BERT_multiclass_emotion_classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/Muntasir2179/BERT-multiclass-emotion-classifier/blob/main/BERT_multiclass_emotion_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.31.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n"
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      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAwVPg-pW1am"
      },
      "source": [
        "## Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "6Ln6CMUmWhw-"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from tqdm.auto import tqdm\n",
        "import tensorflow as tf\n",
        "from transformers import BertTokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LIgYoUIXMAu"
      },
      "source": [
        "## Importing dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "C2vV9NG5XLrf"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('Emotion Dataset (English Text).csv', index_col=0)"
      ]
    },
    {
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      "execution_count": 7,
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              "                                                     text    label\n",
              "serial                                                            \n",
              "0                                 i didnt feel humiliated  sadness\n",
              "1       i can go from feeling so hopeless to so damned...  sadness\n",
              "2        im grabbing a minute to post i feel greedy wrong    anger\n",
              "3       i am ever feeling nostalgic about the fireplac...     love\n",
              "4                                    i am feeling grouchy    anger"
            ],
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 20000 entries, 0 to 1999\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text    20000 non-null  object\n",
            " 1   label   20000 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 468.8+ KB\n"
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            "text/plain": [
              "joy         6761\n",
              "sadness     5797\n",
              "anger       2709\n",
              "fear        2373\n",
              "love        1641\n",
              "surprise     719\n",
              "Name: label, dtype: int64"
            ]
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          "execution_count": 9
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              "<Axes: xlabel='label', ylabel='count'>"
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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x='label', data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_WrR41wZUs7"
      },
      "source": [
        "## Data preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "WHDz_G__ZX7-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "ca0cf9f8b2a9481787840476b115688c",
            "d4bfc36bc1b84316a984c2c15945a2ca",
            "1b4f5e46d7c84f0a8b18b0ccd00be658",
            "8ecd78117305400eadddee5e32ca32f4",
            "25f22a3ac1b54fa997186e1ce83fb379",
            "94c54da5dea441b7a3c1d10b9ba11bbd",
            "16bba26d3c4d4175900cc869f1ae3e27",
            "5460499834404d90a1eca317089366a8",
            "18d89054a964480aba256be4ebc2cc4e",
            "1f1b62b7f4394b25afcc3f8969c90d2b",
            "a2d49f6bc5aa42fb9b1520ac346b0763",
            "a5833d818b424b7798ff42ae286bedb2",
            "b0d042d911f14b65825394de2d3363d5",
            "5b8affdb2f8a4b479bed199d2207af3b",
            "76d44757e02e457fab6778def7ce2b8e",
            "66b59024540448d08d3fcaa241ce8e1a",
            "2ff8daed6dbf4103a210092196bd3b34",
            "541f776beebd468c93cd4bddd926bfbc",
            "3d4327d398624a099dda7f92aae15a0d",
            "fad77da2c94c4f749dac4af71726d6fc",
            "29ece187e5ae451b8700c1b676861ab1",
            "c6b40a6fa7ba47e498519f4fc63c3277",
            "b5013fa0778c46b7819b4c3b1de0b9ab",
            "f1d3b6dd6dd648c497466885cdd8b41e",
            "883eaec7dc284d9690f864836207204a",
            "a120f4a2fa644117b361348b61750c59",
            "0a18dce1167a40258d6e850acc1bf741",
            "ba083e8ddc2644a0a09534f227324de8",
            "ce7c598b01354031b13054d4c11e4be0",
            "33c32dcd6b8a497e87dd826873e40904",
            "77e303d73851478bba924623d346f997",
            "9d61b72f46e84e75b95fd09b066eb603",
            "d59db71a0b364fcfab75d4e43f5941b3"
          ]
        },
        "outputId": "a27be975-7a49-4807-a255-6177ebcff8fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca0cf9f8b2a9481787840476b115688c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 7d2106a5-a424-4383-8258-fae0fabb46c2)')' thrown while requesting HEAD https://huggingface.co/bert-base-cased/resolve/main/special_tokens_map.json\n",
            "WARNING:huggingface_hub.utils._http:'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 7d2106a5-a424-4383-8258-fae0fabb46c2)')' thrown while requesting HEAD https://huggingface.co/bert-base-cased/resolve/main/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5833d818b424b7798ff42ae286bedb2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5013fa0778c46b7819b4c3b1de0b9ab"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# initializing the tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "2UF_GUamZkjH"
      },
      "outputs": [],
      "source": [
        "token = tokenizer.encode_plus(\n",
        "    df['text'].iloc[0],\n",
        "    max_length=256,\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    add_special_tokens=True,\n",
        "    return_tensors='tf'\n",
        ")\n",
        "# special tokens refers to [CLS], [SEP], [PAD] tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ASu2qoSMajRX"
      },
      "outputs": [],
      "source": [
        "x_input_ids = np.zeros((len(df), 256))\n",
        "x_attn_masks = np.zeros((len(df), 256))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jR_CR6FAbhhF",
        "outputId": "9c08f7b4-794a-4754-baba-1faeeb80cf4c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 256)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "x_input_ids.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "uwhDbG9WblKe"
      },
      "outputs": [],
      "source": [
        "def generate_training_data(df, ids, masks, tokenizer):\n",
        "  for i, text in tqdm(enumerate(df['text'])):\n",
        "    tokenized_text = tokenizer.encode_plus(\n",
        "        text,\n",
        "        max_length=256,\n",
        "        truncation=True,\n",
        "        padding='max_length',\n",
        "        add_special_tokens=True,\n",
        "        return_tensors='tf'\n",
        "    )\n",
        "    ids[i, :] = tokenized_text.input_ids  # populate all the ids for the i'th text\n",
        "    masks[i, :] = tokenized_text.attention_mask  # populate all the masks for the i'th text\n",
        "  return ids, masks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "lEMMEiandJs9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ea0830161ec74bbca14c36fcd29b945b",
            "4440fca211784cf7a0e958a65e5abe03",
            "30974e1eff0c4dfdb186c253ca785b00",
            "207b8bf4ecee486593ae65e3ccf468ba",
            "84644086f858407f95e9a83f98402300",
            "e033e516d7d5406e8feaca7c9524cb8b",
            "169df1f399da4813bcdc51a773baaf79",
            "84f1a7f767f8406199a4d3b70e07f7fb",
            "e29c1970f4524ca782e7f14c98024208",
            "c3fd4f2721c540048a6366ff201decd8",
            "229f764aa6b14ac09d5cce3a7ee397b8"
          ]
        },
        "outputId": "7b18530d-7053-425e-ec85-ab00280294a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea0830161ec74bbca14c36fcd29b945b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "x_input_ids, x_attn_masks = generate_training_data(df, x_input_ids, x_attn_masks, tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3L5haGssN7_0"
      },
      "source": [
        "## Applying OneHot encodeing on the labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ev-I_W_gNIVL"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "encoder = OneHotEncoder()\n",
        "labels = encoder.fit_transform((df['label'].to_numpy()).reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "aEmru0mxB4W3"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "with open(\"onehot_encoder_for_BERT_multiclass_classification.pkl\", \"wb\") as f:\n",
        "    pickle.dump(encoder, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "cV0-xZ5jNpwG"
      },
      "outputs": [],
      "source": [
        "labels = labels.toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Q3CKHvcOVu6",
        "outputId": "6ca7d2aa-cdd0-42cd-9777-bfe7056bb5e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1., 0.],\n",
              "       [1., 0., 0., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbpdYt0dOQK9"
      },
      "source": [
        "## Creating data slices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "aXLH8c8hf-39"
      },
      "outputs": [],
      "source": [
        "dataset = tf.data.Dataset.from_tensor_slices((x_input_ids, x_attn_masks, labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9bSzxeCgSZe",
        "outputId": "8146de60-aa08-4a5f-a78b-bfd8abcfb585"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_TakeDataset element_spec=(TensorSpec(shape=(256,), dtype=tf.float64, name=None), TensorSpec(shape=(256,), dtype=tf.float64, name=None), TensorSpec(shape=(6,), dtype=tf.float64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "dataset.take(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "_-2smwNEgde9"
      },
      "outputs": [],
      "source": [
        "# map function\n",
        "def SentimentDatasetMapFunction(input_ids, attn_masks, labels):\n",
        "  return {\n",
        "      'input_ids': input_ids,\n",
        "      'attention_mask': attn_masks\n",
        "  }, labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "AdS9uDn_g5Z0"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.map(SentimentDatasetMapFunction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "al2eqUARhAB2",
        "outputId": "3b6a60a1-125c-4e3b-ef9c-875850688aef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_TakeDataset element_spec=({'input_ids': TensorSpec(shape=(256,), dtype=tf.float64, name=None), 'attention_mask': TensorSpec(shape=(256,), dtype=tf.float64, name=None)}, TensorSpec(shape=(6,), dtype=tf.float64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "dataset.take(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "NQprXZmohKDk"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.shuffle(20000).batch(16, drop_remainder=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcD-9hRqpIPy",
        "outputId": "786886c6-ea4d-4832-8358-2963325398fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1250"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "len(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "JIGi5RU8hVME"
      },
      "outputs": [],
      "source": [
        "train_size = int((len(dataset)) * 0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_QEC-FnhhLM",
        "outputId": "eb0c27c2-efa4-47fb-8546-941b0542293e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "train_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "0WfbmEENhqqc"
      },
      "outputs": [],
      "source": [
        "train_dataset = dataset.take(train_size)\n",
        "val_dataset = dataset.skip(train_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajFWcaE4c5A0",
        "outputId": "feeec41d-dd5f-4e4d-c1bc-48721cc78ceb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "len(train_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3NgrCe1c-ql",
        "outputId": "c958d8ec-8618-4460-9c5e-b769a90dddbd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "250"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "len(val_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhDdPz9Zh801"
      },
      "source": [
        "## Model creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "sCaBqUvVh75-"
      },
      "outputs": [],
      "source": [
        "from transformers import TFBertModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "3ed475fed8cf4dd89320579adb59d8de",
            "9cd68eddf72d42669a73505876671683",
            "edea5791043948909f54a2f6f312d521",
            "8960ff99b27641edbecee88e41bd1f91",
            "adb016efb3014dbcb005f3b94267d5ba",
            "d33b4c0b56ef4049b78b56d3813712ad",
            "35fdc48ab9f844d896c8edf3aeecb852",
            "c2b8c5a8a6c34f18a68feffcd98618a9",
            "c7c69a3466f84c2b849da2d0ea6f7a34",
            "19fc85121535461bbad415d84255b378",
            "6773aa3872f44e289c794708b3623c6a"
          ]
        },
        "id": "hiVZj7lXiFEM",
        "outputId": "db84c609-6f73-4b31-990b-1075611fddb4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ed475fed8cf4dd89320579adb59d8de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the weights of TFBertModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "bert_model = TFBertModel.from_pretrained('bert-base-cased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "z6KLEYLni17t"
      },
      "outputs": [],
      "source": [
        "# input layers\n",
        "input_ids = tf.keras.layers.Input(shape=(256,), name='input_ids', dtype='int32')\n",
        "attention_masks = tf.keras.layers.Input(shape=(256,), name='attention_mask', dtype='int32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "SaiWFRpei_F0"
      },
      "outputs": [],
      "source": [
        "bert_embds = bert_model.bert(input_ids, attention_mask=attention_masks)[1]\n",
        "intermediate_layer = tf.keras.layers.Dense(512, activation='relu', name='intermediate_layer')(bert_embds)\n",
        "output_layer = tf.keras.layers.Dense(6, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01), name='output_layer')(intermediate_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wCfi-Fekqw0",
        "outputId": "fbdb4d97-7b8a-4bcd-beea-66776540e1e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_ids (InputLayer)         [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " attention_mask (InputLayer)    [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " bert (TFBertMainLayer)         TFBaseModelOutputWi  108310272   ['input_ids[0][0]',              \n",
            "                                thPoolingAndCrossAt               'attention_mask[0][0]']         \n",
            "                                tentions(last_hidde                                               \n",
            "                                n_state=(None, 256,                                               \n",
            "                                 768),                                                            \n",
            "                                 pooler_output=(Non                                               \n",
            "                                e, 768),                                                          \n",
            "                                 past_key_values=No                                               \n",
            "                                ne, hidden_states=N                                               \n",
            "                                one, attentions=Non                                               \n",
            "                                e, cross_attentions                                               \n",
            "                                =None)                                                            \n",
            "                                                                                                  \n",
            " intermediate_layer (Dense)     (None, 512)          393728      ['bert[0][1]']                   \n",
            "                                                                                                  \n",
            " output_layer (Dense)           (None, 6)            3078        ['intermediate_layer[0][0]']     \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 108,707,078\n",
            "Trainable params: 108,707,078\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.Model(inputs=[input_ids, attention_masks], outputs=output_layer)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "lDJig6FulBaM"
      },
      "outputs": [],
      "source": [
        "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate=1e-5,\n",
        "    decay_steps=10000,\n",
        "    decay_rate=1e-6)\n",
        "optim = tf.keras.optimizers.Adam(learning_rate=lr_schedule)\n",
        "loss_func = tf.keras.losses.CategoricalCrossentropy()\n",
        "acc = tf.keras.metrics.CategoricalAccuracy('accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "siTQP9XZmP70"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=optim, loss=loss_func, metrics=[acc])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iC4DzK-smdaN",
        "outputId": "d8354b24-a942-4835-a98d-9fe1a44168ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1000/1000 [==============================] - 923s 869ms/step - loss: 0.6916 - accuracy: 0.7961 - val_loss: 0.3020 - val_accuracy: 0.9293\n",
            "Epoch 2/5\n",
            "1000/1000 [==============================] - 867s 867ms/step - loss: 0.3299 - accuracy: 0.9207 - val_loss: 0.2752 - val_accuracy: 0.9323\n",
            "Epoch 3/5\n",
            "1000/1000 [==============================] - 864s 864ms/step - loss: 0.2935 - accuracy: 0.9301 - val_loss: 0.2615 - val_accuracy: 0.9365\n",
            "Epoch 4/5\n",
            "1000/1000 [==============================] - 864s 863ms/step - loss: 0.2914 - accuracy: 0.9293 - val_loss: 0.2628 - val_accuracy: 0.9377\n",
            "Epoch 5/5\n",
            "1000/1000 [==============================] - 864s 863ms/step - loss: 0.2849 - accuracy: 0.9339 - val_loss: 0.2457 - val_accuracy: 0.9430\n"
          ]
        }
      ],
      "source": [
        "hist = model.fit(\n",
        "    train_dataset,\n",
        "    validation_data = val_dataset,\n",
        "    epochs = 5\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract training and validation metrics from history\n",
        "train_loss = hist.history['loss']\n",
        "val_loss = hist.history['val_loss']\n",
        "train_acc = hist.history['accuracy']\n",
        "val_acc = hist.history['val_accuracy']\n",
        "\n",
        "# Plot the learning curves\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(train_loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(train_acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "mBM-e0c46-hf",
        "outputId": "6aa283c4-513a-4ca3-abdb-4937eca32ac7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pm0jhqmEpO_1"
      },
      "source": [
        "## Saving the trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9pR0oPhnyuN",
        "outputId": "c83bbc6e-a2ac-4aa9-9c79-c365f1ac7854"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, pooler_layer_call_fn while saving (showing 5 of 420). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "model.save('/content/drive/MyDrive/Trained Models/bert_sentiment_classifier')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NU54ZBjgpWIp"
      },
      "source": [
        "## Loading the trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aN-PsvT0GtVR",
        "outputId": "89c2f947-7f67-412c-c406-3b510bf3a18d"
      },
      "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-4.29.2-py3-none-any.whl (7.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m108.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.15.1 tokenizers-0.13.3 transformers-4.29.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "1zyynx2EGRae"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from transformers import BertTokenizer\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "SzChSYX_otN4"
      },
      "outputs": [],
      "source": [
        "loaded_model = tf.keras.models.load_model('/content/drive/MyDrive/Trained Models/bert_sentiment_classifier')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "zZdBCX8LDFSP"
      },
      "outputs": [],
      "source": [
        "with open('/content/drive/MyDrive/Trained Models/onehot_encoder_for_BERT_multiclass_classification.pkl', 'rb') as f:\n",
        "    encoder = pickle.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "ZNbhUYdb-0R-"
      },
      "outputs": [],
      "source": [
        "def get_prediction(input_text):\n",
        "  tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "  # creating input ids and attention_masks of the input text\n",
        "  token = tokenizer.encode_plus(\n",
        "      input_text,\n",
        "      max_length=256,\n",
        "      truncation=True,\n",
        "      padding='max_length',\n",
        "      add_special_tokens=True,\n",
        "      return_tensors='tf'\n",
        "  )\n",
        "\n",
        "  tokenized_input_text = {\n",
        "      'input_ids': tf.cast(token.input_ids, tf.float64),\n",
        "      'attention_mask': tf.cast(token.attention_mask, tf.float64)\n",
        "  }\n",
        "\n",
        "  # will give us the probability distributions of each output class\n",
        "  prediction_probabilities = loaded_model.predict(tokenized_input_text)\n",
        "  label = np.zeros(6)\n",
        "  label[np.argmax(prediction_probabilities[0])] = 1.0\n",
        "  label = encoder.inverse_transform(label.reshape(1, -1))\n",
        "  return label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "DyJu6fVUE10Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "993f2eff-e628-4822-ad7f-8bd0bd1ed8b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I am feeling very happy.\n",
            "1/1 [==============================] - 3s 3s/step\n",
            "joy\n"
          ]
        }
      ],
      "source": [
        "print(get_prediction(input())[0][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sH3DBWnQFbIb"
      },
      "source": [
        "## Divide datset into 5 parts and generating prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "PpMrlzyeF1ec"
      },
      "outputs": [],
      "source": [
        "dataset = pd.read_csv('Emotion Dataset (English Text).csv', index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQ60RX8wFajz",
        "outputId": "495854ec-cf75-44ab-a906-5fb6592ff918"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 2)\n",
            "(100, 2)\n",
            "(100, 2)\n",
            "(100, 2)\n",
            "(100, 2)\n"
          ]
        }
      ],
      "source": [
        "data_1 = dataset.iloc[:100, :]\n",
        "data_2 = dataset.iloc[100:200, :]\n",
        "data_3 = dataset.iloc[200:300, :]\n",
        "data_4 = dataset.iloc[300:400, :]\n",
        "data_5 = dataset.iloc[400:500, :]\n",
        "\n",
        "print(data_1.shape)\n",
        "print(data_2.shape)\n",
        "print(data_3.shape)\n",
        "print(data_4.shape)\n",
        "print(data_5.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "BNrKyGfJHtdB",
        "outputId": "77b1e95f-1933-4f20-daca-1e31e264cb0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 79ms/step\n",
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            "1/1 [==============================] - 0s 71ms/step\n",
            "1/1 [==============================] - 0s 75ms/step\n"
          ]
        }
      ],
      "source": [
        "# generating predictions for data_1\n",
        "predictions = []\n",
        "for text in data_1['text']:\n",
        "  predictions.append(get_prediction(text)[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0scuPlN0JrCX",
        "outputId": "1648e09b-7aa3-4b88-ef45-7d78e814e2ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text    label predictions\n",
              "0                            i didnt feel humiliated  sadness     sadness\n",
              "1  i can go from feeling so hopeless to so damned...  sadness     sadness\n",
              "2   im grabbing a minute to post i feel greedy wrong    anger       anger\n",
              "3  i am ever feeling nostalgic about the fireplac...     love        love\n",
              "4                               i am feeling grouchy    anger       anger"
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "predictions = pd.DataFrame(predictions, columns=['predictions'])\n",
        "data_1 = pd.concat([data_1, predictions], axis=1)\n",
        "data_1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "yNe-eslpT4uX"
      },
      "outputs": [],
      "source": [
        "match_count = 0\n",
        "non_match_count = 0\n",
        "\n",
        "for label, predictions in zip(data_1['label'], data_1['predictions']):\n",
        "  if label!=predictions:\n",
        "    non_match_count += 1\n",
        "  else:\n",
        "    match_count += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_IDH2_fWCHu",
        "outputId": "d12bebb2-855d-46d6-9cce-25f6aa4d7a4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct predictions: 98\n",
            "Incorrect predictions: 2\n",
            "The accuracy: 0.98\n"
          ]
        }
      ],
      "source": [
        "print('Correct predictions: {}'.format(match_count))\n",
        "print('Incorrect predictions: {}'.format(non_match_count))\n",
        "print('The accuracy: {}'.format(match_count/len(data_1)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXT0yURfaHAH"
      },
      "source": [
        "## Evaluation parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "SZRDifwjaLN_"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, classification_report, precision_score, recall_score, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "bGvpvCgqcBxg"
      },
      "outputs": [],
      "source": [
        "y_test = data_1['label']\n",
        "y_pred = data_1['predictions']\n",
        "classes = dataset['label'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5q4-61a3bbkv",
        "outputId": "b3b45d54-f59f-4b20-9066-16bbf5c75178"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set performance for model\n",
            "Accuracy score :  0.98\n",
            "F1 score       :  0.9809558823529412\n",
            "Precision score:  0.9844444444444445\n",
            "Recall score   :  0.98\n"
          ]
        }
      ],
      "source": [
        "print('Test set performance for model')\n",
        "print('Accuracy score : ', accuracy_score(y_test, y_pred))\n",
        "print('F1 score       : ', f1_score(y_test, y_pred, average='weighted'))\n",
        "print('Precision score: ', precision_score(y_test, y_pred, average='weighted'))\n",
        "print('Recall score   : ', recall_score(y_test, y_pred, average='weighted'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "c4Yy3JLIcVqX",
        "outputId": "63440194-a400-4722-ffec-19fd416ebb76"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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0DWlrSuYZFREREVk89oQQERGZOQ7HEBERkSQ0sILGgMELQ9qaknlGRURERBaPPSFERERmTi0UUBswpGJIW1NiEUJERGTmOCeEiIiIJCEMfIuu4BNTiYiIiP6HPSFERERmTg0F1Aa8hM6QtqbEIoSIiMjMaYRh8zo0wojBGBGHY4iIiEhHQkICHn30Ubi4uKB+/fro378/0tPTdfbp0qULFAqFzvLaa6/pdR72hMhQn7irGPT6ZXh4VeJcqgM++fcjSE9xlDoso1CeuQn3bZegzL4Fm/wKXBgXhOI2btrt1gUVqPfN33A8fRNWJZW4FeqCKy/5ocJbKV3QJmTJ1/p+5Ji3HHMG5JO3xsCJqfq23bt3L0aPHo1HH30UlZWVeOedd9C9e3ekpqbCyclJu9/IkSMxa9Ys7WdHR/1+9uwJkZnOfW9g1IwLWDvfG6NjmuBcqhJzE8/B1bNC6tCMwqpMg3J/R1x+2b/6RiHgs+gcbC+X48KbQciZFYZKTzs8Mi8TijJ13QdrYpZ+re9FjnnLMWdAXnlroDB40UdSUhLi4uLQrFkzREZGYtWqVcjJycGxY8d09nN0dIS3t7d2UalUep2HRYjMDBh1FUmJHtix3gM5GUosftsPZbcUiBl8XerQjKIk0hXXBvmiuK1btW22l8rgkFWMy7H+KAtyQoWPEpdj/aEo18Dl8I26D9bELP1a34sc85ZjzoB88zZEYWGhzlJWVlajdgUFBQAADw8PnfVr165FvXr10Lx5c0ydOhUlJSV6xcMixAQqKsyzCrex1SCkRQmO73fRrhNCgRP7XRDeRr8vzsNIUVE1M0vY3vG1t1JA2CrgkFEkUVSmIddrLce85ZgzIL+8bz8x1ZAFAPz9/eHq6qpdEhISHnhujUaDt956Cx06dEDz5s2161988UV89dVX+OWXXzB16lR8+eWXeOmll/TK66EuQpKSktCxY0e4ubnB09MTvXv3RlZWFgAgOzsbCoUCGzduRNeuXeHo6IjIyEgcPnxY5xgrVqyAv78/HB0d8cwzz2D+/Plwc3PT2ef7779H69atoVQqERQUhPj4eFRWVmq3KxQKLF26FH379oWTkxPmzp1r8txrQ+WhhrUNkH9FdyrQjas2cPeqvEcry1Huo0SFpx08v/0bVsWVQKUG7j9ehO31Cljnm2fhWFtyvdZyzFuOOQPyy/v2nBBDFgDIzc1FQUGBdpk6deoDzz169GicPn0a69at01k/atQoxMTEICIiAkOGDMGaNWuwadMm7e/hmnioi5Di4mJMmDABv/32G3bt2gUrKys888wz0Gg02n2mTZuGiRMnIiUlBU2aNMHgwYO1BcTBgwfx2muv4c0330RKSgq6detWrYDYv38/hg4dijfffBOpqan49NNPsWrVqmr7zZw5E8888wx+//13DBs2rFqsZWVl1brBqI7ZKJA3Ngh2l8rQ+I1TCB6ZAoe0IhS3UAEK87yHnojImFQqlc5ib29/3/3HjBmDrVu34pdffoGfn999933ssccAAJmZmTWO56G+O2bgwIE6n7/44gt4eXkhNTUVzs7OAICJEyfi6aefBgDEx8ejWbNmyMzMRNOmTfHxxx+jZ8+emDhxIgCgSZMmOHToELZu3ao9Znx8PKZMmYLY2FgAQFBQEGbPno3JkydjxowZ2v1efPFFvPLKK/eMNSEhAfHx8cZJvJYKr1tDXQm4/eOvBPd6lbhx5aH+KtRYWaAjcmaHwapEDUWlBmqVLfzjz6A00LJm08v1WssxbznmDMgvbw0MfHeMnhNThRAYO3YsNm3ahD179iAwMPCBbVJSUgAAPj4+NT7PQ90TkpGRgcGDByMoKAgqlQqNGjUCAOTk5Gj3adGihfbft38wly9fBgCkp6ejXbt2Osf85+eTJ09i1qxZcHZ21i4jR45EXl6ezgSctm3b3jfWqVOn6nSB5ebm6p+wgSorrJBxyhGtOt7UrlMoBFp2LELqMcv6JfwgGkdrqFW2sL1YCvvzJShu5SZ1SEYl12stx7zlmDMgv7yFgXfGCD2LkNGjR+Orr75CYmIiXFxccPHiRVy8eBG3bt0CAGRlZWH27Nk4duwYsrOzsWXLFgwdOhSdOnXS+b37IA91udinTx8EBARgxYoV8PX1hUajQfPmzVFeXq7dx9bWVvtvxX+73O8crnmQoqIixMfHY8CAAdW2KZX/e7bEnfdN3429vf0Du73qwsbl9TBxYS7OnnRE+glHPDPyCpSOGuxY5/Hgxg8BRakatpf+N9vb9koZ7P4sgcbZBpWednD+9QbULjao8LSD/V+34LX2LxS3cUNJhH63lT0MLP1a34sc85ZjzoC88q7rt+guXboUQNUDye60cuVKxMXFwc7ODj///DMWLlyI4uJi+Pv7Y+DAgfj3v/+t13ke2iLk2rVrSE9Px4oVK/DEE08AAA4cOKDXMUJDQ3H06FGddf/83Lp1a6SnpyM4ONiwgM3E3i3ucPVUY+iki3D3qsS5PxwwbUgg8q/aPrjxQ0B5vgR+72doP3t9/TcAoLCjBy6NbATr/ArU+/ov2BRUotLNFoUdPHC9n7dU4ZqUpV/re5Fj3nLMGZBv3nVBiPs/593f3x979+41+DwPbRHi7u4OT09PLF++HD4+PsjJycGUKVP0OsbYsWPRqVMnzJ8/H3369MHu3buxbds2bY8JAEyfPh29e/dGw4YNMWjQIFhZWeHkyZM4ffo05syZY+y06sSWlfWwZWU9qcMwiVthLshY3fqe2wu610dB9/p1GJG0LPla348c85ZjzoB88q7rJ6bWFfOMqgasrKywbt06HDt2DM2bN8f48ePxwQcf6HWMDh06YNmyZZg/fz4iIyORlJSE8ePH6wyzxMTEYOvWrdixYwceffRRPP7441iwYAECAgKMnRIREdFd3R6OMWQxRw9tTwgAREdHIzU1VWfdnV1I/+xOcnNzq7Zu5MiRGDlypM7nfw69xMTEICYm5p5xPKjbioiIiKp7qIsQY/jwww/RrVs3ODk5Ydu2bVi9ejU++eQTqcMiIiLSqs37X/7Z3hzJvgj59ddfMW/ePNy8eRNBQUFYvHgxRowYIXVYREREWnV9d0xdkX0R8s0330gdAhERkSzJvgghIiIyd+wJISIiIklYahHy0N6iS0RERA839oQQERGZOUvtCWERQkREZOYEDLvN1lyfZsUihIiIyMxZak8I54QQERGRJNgTQkREZOYstSeERQgREZGZs9QihMMxREREJAn2hBAREZk5S+0JYRFCRERk5oRQQBhQSBjS1pQ4HENERESSYE8IERGRmdNAYdDDygxpa0osQoiIiMycpc4J4XAMERERSYI9IURERGbOUiemsgghIiIyc5Y6HMMihIiIyMxZak8I54QQERGRJNgTQnUuJPa41CHUuYrubaUOQRK2O36TOgQiiyAMHI4x154QFiFERERmTgAQwrD25ojDMURERCQJ9oQQERGZOQ0UUPCJqURERFTXeHcMERERkRGxJ4SIiMjMaYQCCj6sjIiIiOqaEAbeHWOmt8dwOIaIiIgkwZ4QIiIiM2epE1NZhBAREZk5FiFEREQkCUudmMo5IURERCQJ9oQQERGZOUu9O4ZFCBERkZmrKkIMmRNixGCMiMMxREREJAn2hBAREZk53h1DREREkhD/XQxpb444HENERESSYBFCRERk5m4Pxxiy6CMhIQGPPvooXFxcUL9+ffTv3x/p6ek6+5SWlmL06NHw9PSEs7MzBg4ciEuXLul1HhYhRERE5k4YYdHD3r17MXr0aBw5cgQ7d+5ERUUFunfvjuLiYu0+48ePxw8//IBvv/0We/fuxYULFzBgwAC9zsM5IURERObOwImp0LNtUlKSzudVq1ahfv36OHbsGDp16oSCggJ8/vnnSExMxJNPPgkAWLlyJcLCwnDkyBE8/vjjNToPe0KIiIhkorCwUGcpKyurUbuCggIAgIeHBwDg2LFjqKioQHR0tHafpk2bomHDhjh8+HCN42ERQkREZOZuPzHVkAUA/P394erqql0SEhIeeG6NRoO33noLHTp0QPPmzQEAFy9ehJ2dHdzc3HT2bdCgAS5evFjjvDgcQ0REZOaM9ZyQ3NxcqFQq7Xp7e/sHth09ejROnz6NAwcO1Pr898IihIiISCZUKpVOEfIgY8aMwdatW7Fv3z74+flp13t7e6O8vBz5+fk6vSGXLl2Ct7d3jY/PIkSG+sRdxaDXL8PDqxLnUh3wyb8fQXqKo9RhmZTcck78cD286xVVW795VxgWf9legojqltyuNyDPnAEZ5S0Uek8urdZen92FwNixY7Fp0ybs2bMHgYGBOtvbtGkDW1tb7Nq1CwMHDgQApKenIycnB1FRUTU+j2zmhHTp0gVvvfWW1GFIrnPfGxg14wLWzvfG6JgmOJeqxNzEc3D1rJA6NJORY86vx/fFwDcHa5eJ83oAAPYeDXxAy4efHK+3HHMG5JW3seaE1NTo0aPx1VdfITExES4uLrh48SIuXryIW7duAQBcXV0xfPhwTJgwAb/88guOHTuGV155BVFRUTW+MwaQURFCVQaMuoqkRA/sWO+BnAwlFr/th7JbCsQMvi51aCYjx5wLbjrgRoGjdolqmYu/L7ng5Jmad5M+rOR4veWYMyDfvOvC0qVLUVBQgC5dusDHx0e7rF+/XrvPggUL0Lt3bwwcOBCdOnWCt7c3Nm7cqNd5WITIiI2tBiEtSnB8v4t2nRAKnNjvgvA2JRJGZjpyzPmfbKzViI7KxLb9TQCY50usjEWO11uOOQMyzLuOH1YmhLjrEhcXp91HqVRiyZIluH79OoqLi7Fx40a95oMAMi1Cbty4gaFDh8Ld3R2Ojo7o2bMnMjIyAFTdQ+3g4IBt27bptNm0aRNcXFxQUlL15c7NzcVzzz0HNzc3eHh4oF+/fsjOzr7nOcvKyqrdn13XVB5qWNsA+Vd0pwLduGoDd6/KOo+nLsgx53/q0PpPODuWY/uBEKlDMTk5Xm855gzIL++6fmx7XanRxNQtW7bU+IB9+/atdTB1JS4uDhkZGdiyZQtUKhXefvtt9OrVC6mpqVCpVOjduzcSExPRs2dPbZu1a9eif//+cHR0REVFBWJiYhAVFYX9+/fDxsYGc+bMQY8ePXDq1CnY2dlVO2dCQgLi4+PrMk0iAECvTmfx6+9+uJbvJHUoREQ6alSE9O/fv0YHUygUUKvVhsRjcreLj4MHD6J9+6q7BNauXQt/f39s3rwZzz77LIYMGYKXX34ZJSUlcHR0RGFhIX788Uds2rQJALB+/XpoNBp89tlnUCiqqsuVK1fCzc0Ne/bsQffu3audd+rUqZgwYYL2c2FhIfz9/esg4/8pvG4NdSXg9o+/EtzrVeLGFcu8UUqOOd+pgedNtG52ATM+fkrqUOqEHK+3HHMGZJq3nkMqD4MaDcdoNJoaLeZegABAWloabGxs8Nhjj2nXeXp6IjQ0FGlpaQCAXr16wdbWVtsD9N1330GlUmkfT3vy5ElkZmbCxcUFzs7OcHZ2hoeHB0pLS5GVlXXX89rb22vvz9b3Pm1jqaywQsYpR7TqeFO7TqEQaNmxCKnHLPCWNsgz5zv1eCID+YVKHDlZtwWvVOR4veWYMyC/vGU9HHMvpaWlUCqVxorFbNjZ2WHQoEFITEzECy+8gMTERDz//POwsan6cRUVFaFNmzZYu3ZttbZeXl51Ha5eNi6vh4kLc3H2pCPSTzjimZFXoHTUYMc6D6lDMxk55gxU/Qe5R8ez2HEwBBqNfKZ/yfF6yzFnQGZ512JyabX2ZkjvIkStVuO9997DsmXLcOnSJZw9exZBQUF499130ahRIwwfPtwUcRpNWFgYKisrkZycrB2OuXbtGtLT0xEeHq7db8iQIejWrRv++OMP7N69G3PmzNFua926NdavX4/69etL0qNhiL1b3OHqqcbQSRfh7lWJc384YNqQQORftZU6NJORY84A0Cb8bzSoV4xt+5pIHUqdkuP1lmPOgHzztiQKIfR7hMmsWbOwevVqzJo1CyNHjsTp06cRFBSE9evXY+HChXq9Pa8udenSBS1btsTChQvRv39/ZGRk4NNPP4WLiwumTJmCzMxMpKamwta26ssrhEBAQAA8PDxQVFSEzMxM7bFKSkrQsmVLPPLII5g1axb8/Pzw559/YuPGjZg8ebLOo23vpbCwEK6uruiCfrBR8P8wlq6ie1upQ5CE7Y7fpA6ByGQqRQX24HsUFBSY7A/S278r/JfNhJVD7UceNLdKkfvaTJPGWht699GuWbMGy5cvx5AhQ2Btba1dHxkZiTNnzhg1OFNZuXIl2rRpg969eyMqKgpCCPz000/aAgSommQ7ePBgnDx5EkOGDNFp7+joiH379qFhw4YYMGAAwsLCMHz4cJSWlprVxSUiIgtRx88JqSt6D8f8/fffCA4OrrZeo9GgosJ8H5W7Z88e7b/d3d2xZs2aB7b5z3/+g//85z933ebt7Y3Vq1cbKzwiIiLZ0bsnJDw8HPv376+2fsOGDWjVqpVRgiIiIqI7sCekyvTp0xEbG4u///4bGo0GGzduRHp6OtasWYOtW7eaIkYiIiJ5q+O36NYVvXtC+vXrhx9++AE///wznJycMH36dKSlpeGHH35At27dTBEjERERWaBaPSfkiSeewM6dO40dCxEREd2FEFWLIe3NUa0fVvbbb79pnzAaHh6ONm3aGC0oIiIiugMfVlblr7/+wuDBg3Hw4EG4ubkBAPLz89G+fXusW7euRs/IICIiItJ7TsiIESNQUVGBtLQ0XL9+HdevX0daWho0Gg1GjBhhihiJiIjk7fbEVEMWM6R3T8jevXtx6NAhhIaGateFhobi448/xhNPPGHU4IiIiAhQiKrFkPbmSO8ixN/f/64PJVOr1fD19TVKUERERHQHC50TovdwzAcffICxY8fit9/+906I3377DW+++SY+/PBDowZHRERElqtGPSHu7u5QKP43nlRcXIzHHntM+2r7yspK2NjYYNiwYejfv79JAiUiIpItC31YWY2KkIULF5o4DCIiIronCx2OqVEREhsba+o4iIiISGZq/bAyACgtLUV5ebnOOr7KnoiIyMgstCdE74mpxcXFGDNmDOrXrw8nJye4u7vrLERERGRkFvoWXb2LkMmTJ2P37t1YunQp7O3t8dlnnyE+Ph6+vr5Ys2aNKWIkIiIiC6T3cMwPP/yANWvWoEuXLnjllVfwxBNPIDg4GAEBAVi7di2GDBliijiJiIjky0LvjtG7J+T69esICgoCUDX/4/r16wCAjh07Yt++fcaNjoiIiLRPTDVkMUd6FyFBQUE4f/48AKBp06b45ptvAFT1kNx+oR0RERHRg+hdhLzyyis4efIkAGDKlClYsmQJlEolxo8fj0mTJhk9QCIiItmz0Impes8JGT9+vPbf0dHROHPmDI4dO4bg4GC0aNHCqMERERGR5TLoOSEAEBAQgICAAGPEQkRERHehgIFv0TVaJMZVoyJk8eLFNT7guHHjah0MERERyUeNipAFCxbU6GAKhYJFCNFd2O747cE7WSDr8CZShyAJdepZqUMgS2Oht+jWqAi5fTcMERERSYCPbSciIiIyHoMnphIREZGJWWhPCIsQIiIiM2foU08t5ompRERERMbAnhAiIiJzZ6HDMbXqCdm/fz9eeuklREVF4e+//wYAfPnllzhw4IBRgyMiIiJY7GPb9S5CvvvuO8TExMDBwQEnTpxAWVkZAKCgoADvvfee0QMkIiIiy6R3ETJnzhwsW7YMK1asgK2trXZ9hw4dcPz4caMGR0RERP+bmGrIYo70nhOSnp6OTp06VVvv6uqK/Px8Y8REREREd7LQJ6bq3RPi7e2NzMzMausPHDiAoKAgowRFREREd+CckCojR47Em2++ieTkZCgUCly4cAFr167FxIkT8frrr5siRiIiIrJAehchU6ZMwYsvvoinnnoKRUVF6NSpE0aMGIFXX30VY8eONUWMREREsibFnJB9+/ahT58+8PX1hUKhwObNm3W2x8XFQaFQ6Cw9evTQ6xx6zwlRKBSYNm0aJk2ahMzMTBQVFSE8PBzOzs76HoqIiIhqQoLnhBQXFyMyMhLDhg3DgAED7rpPjx49sHLlSu1ne3t7vc5R64eV2dnZITw8vLbNiYiIyIz17NkTPXv2vO8+9vb28Pb2rvU59C5CunbtCoXi3rNsd+/eXetgiIiI6C4Mvc32v20LCwt1Vtvb2+vde3GnPXv2oH79+nB3d8eTTz6JOXPmwNPTs8bt9S5CWrZsqfO5oqICKSkpOH36NGJjY/U9HBERET2IkYZj/P39dVbPmDEDM2fOrNUhe/TogQEDBiAwMBBZWVl455130LNnTxw+fBjW1tY1OobeRciCBQvuun7mzJkoKirS93BERERUR3Jzc6FSqbSfDekFeeGFF7T/joiIQIsWLdC4cWPs2bMHTz31VI2OYbS36L700kv44osvjHU4IiIius1IzwlRqVQ6iyFFyD8FBQWhXr16d32W2L0Y7S26hw8fhlKpNNbhiIiI6L8MffR6XTy2/a+//sK1a9fg4+NT4zZ6FyH/vE1HCIG8vDz89ttvePfdd/U9HBEREZmhoqIinV6N8+fPIyUlBR4eHvDw8EB8fDwGDhwIb29vZGVlYfLkyQgODkZMTEyNz6F3EeLq6qrz2crKCqGhoZg1axa6d++u7+GIiIjIDP3222/o2rWr9vOECRMAALGxsVi6dClOnTqF1atXIz8/H76+vujevTtmz56t1xCPXkWIWq3GK6+8goiICLi7u+vTlIiIiGpLgoeVdenSBULcu+H27dsNCKiKXhNTra2t0b17d74tl4iIqA5J8dj2uqD33THNmzfHuXPnTBELERERyYjeRcicOXMwceJEbN26FXl5eSgsLNRZHmYzZ86s9jA2S9Qn7ipWJ6fih3OnsGhrBkJblkgdksnJMWdAvnkDwLMvnMFPP3+LUa+nSB1KnZDrtZZV3gbenmuOalyEzJo1C8XFxejVqxdOnjyJvn37ws/PD+7u7nB3d4ebm9tDP09k4sSJ2LVrl9RhmFTnvjcwasYFrJ3vjdExTXAuVYm5iefg6lkhdWgmI8ecAfnmDQAhodfR8+ksnMtyffDOFkCu11pWeRvpOSHmpsZFSHx8PIqLi/HLL79ol927d2uX25+lVF5eXqt2QghUVlbC2dlZr2feP4wGjLqKpEQP7FjvgZwMJRa/7YeyWwrEDL4udWgmI8ecAfnmrVRWYvLUZCxe0BZFRXZSh1Mn5Hqt5Zq3JalxEXJ7hmznzp3vu+hrw4YNiIiIgIODAzw9PREdHY3i4mJ06dIFb731ls6+/fv3R1xcnPZzo0aNMHv2bAwdOhQqlQqjRo1CdnY2FAoF1q1bh/bt20OpVKJ58+bYu3evtt2ePXugUCiwbds2tGnTBvb29jhw4EC14Zg9e/agXbt2cHJygpubGzp06IA///xTu/37779H69atoVQqERQUhPj4eFRWVur9M6grNrYahLQowfH9Ltp1QihwYr8LwttYZhemHHMG5Js3ALwx7jh+TfZByvEGUodSJ+R6reWWNyemAvd9e25t5OXlYfDgwRg2bBjS0tKwZ88eDBgw4L63BP3Thx9+iMjISJw4cULnYWmTJk3Cv/71L5w4cQJRUVHo06cPrl27ptN2ypQpeP/995GWloYWLVrobKusrET//v3RuXNnnDp1CocPH8aoUaO0P4P9+/dj6NChePPNN5GamopPP/0Uq1atwty5c+8aZ1lZmeTzZ1QealjbAPlXdO/MvnHVBu5e5ls8GUKOOQPyzbtTlxwEh9zAqs8ipA6lzsj1WssubwsdjtHrOSFNmjR5YCFy/XrNu8Hy8vJQWVmJAQMGICAgAEDVS3D08eSTT+Jf//qX9nN2djYAYMyYMRg4cCAAYOnSpUhKSsLnn3+OyZMna/edNWsWunXrdtfjFhYWoqCgAL1790bjxo0BAGFhYdrt8fHxmDJlivbNwUFBQZg9ezYmT56MGTNmVDteQkIC4uPj9cqNiGqunlcJXh2dgmmTO6GiomZv8CQiaelVhMTHx1d7YqohIiMj8dRTTyEiIgIxMTHo3r07Bg0apNcE17Zt2951fVRUlPbfNjY2aNu2LdLS0mrUFgA8PDwQFxeHmJgYdOvWDdHR0Xjuuee0z8Q/efIkDh48qNPzoVarUVpaipKSEjg6Ouocb+rUqdqnzQFVRc4/X6lsaoXXraGuBNz+8VeCe71K3LhitNcImRU55gzIM++QkBtwdy/Dx8t+1q6zthZoHnEFffpnol/PgdBojNubaw7keK0B+eX9MLw7pjb0ulIvvPAC6tevb7STW1tbY+fOnTh06BB27NiBjz/+GNOmTUNycjKsrKyqDctUVFSf8ezk5FTr8z+o7cqVKzFu3DgkJSVh/fr1+Pe//42dO3fi8ccfR1FREeLj46u9SwfAXV/kZ29vb9S3FdZGZYUVMk45olXHmzicVFVMKhQCLTsWYcsqy5yQK8ecAXnmnXKiPl4fofvqiPGTjuKvHBd8u76pRRYggDyvNSDDvCV4YmpdqPGcEGPPB7nzuB06dEB8fDxOnDgBOzs7bNq0CV5eXsjLy9Pup1arcfr06Rof98iRI9p/V1ZW4tixYzrDKTXVqlUrTJ06FYcOHULz5s2RmJgIAGjdujXS09MRHBxcbbGy0vvxK3Vm4/J66PnidUQ/ex3+waUY+/5fUDpqsGOdh9ShmYwccwbkl/etW7b4M9tVZykttUFhoT3+zLbsW3Xldq1vk2velqTGPSH6TBatqeTkZOzatQvdu3dH/fr1kZycjCtXriAsLAxOTk6YMGECfvzxRzRu3Bjz58/X63HxS5YsQUhICMLCwrBgwQLcuHEDw4YNq3H78+fPY/ny5ejbty98fX2Rnp6OjIwMDB06FAAwffp09O7dGw0bNsSgQYNgZWWFkydP4vTp05gzZ46+P4o6s3eLO1w91Rg66SLcvSpx7g8HTBsSiPyrtlKHZjJyzBmQb95yJNdrLau8LbQnpMZFiEajMfrJVSoV9u3bh4ULF6KwsBABAQH46KOP0LNnT1RUVODkyZMYOnQobGxsMH78eJ23+T3I+++/j/fffx8pKSkIDg7Gli1bUK9evRq3d3R0xJkzZ7B69Wpcu3YNPj4+GD16NF599VUAQExMDLZu3YpZs2bhP//5D2xtbdG0aVOMGDFC759DXduysh62rKz5z8ISyDFnQL553zblX12kDqHOyPVayyVvS50TohCm6OKQUHZ2NgIDA3HixAmzfgR7YWEhXF1d0QX9YKOwwKqdCIB1eBOpQ5CEOvWs1CFQHagUFdiD71FQUACVSmWSc9z+XRH61nuwtq8+37Cm1GWlSF/4jkljrQ3znbxAREREFs3y7mMiIiKyNHKfE/KwaNSokUkm0RIREUnFUueEcDiGiIiIJGFxPSFEREQWh8MxREREJAUOxxAREREZEXtCiIiIzB2HY4iIiEgSFlqEcDiGiIiIJMGeECIiIjOn+O9iSHtzxCKEiIjI3FnocAyLECIiIjPHW3SJiIiIjIg9IUREROaOwzFEREQkGTMtJAzB4RgiIiKSBHtCiIiIzJylTkxlEUJERGTuLHROCIdjiIiISBLsCSEiIjJzHI4hIiIiaXA4hoiIiMh42BNCRERk5jgcQ0SkJ3XqWalDkMStfu2kDqHOOXz/q9QhWDYLHY5hEUJERGTuLLQI4ZwQIiIikgR7QoiIiMwc54QQERGRNDgcQ0RERGQ87AkhIiIycwohoBC1784wpK0psSeEiIjI3AkjLHrat28f+vTpA19fXygUCmzevFk3JCEwffp0+Pj4wMHBAdHR0cjIyNDrHCxCiIiIqJri4mJERkZiyZIld90+b948LF68GMuWLUNycjKcnJwQExOD0tLSGp+DwzFERERmToq7Y3r27ImePXvedZsQAgsXLsS///1v9OvXDwCwZs0aNGjQAJs3b8YLL7xQo3OwJ4SIiMjcGWk4prCwUGcpKyurVTjnz5/HxYsXER0drV3n6uqKxx57DIcPH67xcViEEBERyYS/vz9cXV21S0JCQq2Oc/HiRQBAgwYNdNY3aNBAu60mOBxDRERk5ow1HJObmwuVSqVdb29vb2BkhmFPCBERkbkz0nCMSqXSWWpbhHh7ewMALl26pLP+0qVL2m01wSKEiIjIzN3uCTFkMabAwEB4e3tj165d2nWFhYVITk5GVFRUjY/D4RgiIiKqpqioCJmZmdrP58+fR0pKCjw8PNCwYUO89dZbmDNnDkJCQhAYGIh3330Xvr6+6N+/f43PwSKEiIjI3Enw7pjffvsNXbt21X6eMGECACA2NharVq3C5MmTUVxcjFGjRiE/Px8dO3ZEUlISlEpljc/BIoSIiOghUNdvwu3SpQvEfR73rlAoMGvWLMyaNavW5+CcECIiIpIEe0KIiIjMnRBViyHtzRCLECIiIjMnxWPb6wKHY4iIiEgS7AkhIiIydxLcHVMXWIQQERGZOYWmajGkvTnicAwRERFJgj0hMtQn7ioGvX4ZHl6VOJfqgE/+/QjSUxylDsuk5JgzwLzlkLeVQoNXeh9H93YZ8FDdwtUCR2w73ARrtrUCoJA6PJOTzbW20OEYWfWECCEwatQoeHh4QKFQICUlReqQ6lznvjcwasYFrJ3vjdExTXAuVYm5iefg6lkhdWgmI8ecAeYtl7xfjDmJfp1SsWB9B7wc/yyWbWqHF7ufwsCuf0gdmsnJ6Vqb27tjjEVWRUhSUhJWrVqFrVu3Ii8vD82bN5c6pDo3YNRVJCV6YMd6D+RkKLH4bT+U3VIgZvB1qUMzGTnmDDBvueTdPOgSDp4MwJHTDXHxugv2ngjC0bRHEBZwRerQTE5W1/r2c0IMWcyQrIqQrKws+Pj4oH379vD29oaNjfFHo8rLy41+TGOxsdUgpEUJju930a4TQoET+10Q3qZEwshMR445A8xbTnmfPtcArZtegF/9fABA40euIaLxJST/4S9tYCYmx2ttiWRThMTFxWHs2LHIycmBQqFAo0aNoNFokJCQgMDAQDg4OCAyMhIbNmzQtlGr1Rg+fLh2e2hoKBYtWlTtuP3798fcuXPh6+uL0NDQu56/rKwMhYWFOktdU3moYW0D5F/RLb5uXLWBu1dlncdTF+SYM8C85ZT32u0tsfu3xvhqxrfY/X+f4fN3NuLb3c2x82iw1KGZlNyutaUOx8hmYuqiRYvQuHFjLF++HEePHoW1tTUSEhLw1VdfYdmyZQgJCcG+ffvw0ksvwcvLC507d4ZGo4Gfnx++/fZbeHp64tChQxg1ahR8fHzw3HPPaY+9a9cuqFQq7Ny5857nT0hIQHx8fF2kSkQy0rXNOXR7NBOzVj6J7AvuCPa7hrHPHsa1AkckHWkidXhkLBY6MVU2RYirqytcXFxgbW0Nb29vlJWV4b333sPPP/+MqKgoAEBQUBAOHDiATz/9FJ07d4atra1O4RAYGIjDhw/jm2++0SlCnJyc8Nlnn8HOzu6e5586dar2NcgAUFhYCH//uu0uLbxuDXUl4PaPvxLc61XixhXL/CrIMWeAecsp7zeeScbaHZHY/VtjAMC5Cx7w9ryJITEpFl2EyPFaWyLZDMf8U2ZmJkpKStCtWzc4OztrlzVr1iArK0u735IlS9CmTRt4eXnB2dkZy5cvR05Ojs6xIiIi7luAAIC9vT1UKpXOUtcqK6yQccoRrTre1K5TKARadixC6jELvKUN8swZYN5yytverhIaoXsrrlpjBStz7X83Erldaw7HWJiioiIAwI8//ohHHnlEZ5u9vT0AYN26dZg4cSI++ugjREVFwcXFBR988AGSk5N19ndycqqboI1g4/J6mLgwF2dPOiL9hCOeGXkFSkcNdqzzkDo0k5FjzgDzlkveh35viJd7pODSdWdkX3BHiP9VPP/U7/jpkOX2gtwmq2vNt+halvDwcNjb2yMnJwedO3e+6z4HDx5E+/bt8cYbb2jX3dlL8jDau8Udrp5qDJ10Ee5elTj3hwOmDQlE/lVbqUMzGTnmDDBvueS9cH17jOh7DBNeOAh3l6qHlW050BSrfmwtdWgmJ7drbYlkW4S4uLhg4sSJGD9+PDQaDTp27IiCggIcPHgQKpUKsbGxCAkJwZo1a7B9+3YEBgbiyy+/xNGjRxEYGCh1+AbZsrIetqysJ3UYdUqOOQPMWw5uldnh42+j8PG3UVKHIgm5XGtDh1Q4HGOGZs+eDS8vLyQkJODcuXNwc3ND69at8c477wAAXn31VZw4cQLPP/88FAoFBg8ejDfeeAPbtm2TOHIiIpIVC707RiGEmQ4UWbjCwkK4urqiC/rBRsGuQyJLcqtfO6lDqHMO3/8qdQh1rlJUYA++R0FBgcluNrj9uyKqxyzY2CprfZzKilIcTppu0lhrQ9Y9IURERA8DDscQERGRNDSiajGkvRliEUJERGTuLHROiGwfVkZERETSYk8IERGRmVPAwDkhRovEuFiEEBERmTsLfWIqh2OIiIhIEuwJISIiMnO8RZeIiIikwbtjiIiIiIyHPSFERERmTiEEFAZMLjWkrSmxCCEiIjJ3mv8uhrQ3QxyOISIiIkmwJ4SIiMjMcTiGiIiIpGGhd8ewCCEiIjJ3fGIqERERkfGwJ4SIiMjM8YmpREREJA0OxxAREREZD3tCiIiIzJxCU7UY0t4csQghIiIydxyOISIiIjIe9oQQ1QFrLy+pQ5CE+soVqUOQhMP3v0odQp3bfiFF6hDqXOFNDdyb1NHJ+LAyIiIikoKlPradwzFERESkY+bMmVAoFDpL06ZNjX4e9oQQERGZOwkmpjZr1gw///yz9rONjfFLBhYhRERE5k4AMOQ221rULzY2NvD29jbgpA/G4RgiIiIzd3tOiCELABQWFuosZWVl9zxnRkYGfH19ERQUhCFDhiAnJ8foebEIISIikgl/f3+4urpql4SEhLvu99hjj2HVqlVISkrC0qVLcf78eTzxxBO4efOmUePhcAwREZG5EzBwTkjV/+Tm5kKlUmlX29vb33X3nj17av/dokULPPbYYwgICMA333yD4cOH1z6Of2ARQkREZO6MNDFVpVLpFCE15ebmhiZNmiAzM7P2MdwFh2OIiIjovoqKipCVlQUfHx+jHpdFCBERkbnTGGHRw8SJE7F3715kZ2fj0KFDeOaZZ2BtbY3BgwcbJ5//4nAMERGRmavrJ6b+9ddfGDx4MK5duwYvLy907NgRR44cgZeRX0HBIoSIiIh0rFu3rk7OwyKEiIjI3EnwxNS6wCKEiIjI3FloEcKJqURERCQJ9oQQERGZOwvtCWERQkREZO40ABQGtjdDLEKIiIjMXF3foltXOCeEiIiIJMGeECIiInPHOSFEREQkCY0AFAYUEhrzLEI4HENERESSYE8IERGRueNwDBEREUnDwCIE5lmEcDiGiIiIJMGekDvExcUhPz8fmzdvljoUk+oTdxWDXr8MD69KnEt1wCf/fgTpKY5Sh2VScsv5uWHn0f6pK/ALLEZ5mRXSUtzwxcJg/P2nk9Sh1Qm5XW/A8nP+YbUnflxTD5dy7QAAAaGlGDL+Ih598iYAYNFkP5zY74Jrl2zh4KhBWNtiDJ92AQ1DyqQM23gsdDiGPSF3WLRoEVatWiV1GCbVue8NjJpxAWvne2N0TBOcS1VibuI5uHpWSB2aycgx5+Zt87F1vR8mvPwopr3aGtY2GsxddgL2DmqpQzM5OV5vOeTs5VOBYe9cwP8lpePjbWcR2eEmZr4SiOx0JQAgpMUt/GtBDlbsPYO5iVmAAN4Z3BhqS/nKa4ThixliEXIHV1dXuLm5SR2GSQ0YdRVJiR7Ysd4DORlKLH7bD2W3FIgZfF3q0ExGjjlPf6MVft7ii5wsZ5w/64L505uhvm8pQsIKpQ7N5OR4veWQ8+PdC9HuqZt4JKgcfo3L8MqUi1A6aXDmWFVvT6+XriHi8WJ4+5cjpMUtxL6dhysX7LQ9J2SeWITcIS4uDv379wcAlJWVYdy4cahfvz6USiU6duyIo0ePAgCEEAgODsaHH36o0z4lJQUKhQKZmZl1HXqN2NhqENKiBMf3u2jXCaHAif0uCG9TImFkpiPHnO/GybkSAHCz0FbiSExLjtdbjjmr1cCezW4oK7FCWNviattLS6ywY70HvBuWwcvXQnqDhMbwxQyxCLmHyZMn47vvvsPq1atx/PhxBAcHIyYmBtevX4dCocCwYcOwcuVKnTYrV65Ep06dEBwcXO14ZWVlKCws1FnqmspDDWsbIP+K7lSgG1dt4O5VWefx1AU55vxPCoXAq5PP4o8Trvgz01nqcExKjtdbTjmfT1OiX3AEejeKxOIp/pj++XkENPnfnI8fVnmiX3AE+gW3wNHdKiSsy4KtnXkOQ+jt9pwQQxYzxCLkLoqLi7F06VJ88MEH6NmzJ8LDw7FixQo4ODjg888/B1DVa5Keno5ff/0VAFBRUYHExEQMGzbsrsdMSEiAq6urdvH396+zfEje3njnDAIaF+H9yRFSh0JkEL/GZfhkZzoW/3gWvYdexYdvBuDPs/ba7U8OuIFPdqTjw40Z8Asqw9xXG6G81JBXz5oRzgmRj6ysLFRUVKBDhw7adba2tmjXrh3S0tIAAL6+vnj66afxxRdfAAB++OEHlJWV4dlnn73rMadOnYqCggLtkpuba/pE/qHwujXUlYDbP/46cq9XiRtXLPNGKTnmfKfXp55Bu05XMWVkG1y7rJQ6HJOT4/WWU862dgKPBFbN+Rj2Th4Cw29h82de2u1OKg0eCSpHxOPF+PeKbORm2uPgNlcJI6YHYRFigBEjRmDdunW4desWVq5cieeffx6Ojne/Jc7e3h4qlUpnqWuVFVbIOOWIVh1vatcpFAItOxYh9Zjl3Mp3JznmXEXg9alnEPXkFUwd2QaX/naQOqA6IcfrLcecbxMCqCi/+68xIQAIxT23P3QsdDjGsspkI2ncuDHs7Oxw8OBBBAQEAKgabjl69Cjeeust7X69evWCk5MTli5diqSkJOzbt0+iiGtu4/J6mLgwF2dPOiL9hCOeGXkFSkcNdqzzkDo0k5Fjzm+8k44uPS9i1luRuFVsDXfPqnHz4iIblJdZSxydacnxessh5y/e88GjTxbC65EK3Cqywi+b3HHqkDPmJmYh70877N3ihjadb8LVoxJX8mzxzf81gJ2DBu2espA7wgQMfE6I0SIxKhYhd+Hk5ITXX38dkyZNgoeHBxo2bIh58+ahpKQEw4cP1+5nbW2NuLg4TJ06FSEhIYiKipIw6prZu8Udrp5qDJ10Ee5elTj3hwOmDQlE/lXLvWtCjjn3fv4vAMC8L47prJ//bjh+3uIrRUh1Ro7XWw4551+1wQfjAnD9sg0cXdQIDCvF3MQstOlchGsXbXA62RmbVnihqMAabvUqEfF4ERZ8nwG3epY1OdfSKIQw0z4aCdz5xNTS0lJMnjwZX3/9NW7evIm2bdtiwYIFePTRR3XanDt3Do0bN8a8efMwadKkGp+rsLAQrq6u6IJ+sFFYzn8o6O6svbwevJMFUl+5InUIVEe2X0iROoQ6V3hTA/cm51BQUGCyIfbbvyuivUfBxqr2zzyp1JTj54vLTRprbbAn5A5lZWVwdq66hVGpVGLx4sVYvHjxfdv8/fffsLW1xdChQ+siRCIikiONBoABz/rQ8DkhZquyshKpqak4fPgwmjVrVqM2ZWVl+OuvvzBz5kw8++yzaNCggYmjJCIisiwsQgCcPn0abdu2RbNmzfDaa6/VqM3XX3+NgIAA5OfnY968eSaOkIiIZI13x1iuli1boqREv8cbx8XFIS4uzjQBERER3Ylv0SUiIiIyHvaEEBERmTuNgEEP+zDTx7azCCEiIjJzQmggDHgTriFtTYlFCBERkbkTBr6EjnNCiIiIiP6HPSFERETmThg4J8RMe0JYhBAREZk7jQZQGDCvw0znhHA4hoiIiCTBnhAiIiJzx+EYIiIikoLQaCAMGI4x11t0ORxDREREkmBPCBERkbnjcAwRERFJQiMAheUVIRyOISIiIkmwJ4SIiMjcCQHAkOeEmGdPCIsQIiIiMyc0AsKA4RjBIoSIiIhqRWhgWE8Ib9ElIiKih8iSJUvQqFEjKJVKPPbYY/j111+NenwWIURERGZOaITBi77Wr1+PCRMmYMaMGTh+/DgiIyMRExODy5cvGy0vFiFERETmTmgMX/Q0f/58jBw5Eq+88grCw8OxbNkyODo64osvvjBaWpwTIpHbk4QqUWHQ82fo4SA05VKHIAm1qJA6BKojhTfNc86BKRUWVeVcF5M+Df1dUYmq/y8WFhbqrLe3t4e9vX21/cvLy3Hs2DFMnTpVu87KygrR0dE4fPhw7QP5BxYhErl58yYA4AB+kjgSqhNXpQ6AyLTcm0gdgXRu3rwJV1dXkxzbzs4O3t7eOHDR8N8Vzs7O8Pf311k3Y8YMzJw5s9q+V69ehVqtRoMGDXTWN2jQAGfOnDE4lttYhEjE19cXubm5cHFxgUKhqNNzFxYWwt/fH7m5uVCpVHV6binJMW855gwwbznlLWXOQgjcvHkTvr6+JjuHUqnE+fPnUV5ueG+qEKLa75u79YLUJRYhErGysoKfn5+kMahUKtn8h+pOcsxbjjkDzFtOpMrZVD0gd1IqlVAqlSY/z53q1asHa2trXLp0SWf9pUuX4O3tbbTzcGIqERER6bCzs0ObNm2wa9cu7TqNRoNdu3YhKirKaOdhTwgRERFVM2HCBMTGxqJt27Zo164dFi5ciOLiYrzyyitGOweLEBmyt7fHjBkzJB8LrGtyzFuOOQPMW055yzHnuvL888/jypUrmD59Oi5evIiWLVsiKSmp2mRVQyiEuT5QnoiIiCwa54QQERGRJFiEEBERkSRYhBAREZEkWIRYOIVCgc2bN0sdBhlJly5d8NZbb0kdhsWYOXMmWrZsKXUY9ySEwKhRo+Dh4QGFQoGUlBSpQ5JEXFwc+vfvL3UYZAK8O4aIZGvixIkYO3as1GHcU1JSElatWoU9e/YgKCgI9erVkzokSSxatKhO3s9CdY9FCFEtVVRUwNbWVuowZK28vBx2dnZ6txNCQK1Ww9nZGc7OziaIzDiysrLg4+OD9u3bm+wctf0Z1qW6eCopSYPDMWZmw4YNiIiIgIODAzw9PREdHY3i4mIcPXoU3bp1Q7169eDq6orOnTvj+PHjOm0zMjLQqVMnKJVKhIeHY+fOnTrbs7OzoVAosHHjRnTt2hWOjo6IjIys9kbEAwcO4IknnoCDgwP8/f0xbtw4FBcXa7d/8sknCAkJgVKpRIMGDTBo0KAHxm+IpKQkdOzYEW5ubvD09ETv3r2RlZWlV04rVqyAv78/HB0d8cwzz2D+/Plwc3PT2ef7779H69atoVQqERQUhPj4eFRWVmq3KxQKLF26FH379oWTkxPmzp1rUF6GunHjBoYOHQp3d3c4OjqiZ8+eyMjIAFD1Pg0HBwds27ZNp82mTZvg4uKCkpISAEBubi6ee+45uLm5wcPDA/369UN2drZJ477Xd+RuQ039+/dHXFyc9nOjRo0we/ZsDB06FCqVCqNGjdJ+B9atW4f27dtDqVSiefPm2Lt3r7bdnj17oFAosG3bNrRp0wb29vY4cOBAteGYPXv2oF27dnBycoKbmxs6dOiAP//8U7v9Qd8RY4qLi8PYsWORk5MDhUKBRo0aQaPRICEhAYGBgXBwcEBkZCQ2bNigbaNWqzF8+HDt9tDQUCxatKjacfv374+5c+fC19cXoaGhJonfmO4cjikrK8O4ceNQv359KJVKdOzYEUePHgVQVVwGBwfjww8/1GmfkpIChUKBzMzMug6dHkSQ2bhw4YKwsbER8+fPF+fPnxenTp0SS5YsETdv3hS7du0SX375pUhLSxOpqali+PDhokGDBqKwsFAIIYRarRbNmzcXTz31lEhJSRF79+4VrVq1EgDEpk2bhBBCnD9/XgAQTZs2FVu3bhXp6eli0KBBIiAgQFRUVAghhMjMzBROTk5iwYIF4uzZs+LgwYOiVatWIi4uTgghxNGjR4W1tbVITEwU2dnZ4vjx42LRokUPjN8QGzZsEN99953IyMgQJ06cEH369BERERFCrVbXKKcDBw4IKysr8cEHH4j09HSxZMkS4eHhIVxdXbXn2Ldvn1CpVGLVqlUiKytL7NixQzRq1EjMnDlTuw8AUb9+ffHFF1+IrKws8eeffxqUV2107txZvPnmm0IIIfr27SvCwsLEvn37REpKioiJiRHBwcGivLxcCCHEoEGDxEsvvaTTfuDAgdp15eXlIiwsTAwbNkycOnVKpKamihdffFGEhoaKsrIyk8R/v+/Inbnd1q9fPxEbG6v9HBAQIFQqlfjwww9FZmamyMzM1H4H/Pz8xIYNG0RqaqoYMWKEcHFxEVevXhVCCPHLL78IAKJFixZix44dIjMzU1y7dk3MmDFDREZGCiGEqKioEK6urmLixIkiMzNTpKamilWrVmmvc02+I8aUn58vZs2aJfz8/EReXp64fPmymDNnjmjatKlISkoSWVlZYuXKlcLe3l7s2bNHCFF1TadPny6OHj0qzp07J7766ivh6Ogo1q9frz1ubGyscHZ2Fi+//LI4ffq0OH36tEniN6bY2FjRr18/IYQQ48aNE76+vuKnn34Sf/zxh4iNjRXu7u7i2rVrQggh5s6dK8LDw3Xajxs3TnTq1Kmuw6YaYBFiRo4dOyYAiOzs7Afuq1arhYuLi/jhhx+EEEJs375d2NjYiL///lu7z7Zt2+5ahHz22Wfaff744w8BQKSlpQkhhBg+fLgYNWqUzrn2798vrKysxK1bt8R3330nVCqVtvipbfyGuHLligAgfv/99xrl9Pzzz4unn35a5xhDhgzRKUKeeuop8d577+ns8+WXXwofHx/tZwDirbfeMkFGNXf7F/XZs2cFAHHw4EHttqtXrwoHBwfxzTffCCGE2LRpk3B2dhbFxcVCCCEKCgqEUqkU27ZtE0JU5RcaGio0Go32GGVlZcLBwUFs377dJPHf7ztS0yKkf//+Ovvc/g68//772nUVFRXCz89P/Oc//xFC/K8I2bx5s07bO4uQa9euCQDaX+j/VJPviLEtWLBABAQECCGEKC0tFY6OjuLQoUM6+wwfPlwMHjz4nscYPXq0GDhwoPZzbGysaNCggckKTVO4XYQUFRUJW1tbsXbtWu228vJy4evrK+bNmyeEEOLvv/8W1tbWIjk5Wbu9Xr16YtWqVZLETvfH4RgzEhkZiaeeegoRERF49tlnsWLFCty4cQNA1ZsLR44ciZCQELi6ukKlUqGoqAg5OTkAgLS0NPj7++u8UvpeLxlq0aKF9t8+Pj4AgMuXLwMATp48iVWrVmnHyp2dnRETEwONRoPz58+jW7duCAgIQFBQEF5++WWsXbtW27V/v/gNkZGRgcGDByMoKAgqlQqNGjUCAG3uD8opPT0d7dq10znmPz+fPHkSs2bN0sl75MiRyMvL0+YHAG3btjU4H2NIS0uDjY0NHnvsMe06T09PhIaGIi0tDQDQq1cv2NraYsuWLQCA7777DiqVCtHR0QCqcs7MzISLi4s2Zw8PD5SWlmqHu4zNGN+Re12DO7/vNjY2aNu2rfZn8aC2AODh4YG4uDjExMSgT58+WLRoEfLy8rTba/odMZXMzEyUlJSgW7duOjGsWbNG53otWbIEbdq0gZeXF5ydnbF8+XKd/68AQEREhNnPA7mbrKwsVFRUoEOHDtp1tra2aNeunfZa+/r64umnn8YXX3wBAPjhhx9QVlaGZ599VpKY6f5YhJgRa2tr7Ny5E9u2bUN4eDg+/vhjhIaG4vz584iNjUVKSgoWLVqEQ4cOISUlBZ6enigvL9f7PHdOplQoFACq3o4IAEVFRXj11VeRkpKiXU6ePImMjAw0btwYLi4uOH78OL7++mv4+Phg+vTpiIyMRH5+/n3jN0SfPn1w/fp1rFixAsnJyUhOTgYAndzvl1NNFBUVIT4+Xifv33//HRkZGTqv0HZycjIol7pkZ2eHQYMGITExEQCQmJiI559/HjY2VfPRi4qK0KZNG52cU1JScPbsWbz44osmiel+3xErK6tqd0BUVFRUO4Yh1+BBbVeuXInDhw+jffv2WL9+PZo0aYIjR44AqPl3xFSKiooAAD/++KNODKmpqdp5IevWrcPEiRMxfPhw7NixAykpKXjllVeq/XfiYfoe18aIESOwbt063Lp1CytXrsTzzz8PR0dHqcOiu+DdMWZGoVCgQ4cO6NChA6ZPn46AgABs2rQJBw8exCeffIJevXoBqJpQePXqVW27sLAw5ObmIi8vT9sTcPs/nvpo3bo1UlNTERwcfM99bGxsEB0djejoaMyYMQNubm7YvXs3BgwYcM/4J0yYoHcsAHDt2jWkp6djxYoVeOKJJwBUTZzVR2hoqHbi2m3//Ny6dWukp6ffN29zEhYWhsrKSiQnJ2vvnLj9swoPD9fuN2TIEHTr1g1//PEHdu/ejTlz5mi3tW7dGuvXr0f9+vWhUqnqLPZ7fUe8vLx0eh7UajVOnz6Nrl271ui4R44cQadOnQAAlZWVOHbsGMaMGaN3fK1atUKrVq0wdepUREVFITExEY8//rjk35Hw8HDY29sjJycHnTt3vus+Bw8eRPv27fHGG29o15mqV0sKjRs3hp2dHQ4ePIiAgAAAVYXq0aNHdSY19+rVC05OTli6dCmSkpKwb98+iSKmB2ERYkaSk5Oxa9cudO/eHfXr10dycjKuXLmCsLAwhISE4Msvv0Tbtm1RWFiISZMmwcHBQds2OjoaTZo0QWxsLD744AMUFhZi2rRpesfw9ttv4/HHH8eYMWMwYsQIODk5ITU1FTt37sT//d//YevWrTh37hw6deoEd3d3/PTTT9BoNAgNDb1v/LXl7u4OT09PLF++HD4+PsjJycGUKVP0OsbYsWPRqVMnzJ8/H3369MHu3buxbds2bY8JAEyfPh29e/dGw4YNMWjQIFhZWeHkyZM4ffq0zi9ucxESEoJ+/fph5MiR+PTTT+Hi4oIpU6bgkUceQb9+/bT7derUCd7e3hgyZAgCAwN1hm+GDBmCDz74AP369cOsWbPg5+eHP//8Exs3bsTkyZPh5+dn9Ljv9x1xcnLChAkT8OOPP6Jx48aYP38+8vPza3zsJUuWICQkBGFhYViwYAFu3LiBYcOG1bj9+fPnsXz5cvTt2xe+vr5IT09HRkYGhg4dCkD674iLiwsmTpyI8ePHQ6PRoGPHjigoKMDBgwehUqkQGxuLkJAQrFmzBtu3b0dgYCC+/PJLHD16FIGBgSaPry44OTnh9ddfx6RJk+Dh4YGGDRti3rx5KCkpwfDhw7X7WVtbIy4uDlOnTkVISMg9h6bJDEg9KYX+JzU1VcTExAgvLy9hb28vmjRpIj7++GMhhBDHjx8Xbdu2FUqlUoSEhIhvv/1WBAQEiAULFmjbp6eni44dOwo7OzvRpEkTkZSUdNeJqSdOnNC2uXHjhgAgfvnlF+26X3/9VXTr1k04OzsLJycn0aJFCzF37lwhRNUk1c6dOwt3d3fh4OAgWrRooZ15f7/4DbFz504RFhYm7O3tRYsWLcSePXu0edU0p+XLl4tHHnlEODg4iP79+4s5c+YIb29vnfMkJSWJ9u3bCwcHB6FSqUS7du3E8uXLtdvv/FlK5c7Jm9evXxcvv/yycHV1FQ4ODiImJkacPXu2WpvJkycLAGL69OnVtuXl5YmhQ4eKevXqCXt7exEUFCRGjhwpCgoKTBL//b4j5eXl4vXXXxceHh6ifv36IiEh4a4TU+/8zgvxv+91YmKiaNeunbCzsxPh4eFi9+7d2n1uT0y9ceOGTts7J6ZevHhR9O/fX/j4+Ag7OzsREBAgpk+fLtRqtXb/B31HjO3OialCCKHRaMTChQtFaGiosLW1FV5eXiImJkbs3btXCFE1eTUuLk64uroKNzc38frrr4spU6ZocxRC906Th8WdMd+6dUuMHTtW+53t0KGD+PXXX6u1ycrKEgC0E1bJPCmE4GPoSH5GjhyJM2fOYP/+/VKHQgbKzs5GYGAgTpw4YdaPYKfaGzx4MKytrfHVV1/VuM3+/fvx1FNPITc3Fw0aNDBhdGQITkwlWfjwww+1d4N8/PHHWL16NWJjY6UOi4juo7KyEqmpqTh8+DCaNWtWozZlZWX466+/MHPmTDz77LMsQMwcixCShV9//RXdunVDREQEli1bhsWLF2PEiBFSh0VE93H69Gm0bdsWzZo1w2uvvVajNl9//TUCAgKQn5+PefPmmThCMhSHY4iIiEgS7AkhIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCGSsbi4OPTv31/7uUuXLjrv4Kgre/bsgUKhuO9j2hUKBTZv3lzjY86cOdPgh5dlZ2dDoVAgJSXFoOMQ0d2xCCEyM3FxcVAoFFAoFLCzs0NwcDBmzZqFyspKk59748aNmD17do32rUnhQER0P3yBHZEZ6tGjB1auXImysjL89NNPGD16NGxtbTF16tRq+5aXl8POzs4o5/Xw8DDKcYiIaoI9IURmyN7eHt7e3ggICMDrr7+O6OhobNmyBcD/hlDmzp0LX19fhIaGAgByc3Px3HPPwc3NDR4eHujXrx+ys7O1x1Sr1ZgwYQLc3Nzg6emJyZMn45/PKvzncExZWRnefvtt+Pv7w97eHsHBwfj888+RnZ2Nrl27Aqh607FCoUBcXBwAQKPRICEhAYGBgXBwcEBkZCQ2bNigc56ffvoJTZo0gYODA7p27aoTZ029/fbbaNKkCRwdHREUFIR3330XFRUV1fb79NNP4e/vD0dHRzz33HMoKCjQ2f7ZZ58hLCwMSqUSTZs2xSeffKJ3LERUOyxCiB4CDg4OKC8v137etWsX0tPTsXPnTmzduhUVFRW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      ],
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        "# setting confusion matrix parameters\n",
        "cm = confusion_matrix(y_test, y_pred, labels=classes)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classes)\n",
        "\n",
        "# showing confusion matrix\n",
        "disp.plot()\n",
        "plt.show()"
      ]
    }
  ],
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