIhI9bwJHBCMfnjTzJ4HPjazXDO70cxmmNlcMzsfwFLuMLNPzexloMXWFZnZa2bWK7g82MxmmdkcM3vFzNqT6hi5JBjdcbiZNTezZ4JtzDCzHwaPbWpm08xsvpndD1i0/yUiIiIi1VelT4BERESkfCTFEGBqcFMP4GB3/4+ZjQLWuXtvM6sNvGVm04BDgIOAzkBL4GPgwe3W2xy4D+gXrKuJu682sz8D/3X3m4LlHgNucffpZtYO+CfQCbgWmO7uvzGzocA5of5HiIiIiIRAHRYiIiKVV9fMZgeX3wQeIHWqxvvu/p/g9qOBrlvnpwAaAR2BfsDj7l4KfG1m/9rB+vsCb2xdl7uv3kkdRwKdzcoHUDQ0swbBNk4KHvuima2pYk4RERGR2KjDQkREpPI2u3v3ijcEnQYbK94EjHb3f2633LFprCMH6OvuhTuoRURERKRG0xwWIiIi4fgn8HMzqwVgZgeaWX3gDeDHwRwXrYEBO3jsu0A/M9sveGyT4PYNwJ4VlpsGjN56xcy2dqK8AZwW3DYE2CttqUREREQiog4LERGRcNxPan6KWWY2D7iH1MjGKcDnwX1/Ad7Z/oHuvhIYBTxrZnOAJ4O7XgBO3DrpJjAG6BVM6vkx33xbyXWkOjzmkzo15IuQMoqIiIiExtw97hpERERERERERLahERYiIiIiIiIiknHUYSEiIiIiIiIiGUcdFiIiIiIiIiKScdRhISIiIiIiIiIZRx0WIiIiIiIiIpJx1GEhIiIiIiIiIhlHHRYiIiIiIiIiknHUYSEiIiIiIiIiGef/AYHfNiez6zdwAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.argmax(pred[0], axis=1)\n",
        "cm = confusion_matrix(labels, predictions, labels=np.unique(labels))\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBL41mhdOyQt",
        "outputId": "849a0c37-c5ae-4a43-9fcf-dd4c8e2bc9bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 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, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0msTtvi1O5KE",
        "outputId": "b75972c2-e985-4849-bff6-314cdce5368f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZfZBxXqT3KA",
        "outputId": "60cce33d-c05a-428b-a437-f42a567a35cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.19.2-py3-none-any.whl (4.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.2 MB 14.6 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 50.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 63.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.7.0-py3-none-any.whl (86 kB)\n",
            "\u001b[K     |████████████████████████████████| 86 kB 6.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.5.18.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.7.0 pyyaml-6.0 tokenizers-0.12.1 transformers-4.19.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"text-classification\", model=\"jonas/sdg_classifier_osdg\")\n",
        "\n",
        "\n",
        "sdg_dict = {\n",
        "'1':\"SDG 1: End poverty in all its forms everywhere\",\n",
        "'2':\"SDG 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture\",\n",
        "'3':\"SDG3: Ensure healthy lives and promote well-being for all at all ages\",\n",
        "'4':\"SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all\",\n",
        "'5':\"SDG 5: Achieve gender equality and empower all women and girls\",\n",
        "'6':\"SDG 6: Ensure availability and sustainable management of water and sanitation for all\",\n",
        "'7':\"SDG 7: Ensure access to affordable, reliable, sustainable and modern energy for all\",\n",
        "'8':\"SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all\",\n",
        "'9':\"SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation\",\n",
        "'10':\"SDG 10: Reduce inequality within and among countries\",\n",
        "'11':\"SDG 11: Make cities and human settlements inclusive, safe, resilient and sustainable\",\n",
        "'12':\"SDG 12: Ensure sustainable consumption and production patterns\",\n",
        "'13':\"SDG 13: Take urgent action to combat climate change and its impacts*\",\n",
        "'14':\"SDG 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development\",\n",
        "'15':\"SDG 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss\"\n",
        "}\n"
      ],
      "metadata": {
        "id": "g4EF0KEOj9rJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def predict(text):\n",
        "    preds = pipe(text)[0]\n",
        "\n",
        "    return sdg_dict[preds[\"label\"]], str(round(preds[\"score\"], 5)*100) + \" %\"\n",
        "    ##return sdg_dict[int(preds[\"label\"][0])], str(round(preds[\"score\"], 5)*100) + \" %\", sdg_dict[int(preds2[\"label\"][0])], str(round(preds2[\"score\"], 5)*100) + \" %\"\n"
      ],
      "metadata": {
        "id": "yPyiBBGIUwBk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = predict('The Paris Agreement, adopted in 2015, aims to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 degrees Celsius above pre-industrial levels. The agreement also aims to strengthen the ability of countries to deal with the impacts of climate change, through appropriate financial flows, a new technology framework and an enhanced capacity building framework.')"
      ],
      "metadata": {
        "id": "FkqsKR8oTx89"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uu5YO2ZhUPaX",
        "outputId": "12b6f05a-f37d-46de-ce09-a59a77228485"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('SDG 13: Take urgent action to combat climate change and its impacts*',\n",
              " '96.38 %')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int(a[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eCGh4qyUdxD",
        "outputId": "5fea1e2c-5ec5-4e76-d5a3-10f6fc9496be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_osdg['text'].iloc[1]"
      ],
      "metadata": {
        "id": "q_wKQLSjUf65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "2121d315-dd11-47b9-e2f8-7ebdf808aa2c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Labour legislation regulates maximum working hours, industrial safety, minimum wages and benefits for workers and the prevention of child labour, but enforcement, if any, is weak. Therefore, the immigration laws applicable in Western countries cannot be applied to these workers. T. H. including basic public services. ,,'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Domain Adaptation"
      ],
      "metadata": {
        "id": "VysymZmTVwlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from spacy.lang.en import English\n",
        "nlp = English()\n",
        "nlp.add_pipe('sentencizer')\n",
        "\n",
        "text= ' '.join([tex for tex in list(df_osdg['text'])]) #.iloc[1]\n",
        "doc = nlp(text)\n",
        "\n",
        "sents_list = []\n",
        "for sent in doc.sents:\n",
        "   sents_list.append(sent.text)\n",
        "\n",
        "print(len(sents_list))\n"
      ],
      "metadata": {
        "id": "MvEoL1myWloh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers==2.2.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "acMi_jZfWkqe",
        "outputId": "c42ab816-a99c-40a7-98df-150edb5521a1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence-transformers==2.2.0\n",
            "  Downloading sentence-transformers-2.2.0.tar.gz (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 3.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (4.24.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (4.64.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (1.12.1+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (0.13.1+cu113)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (1.7.3)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (3.7)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (0.1.97)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.7/dist-packages (from sentence-transformers==2.2.0) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence-transformers==2.2.0) (4.1.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (21.3)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (0.13.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (2022.6.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (4.13.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (3.8.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (3.10.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers==2.2.0) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers==2.2.0) (1.2.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers==2.2.0) (1.25.11)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers==2.2.0) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers==2.2.0) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.0-py3-none-any.whl size=120748 sha256=81eda39c5ee95be65ca023df96b3e90e4c7c333e1473807aa90e9f4145880067\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/c0/df/b6873ab7aac3f2465aa9144b6b4c41c4391cfecc027c8b07e7\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentence-transformers\n",
            "  Attempting uninstall: sentence-transformers\n",
            "    Found existing installation: sentence-transformers 2.2.2\n",
            "    Uninstalling sentence-transformers-2.2.2:\n",
            "      Successfully uninstalled sentence-transformers-2.2.2\n",
            "Successfully installed sentence-transformers-2.2.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sentence_transformers"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yajNRYs7Yfry",
        "outputId": "3150900e-f805-4f34-c625-1be01b1580a9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, LoggingHandler\n",
        "from sentence_transformers import models, util, datasets, evaluation, losses\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define your sentence transformer model using CLS pooling\n",
        "model_name = 'bert-base-uncased'\n",
        "word_embedding_model = models.Transformer(model_name)\n",
        "pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), 'cls')\n",
        "model = SentenceTransformer(modules=[word_embedding_model, pooling_model])\n",
        "\n",
        "# Define a list with sentences (1k - 100k sentences)\n",
        "train_sentences = list(df_osdg['text'])\n",
        "\n",
        "# Create the special denoising dataset that adds noise on-the-fly\n",
        "train_dataset = datasets.DenoisingAutoEncoderDataset(train_sentences)\n",
        "\n",
        "# DataLoader to batch your data\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
        "\n",
        "# Use the denoising auto-encoder loss\n",
        "train_loss = losses.DenoisingAutoEncoderLoss(model, decoder_name_or_path=model_name, tie_encoder_decoder=True)\n",
        "\n",
        "# Call the fit method\n",
        "model.fit(\n",
        "    train_objectives=[(train_dataloader, train_loss)],\n",
        "    epochs=1,\n",
        "    weight_decay=0,\n",
        "    scheduler='constantlr',\n",
        "    optimizer_params={'lr': 3e-5},\n",
        "    show_progress_bar=True\n",
        ")\n",
        "\n",
        "model.save('output/tsdae-model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275,
          "referenced_widgets": [
            "f88cbedc3ea747968900e2b7dc65acfa",
            "9df049b85ba04dfabd29e2c9275a2708",
            "4ea9ee7a1a854374aa4d2f16fbb4a1c5",
            "262042f4c5bb4b30a50b787976ec50c4",
            "1765c8ce0e664135b231969ce30194c6",
            "42966bb6ced44012866b7734163bc170",
            "c57bcbed66fc489bbdf7ce6a3934de0d",
            "ffcc07b3a3a441b095fc071d12a66a64",
            "fc582841d4e24279bbb14a23b41ef61b",
            "f8ede9e89cc34313ba3cee0b278eb76d",
            "d2b1acac7f7645f0be573580e7d68e4e",
            "f8cbd7892e8e4164be3787fc28bbb74c",
            "a955ea7322be4e3fac3565a58b3d37c7",
            "d2e639d0741f477a8880f8110a7c98ea",
            "c22431d2f2ec4d789d06871cfe3c8c83",
            "861a82f9698b4b8f93580022f37025f4",
            "5380413161044b15a681808746e70e50",
            "5cb4258f04eb48f7bf6e824698a932dc",
            "412ba00248ba4fbab93d41b54bb79dd3",
            "3c68da0c7bae43c5a8a23895b8c04c2b",
            "2f636f203c1e4f90936cc246f5a15b78",
            "549f3f665d2446ca913dba8fe3ccc6d8"
          ]
        },
        "id": "6gu0AHA7VzKY",
        "outputId": "6bf18921-557a-47f4-c7f9-132a13e2f62d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "WARNING:sentence_transformers.losses.DenoisingAutoEncoderLoss:When tie_encoder_decoder=True, the decoder_name_or_path will be invalid.\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertLMHeadModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertLMHeadModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertLMHeadModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.self.value.weight', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.4.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.2.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.4.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.self.query.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.0.crossattention.output.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "The following encoder weights were not tied to the decoder ['bert/pooler']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f88cbedc3ea747968900e2b7dc65acfa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/4015 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8cbd7892e8e4164be3787fc28bbb74c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RUqt-oUOsrAj",
        "outputId": "a83b6e90-619d-4f9d-d40e-bfdfe3a19834"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.7/dist-packages (0.11.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (3.8.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.1.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.64.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.13.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface_hub) (3.10.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (1.25.11)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2.10)\n",
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "    \n",
            "Token: \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# Load or train a model\n",
        "#model.save_to_hub(\"sdg_sentence_transformer\")\n",
        "model.save_to_hub(repo_name=\"peter2000/sdg_sentence_transformer\", train_datasets=[\"Filippo/osdg_cd\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "aBpQqk1Xqjuu",
        "outputId": "4b200e63-4cfc-4fe0-b69d-46d7983b34fc"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-53-55c4b9c224e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Load or train a model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#model.save_to_hub(\"sdg_sentence_transformer\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"peter2000/sdg_sentence_transformer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_datasets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Filippo/osdg_cd\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sentence_transformers/SentenceTransformer.py\u001b[0m in \u001b[0;36msave_to_hub\u001b[0;34m(self, repo_name, organization, private, commit_message, local_model_path, exist_ok, replace_model_card, train_datasets)\u001b[0m\n\u001b[1;32m    471\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m             \u001b[0;31m# If user provides local files, copy them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mlocal_model_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    474\u001b[0m                 \u001b[0mcopy_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_model_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmp_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Else, save model directly into local repo.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m             )\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m             )\n\u001b[1;32m     44\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: create_repo() got an unexpected keyword argument 'organization'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub('bert_sdg_adapted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "VSPZSYcVYC4A",
        "outputId": "6bb1de52-463b-48dc-a3ec-c7ee842faa0d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-998df0db34c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bert_sdg_adapted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1206\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m-> 1208\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m   1209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Module'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'SentenceTransformer' object has no attribute 'push_to_hub'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example1 = \"The most vulnerable countries are seeking ‘loss and damage’ compensation from the biggest polluters.\"                             \n",
        "example2 = \"COP27 opens amid compounding crises of war, warming and economic instability.\""
      ],
      "metadata": {
        "id": "Bg3wRHlWoo-M"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example1 = \"The most vulnerable countries are seeking ‘loss and damage’ compensation from the biggest polluters.\"                             \n",
        "example2 = \"COP27 opens amid compounding crises of war, warming and economic instability.\""
      ],
      "metadata": {
        "id": "cLNkkjbap0HH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example1 = \"The most vulnerable countries are seeking ‘loss and damage’ compensation from the biggest polluters.\"                             \n",
        "example2 = \"COP27 opens amid compounding crises of war, warming and economic instability.\""
      ],
      "metadata": {
        "id": "l1LAHi2Cp8MO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "xupEjlaCoo6s"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_original = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
          "referenced_widgets": [
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        "def similarity(x,y,model):\n",
        "    document = [x ,y]\n",
        "        \n",
        "    #Encode paragraphs\n",
        "    document_embeddings = model.encode(document , show_progress_bar=False)\n",
        "    #Compute cosine similarity between labels sentences and paragraphs\n",
        "    similarity_matrix = cosine_similarity(document_embeddings)\n",
        "\n",
        "    print(\"Text similarity:\", round(similarity_matrix[0][1]*100,2),\"%\")"
      ],
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      },
      "execution_count": 32,
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        "similarity(example1 ,example2,model)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "3afdf97e-0cfb-40b0-c976-27c2824a0627"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text similarity: 61.61 %\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "similarity(example1 ,example2,model_original)"
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        },
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      },
      "execution_count": 35,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Text similarity: 25.43 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r sdg_sentence_transformer.zip output/tsdae-model/"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__GyUjxCpevw",
        "outputId": "33c0854d-e473-4e98-dc98-415a57cefd9e"
      },
      "execution_count": 54,
      "outputs": [
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          "name": "stdout",
          "text": [
            "  adding: output/tsdae-model/ (stored 0%)\n",
            "  adding: output/tsdae-model/tokenizer.json (deflated 71%)\n",
            "  adding: output/tsdae-model/vocab.txt (deflated 53%)\n",
            "  adding: output/tsdae-model/1_Pooling/ (stored 0%)\n",
            "  adding: output/tsdae-model/1_Pooling/config.json (deflated 49%)\n",
            "  adding: output/tsdae-model/sentence_bert_config.json (deflated 4%)\n",
            "  adding: output/tsdae-model/config_sentence_transformers.json (deflated 26%)\n",
            "  adding: output/tsdae-model/special_tokens_map.json (deflated 42%)\n",
            "  adding: output/tsdae-model/config.json (deflated 48%)\n",
            "  adding: output/tsdae-model/pytorch_model.bin (deflated 7%)\n",
            "  adding: output/tsdae-model/tokenizer_config.json (deflated 41%)\n",
            "  adding: output/tsdae-model/modules.json (deflated 53%)\n",
            "  adding: output/tsdae-model/README.md (deflated 58%)\n"
          ]
        }
      ]
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