baseline_model.ipynb

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     "name": "#%%\n"
    },
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    "id": "2kNLXzQBuC1k"
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   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import f1_score, recall_score, precision_score, classification_report, accuracy_score, confusion_matrix\n",
    "from torch.utils.data import TensorDataset, random_split, DataLoader, RandomSampler, SequentialSampler\n",
    "from transformers import (\n",
    "    BertTokenizer,\n",
    "    BertConfig,\n",
    "    BertForSequenceClassification,\n",
    "    AdamW,\n",
    "    Adafactor,\n",
    "    get_linear_schedule_with_warmup,\n",
    "    logging,\n",
    ")\n",
    "from simpletransformers.classification import ClassificationModel"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from google.colab import drive\n",
    "\n",
    "drive.mount('/content/drive')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2f597SVbu1ap",
    "outputId": "86710164-51e8-4b90-e9e6-00db8bbd9b89"
   },
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true,
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7VJY0QVmuC1m",
    "outputId": "859668b8-228c-4f76-e8b8-7d92c325162e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "GPU: NVIDIA A100-SXM4-40GB\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name == '/device:GPU:0':\n",
    "  device = torch.device(\"cuda\")\n",
    "  print('GPU:', torch.cuda.get_device_name(0))\n",
    "else:\n",
    "  raise SystemError('GPU device not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true,
    "id": "RkOf-3QGuC1m"
   },
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-turkish-128k-uncased', do_lower_case=True, use_cuda=torch.cuda.is_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true,
    "id": "1BfKbwhPuC1m"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/content/drive/MyDrive/Nane&Limon/Dataset/teknofest_train_final.csv',sep=\"|\")\n",
    "df = pd.DataFrame(df, columns=['text', 'target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "cVs05_KeuC1n"
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df[\"target\"] = le.fit_transform(df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
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    },
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   "outputs": [
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     "name": "stdout",
     "text": [
      "[0 3 4 2 1]\n",
      "                                                text  target\n",
      "0                                        çürük dişli       0\n",
      "1  Bu adamın islama ve müslümanlara verdiği zarar...       3\n",
      "2                               erkekler zora gelmez       4\n",
      "3  Utanmazın götüne kazık sokmuşlar bu tıkırtı ne...       2\n",
      "4     otomasyon< sistemlerine= doğrudan bağlanabilir       1\n"
     ]
    }
   ],
   "source": [
    "print(df['target'].unique())\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "training = df.groupby('target').apply(lambda x : x.sample(frac = 0.8))\n",
    "test = pd.concat([df,training]).drop_duplicates(keep=False)"
   ],
   "metadata": {
    "id": "x4g69kg_3G7R"
   },
   "execution_count": 38,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "training.reset_index(drop=True)"
   ],
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "pHtqlGYH3P63",
    "outputId": "db156178-d429-4e20-a2be-99acd512768a"
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   "execution_count": 39,
   "outputs": [
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    "test.reset_index(drop=True)"
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       "                                                   text  target\n",
       "0        otomasyon< sistemlerine= doğrudan bağlanabilir       1\n",
       "1     Kadın yönetici sayısı oldukça az olduğu için ş...       4\n",
       "2     makara bunu duyunca avluya çıkmış ve kızağa sa...       1\n",
       "3                                     işe yaramaz herif       0\n",
       "4     annenin babası bir türk bile olsa umurumda değ...       3\n",
       "...                                                 ...     ...\n",
       "2491  en birinci emelleri oğullarına yahut kardeşler...       1\n",
       "2492        hiçbir tane de mi ceren gözlü kürt kızı yok       1\n",
       "2493              yanlarında bir de ermeni papaz vardır       1\n",
       "2494  macaristandan almanyaya fon tepkisi macaristan...       1\n",
       "2495  uyuma taklidi yapan tehlikeli bir hayvanın göz...       1\n",
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       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-89b602e5-5a72-4d3e-8338-2b89ba3c25a8 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-89b602e5-5a72-4d3e-8338-2b89ba3c25a8');\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",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true,
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XKkB5XPRuC1n",
    "outputId": "9f5b5964-d313-46d2-af86-bf2365c19365"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at dbmdz/bert-base-turkish-128k-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.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 dbmdz/bert-base-turkish-128k-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": [
    "model = ClassificationModel('bert', 'dbmdz/bert-base-turkish-128k-uncased',\n",
    "                        num_labels=5,\n",
    "                        args={'reprocess_input_data': True,\n",
    "                        'overwrite_output_dir': True,\n",
    "                        'num_train_epochs': 3,\n",
    "                        \"output_dir\": \"bert_model\",\n",
    "                        \"warmup_steps\": 100,\n",
    "                        \"weight_decay\":0,\n",
    "                        \"load_best_model_at_end\": True,\n",
    "                        \"use_early_stopping\": True,\n",
    "                        \"early_stopping_patience\": 5,\n",
    "                        \"early_stopping_delta\": 0.001,\n",
    "                        \"max_length\": 80,\n",
    "                        \"tokenizer_name\": tokenizer,\n",
    "                        \"manual_seed\": 42},\n",
    "                        use_cuda=torch.cuda.is_available())"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "training_texts = df.text.values\n",
    "training_labels = df.target.values"
   ],
   "metadata": {
    "id": "YFBjhdWMzCdx"
   },
   "execution_count": 42,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "input_ids = []\n",
    "attention_masks = []\n",
    "max_len = 64\n",
    "\n",
    "\n",
    "for text in training_texts:\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        str(text),                     \n",
    "                        add_special_tokens = True,\n",
    "                        max_length = max_len,      \n",
    "                        pad_to_max_length = True,\n",
    "                        return_attention_mask = True, \n",
    "                        return_tensors = 'pt',\n",
    "                   )\n",
    "    \n",
    "    input_ids.append(encoded_dict['input_ids'])\n",
    "    attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "input_ids = torch.cat(input_ids, dim=0)\n",
    "attention_masks = torch.cat(attention_masks, dim=0)\n",
    "labels = torch.tensor(training_labels)\n",
    "\n",
    "print('Original: ', training_texts[0])\n",
    "print('Token IDs:', input_ids[0])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pBB-YziKy96C",
    "outputId": "e86eea8a-1675-4c0b-d29c-e113cc5e78a3"
   },
   "execution_count": 43,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:2346: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Original:  çürük dişli\n",
      "Token IDs: tensor([    2, 18534,  2067,  5092,  1987,     3,     0,     0,     0,     0,\n",
      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "            0,     0,     0,     0])\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "train_dataset = TensorDataset(input_ids, attention_masks, labels)\n",
    "\n",
    "batch_size = 64\n",
    "\n",
    "train_dataloader = DataLoader(\n",
    "            train_dataset,  \n",
    "            sampler = RandomSampler(train_dataset), \n",
    "            batch_size = batch_size \n",
    "        )\n",
    "\n",
    "\n",
    "num_of_cat = len(df['target'].unique())\n",
    "\n",
    "model = BertForSequenceClassification.from_pretrained(\n",
    "    \"dbmdz/bert-base-turkish-128k-uncased\",\n",
    "    num_labels = num_of_cat, \n",
    "    output_attentions = False,\n",
    "    output_hidden_states = False,\n",
    ")\n",
    "model.cuda()\n",
    "\n",
    "\n",
    "epochs = 5 \n",
    "\n",
    "optimizer = AdamW(model.parameters(),\n",
    "                  lr = 5e-6,\n",
    "                  eps = 1e-8 \n",
    "                )\n",
    "\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
    "                                            num_warmup_steps = 0,\n",
    "                                            num_training_steps = total_steps)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VfjLszzQzmtm",
    "outputId": "42e9389a-38a6-4aeb-8b90-1608f3e59e3b"
   },
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at dbmdz/bert-base-turkish-128k-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.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 dbmdz/bert-base-turkish-128k-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",
      "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def format_time(elapsed):\n",
    "    elapsed_rounded = int(round((elapsed)))\n",
    "    return str(datetime.timedelta(seconds=elapsed_rounded))"
   ],
   "metadata": {
    "id": "-iQp_WeN1WTn"
   },
   "execution_count": 45,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "seed_val = 20\n",
    "\n",
    "random.seed(seed_val)\n",
    "np.random.seed(seed_val)\n",
    "torch.manual_seed(seed_val)\n",
    "torch.cuda.manual_seed_all(seed_val)\n",
    "\n",
    "training_stats = []\n",
    "total_t0 = time.time()\n",
    "\n",
    "for epoch_i in range(0, epochs):\n",
    "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
    "    t0 = time.time()\n",
    "    total_train_loss = 0\n",
    "    model.train()\n",
    "    \n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "        if step % 10 == 0 and not step == 0:\n",
    "            elapsed = format_time(time.time() - t0)\n",
    "            print('Batch {:>5,}  of  {:>5,}.    Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
    "\n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        b_labels = batch[2].to(device)\n",
    "\n",
    "        model.zero_grad()        \n",
    "        output = model(b_input_ids, \n",
    "                             token_type_ids=None, \n",
    "                             attention_mask=b_input_mask, \n",
    "                             labels=b_labels)\n",
    "        loss = output['loss']\n",
    "        logits = output['logits']\n",
    "        total_train_loss += loss.item()\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)            \n",
    "    training_time = format_time(time.time() - t0)\n",
    "\n",
    "    print(\"Average training loss: {0:.2f}\".format(avg_train_loss))\n",
    "    print(\"Training epoch took: {:}\".format(training_time))\n",
    "\n",
    "    training_stats.append(\n",
    "        {\n",
    "            'epoch': epoch_i + 1,\n",
    "            'Training Loss': avg_train_loss,\n",
    "            'Training Time': training_time,\n",
    "        }\n",
    "    )\n",
    "\n",
    "print(\"Training completed in {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HM0TnpVo1U8T",
    "outputId": "c6940620-e1fa-45b7-c244-6e4478a451b6"
   },
   "execution_count": 46,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======== Epoch 1 / 5 ========\n",
      "Batch    10  of    198.    Elapsed: 0:00:02.\n",
      "Batch    20  of    198.    Elapsed: 0:00:04.\n",
      "Batch    30  of    198.    Elapsed: 0:00:05.\n",
      "Batch    40  of    198.    Elapsed: 0:00:07.\n",
      "Batch    50  of    198.    Elapsed: 0:00:09.\n",
      "Batch    60  of    198.    Elapsed: 0:00:11.\n",
      "Batch    70  of    198.    Elapsed: 0:00:12.\n",
      "Batch    80  of    198.    Elapsed: 0:00:14.\n",
      "Batch    90  of    198.    Elapsed: 0:00:16.\n",
      "Batch   100  of    198.    Elapsed: 0:00:18.\n",
      "Batch   110  of    198.    Elapsed: 0:00:19.\n",
      "Batch   120  of    198.    Elapsed: 0:00:21.\n",
      "Batch   130  of    198.    Elapsed: 0:00:23.\n",
      "Batch   140  of    198.    Elapsed: 0:00:25.\n",
      "Batch   150  of    198.    Elapsed: 0:00:26.\n",
      "Batch   160  of    198.    Elapsed: 0:00:28.\n",
      "Batch   170  of    198.    Elapsed: 0:00:30.\n",
      "Batch   180  of    198.    Elapsed: 0:00:31.\n",
      "Batch   190  of    198.    Elapsed: 0:00:33.\n",
      "Average training loss: 1.28\n",
      "Training epoch took: 0:00:34\n",
      "======== Epoch 2 / 5 ========\n",
      "Batch    10  of    198.    Elapsed: 0:00:02.\n",
      "Batch    20  of    198.    Elapsed: 0:00:03.\n",
      "Batch    30  of    198.    Elapsed: 0:00:05.\n",
      "Batch    40  of    198.    Elapsed: 0:00:07.\n",
      "Batch    50  of    198.    Elapsed: 0:00:09.\n",
      "Batch    60  of    198.    Elapsed: 0:00:10.\n",
      "Batch    70  of    198.    Elapsed: 0:00:12.\n",
      "Batch    80  of    198.    Elapsed: 0:00:14.\n",
      "Batch    90  of    198.    Elapsed: 0:00:16.\n",
      "Batch   100  of    198.    Elapsed: 0:00:17.\n",
      "Batch   110  of    198.    Elapsed: 0:00:19.\n",
      "Batch   120  of    198.    Elapsed: 0:00:21.\n",
      "Batch   130  of    198.    Elapsed: 0:00:23.\n",
      "Batch   140  of    198.    Elapsed: 0:00:24.\n",
      "Batch   150  of    198.    Elapsed: 0:00:26.\n",
      "Batch   160  of    198.    Elapsed: 0:00:28.\n",
      "Batch   170  of    198.    Elapsed: 0:00:30.\n",
      "Batch   180  of    198.    Elapsed: 0:00:31.\n",
      "Batch   190  of    198.    Elapsed: 0:00:33.\n",
      "Average training loss: 0.44\n",
      "Training epoch took: 0:00:34\n",
      "======== Epoch 3 / 5 ========\n",
      "Batch    10  of    198.    Elapsed: 0:00:02.\n",
      "Batch    20  of    198.    Elapsed: 0:00:03.\n",
      "Batch    30  of    198.    Elapsed: 0:00:05.\n",
      "Batch    40  of    198.    Elapsed: 0:00:07.\n",
      "Batch    50  of    198.    Elapsed: 0:00:09.\n",
      "Batch    60  of    198.    Elapsed: 0:00:10.\n",
      "Batch    70  of    198.    Elapsed: 0:00:12.\n",
      "Batch    80  of    198.    Elapsed: 0:00:14.\n",
      "Batch    90  of    198.    Elapsed: 0:00:16.\n",
      "Batch   100  of    198.    Elapsed: 0:00:17.\n",
      "Batch   110  of    198.    Elapsed: 0:00:19.\n",
      "Batch   120  of    198.    Elapsed: 0:00:21.\n",
      "Batch   130  of    198.    Elapsed: 0:00:23.\n",
      "Batch   140  of    198.    Elapsed: 0:00:24.\n",
      "Batch   150  of    198.    Elapsed: 0:00:26.\n",
      "Batch   160  of    198.    Elapsed: 0:00:28.\n",
      "Batch   170  of    198.    Elapsed: 0:00:30.\n",
      "Batch   180  of    198.    Elapsed: 0:00:31.\n",
      "Batch   190  of    198.    Elapsed: 0:00:33.\n",
      "Average training loss: 0.28\n",
      "Training epoch took: 0:00:34\n",
      "======== Epoch 4 / 5 ========\n",
      "Batch    10  of    198.    Elapsed: 0:00:02.\n",
      "Batch    20  of    198.    Elapsed: 0:00:03.\n",
      "Batch    30  of    198.    Elapsed: 0:00:05.\n",
      "Batch    40  of    198.    Elapsed: 0:00:07.\n",
      "Batch    50  of    198.    Elapsed: 0:00:09.\n",
      "Batch    60  of    198.    Elapsed: 0:00:10.\n",
      "Batch    70  of    198.    Elapsed: 0:00:12.\n",
      "Batch    80  of    198.    Elapsed: 0:00:14.\n",
      "Batch    90  of    198.    Elapsed: 0:00:16.\n",
      "Batch   100  of    198.    Elapsed: 0:00:17.\n",
      "Batch   110  of    198.    Elapsed: 0:00:19.\n",
      "Batch   120  of    198.    Elapsed: 0:00:21.\n",
      "Batch   130  of    198.    Elapsed: 0:00:23.\n",
      "Batch   140  of    198.    Elapsed: 0:00:24.\n",
      "Batch   150  of    198.    Elapsed: 0:00:26.\n",
      "Batch   160  of    198.    Elapsed: 0:00:28.\n",
      "Batch   170  of    198.    Elapsed: 0:00:30.\n",
      "Batch   180  of    198.    Elapsed: 0:00:31.\n",
      "Batch   190  of    198.    Elapsed: 0:00:33.\n",
      "Average training loss: 0.24\n",
      "Training epoch took: 0:00:34\n",
      "======== Epoch 5 / 5 ========\n",
      "Batch    10  of    198.    Elapsed: 0:00:02.\n",
      "Batch    20  of    198.    Elapsed: 0:00:03.\n",
      "Batch    30  of    198.    Elapsed: 0:00:05.\n",
      "Batch    40  of    198.    Elapsed: 0:00:07.\n",
      "Batch    50  of    198.    Elapsed: 0:00:09.\n",
      "Batch    60  of    198.    Elapsed: 0:00:10.\n",
      "Batch    70  of    198.    Elapsed: 0:00:12.\n",
      "Batch    80  of    198.    Elapsed: 0:00:14.\n",
      "Batch    90  of    198.    Elapsed: 0:00:16.\n",
      "Batch   100  of    198.    Elapsed: 0:00:17.\n",
      "Batch   110  of    198.    Elapsed: 0:00:19.\n",
      "Batch   120  of    198.    Elapsed: 0:00:21.\n",
      "Batch   130  of    198.    Elapsed: 0:00:23.\n",
      "Batch   140  of    198.    Elapsed: 0:00:24.\n",
      "Batch   150  of    198.    Elapsed: 0:00:26.\n",
      "Batch   160  of    198.    Elapsed: 0:00:28.\n",
      "Batch   170  of    198.    Elapsed: 0:00:30.\n",
      "Batch   180  of    198.    Elapsed: 0:00:31.\n",
      "Batch   190  of    198.    Elapsed: 0:00:33.\n",
      "Average training loss: 0.23\n",
      "Training epoch took: 0:00:34\n",
      "Training completed in 0:02:52 (h:mm:ss)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "df_stats = pd.DataFrame(data=training_stats)\n",
    "plt.plot(df_stats['Training Loss'], label=\"Training\")\n",
    "plt.title(\"Training Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8])\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "4SpRCSxO2L0-",
    "outputId": "30346b54-5e8a-4c7b-c172-8e06ef69ea9a"
   },
   "execution_count": 52,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "test_texts = test.text.values\n",
    "test_labels = test.target.values\n",
    "\n",
    "input_ids = []\n",
    "attention_masks = []\n",
    "\n",
    "for text in test_texts:\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        text,                     \n",
    "                        add_special_tokens = True, \n",
    "                        max_length = max_len,          \n",
    "                        pad_to_max_length = True,\n",
    "                        return_attention_mask = True,  \n",
    "                        return_tensors = 'pt',   \n",
    "                   )\n",
    "    \n",
    "    input_ids.append(encoded_dict['input_ids'])\n",
    "    attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "input_ids = torch.cat(input_ids, dim=0)\n",
    "attention_masks = torch.cat(attention_masks, dim=0)\n",
    "labels = torch.tensor(test_labels)\n",
    "\n",
    "\n",
    "prediction_data = TensorDataset(input_ids, attention_masks, labels)\n",
    "prediction_sampler = SequentialSampler(prediction_data)\n",
    "prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YvFNDnN_2TMY",
    "outputId": "ba3d5260-ff7b-4341-db1d-6f3bb9d7580c"
   },
   "execution_count": 53,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:2346: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  warnings.warn(\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print('Prediction started on test data')\n",
    "model.eval()\n",
    "predictions , true_labels = [], []\n",
    "\n",
    "for batch in prediction_dataloader:\n",
    "  batch = tuple(t.to(device) for t in batch)\n",
    "  b_input_ids, b_input_mask, b_labels = batch\n",
    "\n",
    "  with torch.no_grad():\n",
    "      outputs = model(b_input_ids, token_type_ids=None, \n",
    "                      attention_mask=b_input_mask)\n",
    "\n",
    "  logits = outputs[0]\n",
    "  logits = logits.detach().cpu().numpy()\n",
    "  label_ids = b_labels.to('cpu').numpy()\n",
    "  \n",
    "  predictions.append(logits)\n",
    "  true_labels.append(label_ids)\n",
    "\n",
    "print('Prediction completed')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u8Ao4UC52TOw",
    "outputId": "098a473a-b3f4-402a-d866-900f221df164"
   },
   "execution_count": 54,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Prediction started on test data\n",
      "Prediction completed\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prediction_set = []\n",
    "\n",
    "for i in range(len(true_labels)):\n",
    "  pred_labels_i = np.argmax(predictions[i], axis=1).flatten()\n",
    "  prediction_set.append(pred_labels_i)\n",
    "\n",
    "prediction_scores = [item for sublist in prediction_set for item in sublist]"
   ],
   "metadata": {
    "id": "de2xfTX_2TRM"
   },
   "execution_count": 55,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "f_score = f1_score(test_labels, prediction_scores, average='macro')\n",
    "precision = precision_score(test_labels, prediction_scores, average='macro')\n",
    "recall = recall_score(test_labels, prediction_scores, average='macro')\n",
    "accr = accuracy_score(test_labels, prediction_scores)"
   ],
   "metadata": {
    "id": "nVBvgS1U2TZM"
   },
   "execution_count": 58,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "print(\"F-Score Macro: \", f_score)\n",
    "print(\"Recall Macro: \", recall)\n",
    "print(\"Precision Macro: \", precision)\n",
    "print(\"Accuracy: \", accr)\n",
    "class_names =['INSULT','OTHER','PROFANITY','RACIST','SEXIST']\n",
    "cm = confusion_matrix(test_labels, prediction_scores, )\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=class_names)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ksT32GUh2Tbl",
    "outputId": "cfb4974d-7b02-4a8d-84a4-65d8f435c2e2"
   },
   "execution_count": 63,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "F-Score Macro:  0.9461078257015206\n",
      "Recall Macro:  0.9488067854267792\n",
      "Precision Macro:  0.9442745858758668\n",
      "Accuracy:  0.9467147435897436\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "disp.plot();"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "YMhy3PXn4hMm",
    "outputId": "53016a61-1574-4025-eb8e-c77f2e1c6c93"
   },
   "execution_count": 64,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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kRfUcNKqeURMRERE9Afb4EBERSYx5ntVVPftOmPgQERFJjB4C9DB1jo9p21sKEx8iIiKJkXKPT/WMmoiIiOgJsMeHiIhIYsxzA8Pq2XfCxIeIiEhi9KIAvan38TFxe0upnukaERER0RNgjw8REZHE6M0w1FVdb2DIxIeIiEhizPN09uqZ+FTPqImIiIieAHt8iIiIJEYHAToTb0Bo6vaWwsSHiIhIYjjURURERCQB7PEhIiKSGB1MH6rSmSeUSsfEh4iISGKkPNTFxIeIiEhi+JBSIiIiIglgjw8REZHEiBCgN3GOj8jL2YmIiKg64FAXERERkQSwx+cpVKN/AqwFG0uHUaXtvv6XpUOoFkK9mls6hGrBytHR0iFUC/qcXEuHQP+jFwXoRdOGqkzd3lKY+BAREUmMzgxPZzd1e0upnlETERERPQH2+BAREUkMh7qIiIhIMvSwgt7EQR9Tt7eU6hk1ERER0RNgjw8REZHE6EQBOhOHqkzd3lKY+BAREUkM5/gQERGRZIhmeDq7yDs3ExEREVVt7PEhIiKSGB0E6Ex8yKip21sKEx8iIiKJ0Yumz9HRi2YKppJxqIuIiIgkgz0+REREEqM3w+RmU7e3FCY+REREEqOHAL2Jc3RM3d5Sqme6RkRERPQEmPgQERFJTPGdm01dymrOnDkQBMFoadCggWF9fn4+IiMj4erqCgcHB/Tu3RspKSlGbdy4cQNdu3aFnZ0dPDw8MHHiRGi12nIfO4e6iIiIJMYSc3waNWqEX3/91fDa2vrfFGTcuHH46aef8N1330GlUmHUqFHo1asX/vzzTwCATqdD165doVarcejQISQlJWHQoEGwsbHBggULyhUHEx8iIiKqcNbW1lCr1SXKMzIyEB0djY0bN+KVV14BAKxbtw4NGzbE4cOH0aZNG+zZswfnzp3Dr7/+Ck9PTzRv3hxz587F5MmTMWfOHMjl8jLHwaEuIiIiidFDMDyv64mX/01uzszMNFo0Gk2p+7x06RK8vLxQt25dhIWF4caNGwCAY8eOobCwECEhIYa6DRo0QJ06dRAbGwsAiI2NRZMmTeDp6WmoExoaiszMTJw9e7Zcx87Eh4iISGLE/13VZcoi/i/x8fb2hkqlMiwLFy4ssb/WrVsjJiYGu3fvxurVq3H16lW0bdsWWVlZSE5Ohlwuh7Ozs9E2np6eSE5OBgAkJycbJT3F64vXlQeHuoiIiCTGnE9nT0hIgJOTk6FcoVCUqNu5c2fD/zdt2hStW7eGj48PtmzZAltbW5PiKC/2+BAREdETc3JyMlpKS3we5OzsjPr16+Py5ctQq9UoKChAenq6UZ2UlBTDnCC1Wl3iKq/i16XNG3oUJj5EREQSU3xVl6nLk8rOzsaVK1dQs2ZNtGzZEjY2Nti3b59h/YULF3Djxg0EBwcDAIKDg3H69GmkpqYa6uzduxdOTk4ICgoq17451EVERCQx5hzqKosJEybg9ddfh4+PDxITEzF79mzIZDL0798fKpUKw4YNw/jx4+Hi4gInJyeMHj0awcHBaNOmDQCgY8eOCAoKwsCBA7FkyRIkJydjxowZiIyMLFMP0/2Y+BAREVGFunnzJvr374+7d+/C3d0dL774Ig4fPgx3d3cAwLJly2BlZYXevXtDo9EgNDQUq1atMmwvk8mwc+dOjBw5EsHBwbC3t0d4eDiioqLKHQsTHyIiIomp7Gd1bdq06ZHrlUolVq5ciZUrVz60jo+PD3bt2lXmfT4MEx8iIiKJqeyhrqqEk5uJiIhIMtjjQ0REJDFS7vFh4kNERCQxUk58ONRFREREksEeHzJZ4+ey0GdEEgKa5MLVsxAfRPgjdk8Nw/oXOqWhS9htBDTJgVMNHd7t3Ajx5+wsGHHluJNkg+j5NfH3fido8qzg5avB+8tuoH6zPABAqFfzUrd7e8Yt9H33NgAg854Mq2bUwpG9KghWwItd0jFy7i3Y2usr6zCqjNcH30Gfkalwcdci/pwtVs2ohQtxT//f0cP0G56AFzreRe26eSjIt8K5E45Y+5Evbl3995ws/voUmrbONNrup01qfDbbv7LDrVJc1QUYNu0Wnm2fCYWtHonXFFg63geXTtlbOrRKwx4fiUlISMDQoUPh5eUFuVwOHx8fjBkzBnfv3sW1a9cgCMIjl5iYGBw4cACCIJS4xTYA+Pr6Yvny5UavS2tn0aJFAFBiny4uLmjXrh0OHjxYSWfENEo7Ha6et8PKmT6lr7fV4+zfDli7yLuSI7OcrHQZxncPgMxaxLxv4vHlgX8wfFYiHFQ6Q51v484YLeM/vgFBEPFi1wxDncWjfHD9gi0WbrqCqPXxOH3EAcsnSuc8FmvX7R6Gz07Eho/ViAytj/hzSszfGA+Va6GlQ7OYJs9l4McNNTGuX1NMG9II1tYi5kefhcJWZ1Tv582eeOuF5wzL2iW+lgm4inBQafHxtovQFQqYMdAfEe2D8EVUbWRnSKsfQATM8JDS6kla/9IA4uPjERwcjPr16+Pbb7+Fn58fzp49i4kTJ+Lnn39GbGwskpKSDPU/+ugj7N69G7/++quhTKVS4ciRI+Xab1RUFCIiIozKHB0djV7/+uuvaNSoEe7cuYP58+fjtddew8WLF0s8kbaqOXrAGUcPOD90/b5tbgAAz9qaSorI8ras9ICbVwEmLE8wlKnrFBjVcfHQGr2O/UWFZi9ko6ZPUb0blxQ4ut8Jn/58wdBL9O68m5g5oC6Gz7oFV7Xx9k+zXsPvYPdGF+zZ7AIAWDG5Np57NROh/dOw5bOq/f6oKDPfbmz0+uMp9bHp8BEENMrGmaMqQ7kmX4Z7d+SVHV6V1e/dFNxJtMHS930NZSkJ5bvz79NAyj0+kkt8IiMjIZfLsWfPHsMTYevUqYMWLVqgXr16mDFjBlavXm2o7+DgAGtr63I/BO1Bjo6Oj23D1dUVarUaarUa06ZNw6ZNm3DkyBF069bNpH1T5Tu8R4WWL2di3nBfnIq1h5u6EK8NvoMuYWml1r932xp/7XPChOXXDWXnj9rDQaU1JD0A8EzbLAhWwD8n7PFC54zSmnrqWNvoEdA0F5s+8zCUiaKAEwcdEdQy14KRVS12jkWJcNYDPRftX09F+26puHdbjiP7XfDtKm9o8mWWCLFKaNMhA8d+c8L0NfFo2iYbd5JtsPNrd/y80c3SoVElkVTik5aWhl9++QXz5883JD3F1Go1wsLCsHnzZqxatQqCYLlMNi8vD19//TUAQC5/+C81jUYDjebfXpTMzMyH1qXKlXRDjp1fu6HX8Nt4c3QKLp60w+qZtWFjI6JDv3sl6u/d4gJbBx1e7PJvMpN22xrOrsa9OjJrwNFZi7RU6bx1nVx0kFkD6beNj/neHWt4+0unF/FRBEHEiGnxOHvMCdcv/TtP5cBOD6QkKpCWKodfYA6GTriG2n55mDe6oQWjtayadTR4beBtbP3SA5s+VaN+81yMjEpAYYGAX//jaunwKg17fCTi0qVLEEURDRuW/qZv2LAh7t27h9u3b8PDw6PUOg+qXbt2ibLc3JK/QidPnowZM2YYlf38889o27at4fXzzz8PKysr5ObmQhRFtGzZEq+++upD971w4UJ88MEHZYqTKpeoBwKa5mHo1KJhU/8mebj2jxI//Z9bqYnPL5tc8ErPe5Arq+uoOVlS5Owr8A3IxYS3mhqV/7zl317maxftkXZbjkXrz6Cmdx6SEmwfbEYSBCvg0ik7rFtcCwBw5awdfAPz0HXgHSY+T9BGdSSpxKeYKJrvy+XgwYMl5uq8/PLLJepNnDgRgwcPNiqrVauW0evNmzejQYMGOHPmDCZNmoSYmBjY2Ng8dN9Tp07F+PHjDa8zMzPh7S29ia9VkYuHFj71843KvAPy8ccuVYm6p4/Y4+YVJaatuWbchrsW6XeN36I6LZCVbl1iftDTLDNNBp0WcHY3PuYablrcuy3JjzAjI2dewXMvp2HigKa4k/LouSr/nCz6rKrpky/ZxCct1QbXLymNyhIuKfFil3TLBESVTlKfGv7+/hAEAefPn0fPnj1LrD9//jxq1KhheFpsWfj5+cHZ2dmozNq65Gl1c3ODv/+jLyH19vZGQEAAAgICoNVq0bNnT5w5cwYKRekfZgqF4qHryLKCns1BwhXjf5tb8Qp41Cp5FdIv37oioGku6jUyTpQatspBdoY1Lp2yRUDTonk+cX84QtQDDVrkVFzwVYy20AqXTtmhxYtZiN1dlDgKgojmL2ZjR4x0fqGXJGLkzHg83+EuJg9sgpSbysduUa9h0d9N2m3pTnY+d9Qe3nWN32u16mqQelNa50TKPT6Supzd1dUVHTp0wKpVq5CXl2e0Ljk5GRs2bMAbb7xh0fk9xfr06QNra2usWrXK0qE8ltJOh7pBuagbVDTEp/bWoG5QLty9iuZfOKi0qBuUizoBRee8dt081A3KRQ33p/dS5F7DU/HPcXt8u8IDt67K8d+tztj1jSu6DbljVC8nywq//6hCp7fulmijToAGrdpnYvkEb/xzwg5n/7LHyhm10K57uqSu6AKArV+4ofNbaQjpmwZv/3yMXnQTSjs99mxysXRoFhM5+wpe6ZaKJe8HIi9HhhpuBajhVgC5ouhy9preeej/7g34N8qGR618tH7lLiYsvojTfznh2gXp3K/mQVu/9ECDZ3Lw5qhkePnmo32PNHQJu4Md68v+g/dpIIqCWZbqSFI9PgDw2Wef4fnnn0doaCjmzZtndDl7rVq1MH/+/ArZb1ZWFpKTk43K7Ozs4OTkVGp9QRDw3nvvYc6cORgxYgTs7KrujdrqN83Bks0XDK9HzCq6hHvvd65YOqEugjuk4/2lVw3rp62MBwB8s8wL3yw3Hu57WgQ2z8Os6KtYt7AmNixTQ+1dgHeibuGVXsbze377oQYgCmjfo+S8HwCY/Nl1rJxeG1P61TPcwPDdebcq4xCqlN921IDKVYdBE5NRw12L+LO2mB7mh/Q7Dx8Kftq99lbR58mSb04blS+dEoBft3misNAKLYLT0WNQIpR2OtxOUuCPPa7YtEraw+EXT9oj6u16GDL1FsLGJiE5QY41c2pj/zbpJtFSI7nEJyAgAEePHsXs2bPRr18/pKWlQa1Wo0ePHpg9ezZcXCrmj3/WrFmYNWuWUdmIESOwZs2ah24THh6O6dOn47PPPsOkSZMqJC5zOHXYCZ18nn3o+r3/ccPe/0jvUtE2HTLRpsOjr7TrMuAuugwo2dtTzKmGDlNXXX/oeinZsc4NO9ZJ7+/oYToHvvjI9XeSFZg0sOkj60jVkX0qHNlXcr6dlBTfhNDUNqojQTTnTF+yqMzMTKhUKrS36QtrQbq/hMti9/W/LB1CtfCwx2qQMasHLnCg0ulzeN+lR9GKhTig34qMjIyHjgaYqvh7ovX292Btb9ocUW2OBkd6rKjQeCuCpOb4EBERkbRJbqiLiIhI6swxOZmTm4mIiKhakPLl7Ex8iIiIJEbKPT6c40NERESSwR4fIiIiiRHNMNRVXXt8mPgQERFJjAjA1JvZVNd74XCoi4iIiCSDPT5EREQSo4cAQaJ3bmbiQ0REJDG8qouIiIhIAtjjQ0REJDF6UYDAGxgSERGRFIiiGa7qqqaXdXGoi4iIiCSDPT5EREQSI+XJzUx8iIiIJIaJDxEREUmGlCc3c44PERERSQZ7fIiIiCRGyld1MfEhIiKSmKLEx9Q5PmYKppJxqIuIiIgkgz0+REREEsOruoiIiEgyxP8tprZRHXGoi4iIiCSDPT5EREQSw6EuIiIikg4Jj3Ux8SEiIpIaM/T4oJr2+HCODxEREVWqRYsWQRAEjB071lCWn5+PyMhIuLq6wsHBAb1790ZKSorRdjdu3EDXrl1hZ2cHDw8PTJw4EVqttlz7ZuJDREQkMcV3bjZ1eRJ///03Pv/8czRt2tSofNy4cfjxxx/x3Xff4bfffkNiYiJ69eplWK/T6dC1a1cUFBTg0KFDWL9+PWJiYjBr1qxy7Z+JDxERkcQUT242dSmv7OxshIWF4csvv0SNGjUM5RkZGYiOjsbHH3+MV155BS1btsS6detw6NAhHD58GACwZ88enDt3Dt988w2aN2+Ozp07Y+7cuVi5ciUKCgrKHAPn+DyFBLk1BMHG0mFUaaFezS0dQrWQscvf0iFUC6quVywdQvUg8Lf20ygzM9PotUKhgEKhKLVuZGQkunbtipCQEMybN89QfuzYMRQWFiIkJMRQ1qBBA9SpUwexsbFo06YNYmNj0aRJE3h6ehrqhIaGYuTIkTh79ixatGhRpnj5V0hERCQ1omCeBYC3tzdUKpVhWbhwYam73LRpE44fP17q+uTkZMjlcjg7OxuVe3p6Ijk52VDn/qSneH3xurJijw8REZHEmPPp7AkJCXBycjKUl9bbk5CQgDFjxmDv3r1QKpWm7dhE7PEhIiKiJ+bk5GS0lJb4HDt2DKmpqXjmmWdgbW0Na2tr/Pbbb1ixYgWsra3h6emJgoICpKenG22XkpICtVoNAFCr1SWu8ip+XVynLJj4EBERSY1opqWMXn31VZw+fRpxcXGGpVWrVggLCzP8v42NDfbt22fY5sKFC7hx4waCg4MBAMHBwTh9+jRSU1MNdfbu3QsnJycEBQWVORYOdREREUlMZT+ywtHREY0bNzYqs7e3h6urq6F82LBhGD9+PFxcXODk5ITRo0cjODgYbdq0AQB07NgRQUFBGDhwIJYsWYLk5GTMmDEDkZGRD51MXZoyJT47duwoc4PdunUrc10iIiIiAFi2bBmsrKzQu3dvaDQahIaGYtWqVYb1MpkMO3fuxMiRIxEcHAx7e3uEh4cjKiqqXPspU+LTo0ePMjUmCAJ0Ol25AiAiIiILsPCztg4cOGD0WqlUYuXKlVi5cuVDt/Hx8cGuXbtM2m+ZEh+9Xm/SToiIiKjqkPLT2U2a3Jyfn2+uOIiIiKiyVPLk5qqk3ImPTqfD3LlzUatWLTg4OCA+Ph4AMHPmTERHR5s9QCIiIiJzKXfiM3/+fMTExGDJkiWQy+WG8saNG+Orr74ya3BERERUEQQzLdVPuROfr7/+Gl988QXCwsIgk8kM5c2aNcM///xj1uCIiIioAnCoq+xu3boFf/+SDy7U6/UoLCw0S1BEREREFaHciU9QUBAOHjxYovw///lPmZ+MSkRERBYk4R6fct+5edasWQgPD8etW7eg1+uxdetWXLhwAV9//TV27txZETESERGROd33dHWT2qiGyt3j0717d/z444/49ddfYW9vj1mzZuH8+fP48ccf0aFDh4qIkYiIiMgsnuhZXW3btsXevXvNHQsRERFVAlEsWkxtozp64oeUHj16FOfPnwdQNO+nZcuWZguKiIiIKpA55uhIJfG5efMm+vfvjz///BPOzs4AgPT0dDz//PPYtGkTateube4YiYiIiMyi3HN83n77bRQWFuL8+fNIS0tDWloazp8/D71ej7fffrsiYiQiIiJzKp7cbOpSDZW7x+e3337DoUOHEBgYaCgLDAzEp59+irZt25o1OCIiIjI/QSxaTG2jOip34uPt7V3qjQp1Oh28vLzMEhQRERFVIAnP8Sn3UNeHH36I0aNH4+jRo4ayo0ePYsyYMfjoo4/MGhwRERGROZWpx6dGjRoQhH/H8nJyctC6dWtYWxdtrtVqYW1tjaFDh6JHjx4VEigRERGZiYRvYFimxGf58uUVHAYRERFVGgkPdZUp8QkPD6/oOIiIiIgq3BPfwBAA8vPzUVBQYFTm5ORkUkBERERUwSTc41Puyc05OTkYNWoUPDw8YG9vjxo1ahgtREREVMVJ+Ons5U58Jk2ahP/+979YvXo1FAoFvvrqK3zwwQfw8vLC119/XRExEhEREZlFuYe6fvzxR3z99dd4+eWXMWTIELRt2xb+/v7w8fHBhg0bEBYWVhFxEhERkblI+Kqucvf4pKWloW7dugCK5vOkpaUBAF588UX8/vvv5o2OiIiIzK74zs2mLtVRuXt86tati6tXr6JOnTpo0KABtmzZgueeew4//vij4aGlJC39RtzECx3vonbdPBRorHDuuBPWfuiDW1dtDXVGz72CFs+nw8WjEPm5Vjh33BFrP/TBzXg7C0ZueY1bZ6Pvu7cR0CQXrmot5gz1RexulaXDshjFlntQxtyFprsK+SPcjVeKIuxmJcHmWC5yZqihfd7BsErV5XKJtnIne6KwnWNFh1xlvDboDroOvANP76ILTq5fVGLDMjWO7ucFJw9yVRdg2LRbeLZ9JhS2eiReU2DpeB9cOmVv6dCoEpS7x2fIkCE4efIkAGDKlClYuXIllEolxo0bh4kTJ5arrcGDB0MQBAiCALlcDn9/f0RFRUGr1eLAgQOGdYIgwN3dHV26dMHp06dLtJOQkIChQ4fCy8sLcrkcPj4+GDNmDO7evWtU7+WXXzZqs3jRarWGOiNGjIBMJsN3331XYj9z5syBIAh45513jMrj4uIgCAKuXbsGALh27RoEQUBcXJxhm0ctISEhCA0NLbG/VatWwdnZGTdv3izXea1sTZ7LxI8bamJc36aYNrgRrG30mL/uLBS2OkOdy2fs8fEUfwzv1BzThwRBEID5687Byqqa/mQwE6WdHvFnlfhsWm1Lh2Jxsov5kP+cAZ2fvNT18u0ZwCN61nPHeSDzG1/DUhgsrS+x20k2WLvQC6M6B2J0l/o4+acj5qy9Cp/6eZYOrUpxUGnx8baL0BUKmDHQHxHtg/BFVG1kZ5h0kXP1I+HJzeX+lx43bpzh/0NCQvDPP//g2LFj8Pf3R9OmTcsdQKdOnbBu3TpoNBrs2rULkZGRsLGxQXBwMADgwoULcHJyQmJiIiZOnIiuXbvi8uXLkMuLPhzj4+MRHByM+vXr49tvv4Wfnx/Onj2LiRMn4ueff8bhw4fh4uJi2F9ERASioqKMT8L/7kCdm5uLTZs2YdKkSVi7di369u1bIl6lUono6Gi8//77CAgIeOzxTZgwwShRevbZZzF8+HBEREQYygoLC9GkSRN8/vnnGDFiBADg6tWrmDRpElavXo3atav2l+LMYUFGrz+eHIBNR/5GQONsnPm7qPfi581qw/rUW8D6ZXWweudJeNbWIOmGslLjrUqO7nfiL3IAyNPDdkkKct/zgHJTWonVVlc0UGy9h+xPvGEz4FqpTYj2VhBdJPbldZ8je417CmMW18RrA++gwTO5uH7R9iFbSU+/d1NwJ9EGS9/3NZSlJCgsFxBVOpM/JXx8fODj4/PE2ysUCqjVRV+KI0eOxLZt27Bjxw5D4uPh4QFnZ2eo1WqMHTsW3bp1wz///GNIsiIjIyGXy7Fnzx7Y2ha9uevUqYMWLVqgXr16mD59OlavXm3Yn52dnWF/D/ruu+8QFBSEKVOmwMvLCwkJCfD29jaqExgYCA8PD0yfPh1btmx57PE5ODjAweHfLnmZTAZHR8cSMXzyyScYNWoUOnbsCF9fXwwbNgwdO3bEwIEDH7uPqsbOoagHLSu99D8vha0OHXunIilBgdtJpf+6J2mxXXUb2ufsoGthBzyY+OTrYbckGXnvuj8ysbFdfRtYkQq92gYFXVQo7OAICNVz8qWprKxEtH0tHQo7Pc4fk1bP1+O06ZCBY785YfqaeDRtk407yTbY+bU7ft7oZunQKpUAMzyd3SyRVL4yJT4rVqwoc4PvvffeEwcDALa2tiWGqAAgIyMDmzZtAgBDb09aWhp++eUXzJ8/35D0FFOr1QgLC8PmzZuxatUqo2eNPUx0dDQGDBgAlUqFzp07IyYmBjNnzixRb9GiRXj22Wdx9OhRtGrV6kkOs4Tw8HBs27YNQ4cORa9evXDmzBmcPXv2kdtoNBpoNBrD68zMTLPEYgpBEDFixjWcPeqI65eMP3C7vpWEYZOuw9Zej4Qrtpg+uBG0heUebaWnjM1vWZBd1iD7k9J7NpVf3oGuoS20wQ6lrgeA/AEu0Dazhai0gvXxXNiuvA0hT4+C7s4VFHXV5NsgD8t3XIJcoUdejhWi3vbDjUvS7VEtTc06Grw28Da2fumBTZ+qUb95LkZGJaCwQMCv/3G1dHhUCcqU+CxbtqxMjQmC8MSJjyiK2LdvH3755ReMHj3aUF48zJOTkwMA6NatGxo0aAAAuHTpEkRRRMOGDUtts2HDhrh37x5u374NDw8PAEXzZr766itDnREjRmDp0qW4dOkSDh8+jK1btwIABgwYgPHjx2PGjBklkqZnnnkG/fr1w+TJk7Fv374nOt7SfPHFF2jUqBF+//13fP/993B3d39k/YULF+KDDz4w2/7NIXJOPHwDcjGhf+MS6/bvcMeJP53h4lGA3sMSMfWTC3j/jSYoLGDyI1XC7UIoP7+DnPlegLzk34H14RxYn8xD9qfepWz9L81b/w5nF9RTQMjXQ/F9uuQSn5tXFHi3YyDsHHVo2zUdE5Zfx8TeAUx+7iNYAZdO2WHd4loAgCtn7eAbmIeuA+9IK/GR8OXsZUp8rl69WmEB7Ny5Ew4ODigsLIRer8dbb72FOXPm4O+//wYAHDx4EHZ2djh8+DAWLFiANWvWlGhDFMveXxcWFobp06cbXhdfibZ27VqEhobCza2ou7NLly4YNmwY/vvf/+LVV18t0c68efPQsGFD7Nmzx5BUmcrDwwMjRozA9u3by/SU+6lTp2L8+PGG15mZmSWG5irTyFnxeK79PUx8qzHuJJccM8/NtkZutjUSr9vinzhHfHf0Lzzf8S5+2/noBI+eXrJLGlil6+AwOsFQJugB2Zl8yH/MQEFXFaySCuHUN95oO7sFydA1UiJncem9RLpAJay+vQcUioBN9fxwfhLaQiskXit6710+bYfA5rno8fZtrJhsuc+FqiYt1QbXH0gEEy4p8WKXdMsEZCkSfmSFxWcCtm/fHqtXr4ZcLoeXl5dhonExPz8/ODs7IzAwEKmpqXjjjTcM9wvy9/eHIAg4f/48evbsWaLt8+fPo0aNGkY9JyqVCv7+/kb1dDod1q9fj+TkZKP963Q6rF27ttTEp169eoiIiMCUKVMQHR1t0jm4n7W1dYlz8DAKhQIKRVWYlCdi5KyreL5DGiYPaISUm4//dSkIAATARl5N3zlkFtrmdshaZfylbLssFfracmj6OkN0kqGgs/Hkb8d3E5Af4YbC1g+fuyKL10DvYCWppKc0ghVgI9dbOowq5dxRe3jXzTcqq1VXg9SbnG8oFRZPfOzt7UskIg8TGRmJhQsXYtu2bejZsydcXV3RoUMHrFq1CuPGjTOa55OcnIwNGzZg0KBBj53fs2vXLmRlZeHEiROQyWSG8jNnzmDIkCFIT08v9R5Fs2bNQr169Qxzj6Qqck48Xn79DqJGNkBejgw13IruI5KTJUOBRga1dz5e6nIHx/9wRkaaDdzUGvQbcQsF+Vb4+4CzZYO3MKWdDl5+/z7oV+1dgLqN8pCVLsPtWxL4ILazgt73geRdKUB0+re8tAnNendriGobAID1kRwI97TQNVACcgHWJ/Kg2HwPmt7OFR19lTJkSiL+3u+E27dsYOugR/se99A0OBvT36pn6dCqlK1femDZ9gt4c1Qyft/pjMDmuegSdgfLJ9exdGiViz0+1YOdnR0iIiIwe/Zs9OjRA4Ig4LPPPsPzzz+P0NBQzJs3z+hy9lq1amH+/PmPbTc6Ohpdu3ZFs2bNjMqDgoIwbtw4bNiwAZGRkSW28/T0xPjx4/Hhhx+a7Riro9fCUgAASzYYT8ZeOtkfv271QIHGCo1bZaLH4CQ4OGmRftcGZ/52wvg3miAjTQJf7o9Qv1kePvz+iuH1Ox8kAgD2bK6BpeMk9kH8pGSAYmcGrL68A4iA3ssG+RFuKOgkrdsEOLtpMfGT63Dx0CI3S4ar55WY/lY9HD8onZs4lsXFk/aIersehky9hbCxSUhOkGPNnNrYv83l8Rs/Rcxx52XJ3LnZ0kaNGoWPP/4Y3333Hfr164eAgAAcPXoUs2fPRr9+/ZCWlga1Wo0ePXpg9uzZRvfwKU1KSgp++uknbNy4scQ6Kysr9OzZE9HR0aUmPkDRfXpWr16N/Pz8UtdLQeeA5x+5Pi1VjlkRQY+sI1WnYh0Q6tXs8RUl5GHzdopl7DLuIda2skd2K16yvWwCE+WyOrJPhSP7pHuHdKkTxPLMDKYqLTMzEyqVCq/Y94e1IO2elMfR/+8qQXq0B5MMKp2q65XHV6KiSUf0UFqxEAf0W5GRkQEnp4rpsSz+nvCdNx9WStOu9tPn5+PajOkVGm9FeKK/woMHD2LAgAEIDg7GrVu3AAD/93//hz/++MOswREREVEFkPAjK8qd+Hz//fcIDQ2Fra0tTpw4YbiBXkZGBhYsWGD2AImIiIjMpdyJz7x587BmzRp8+eWXsLGxMZS/8MILOH78uFmDIyIiIvMrntxs6lIdlXty84ULF/DSSy+VKFepVEhPTzdHTERERFSRJHzn5nL3+KjValy+fLlE+R9//IG6deuaJSgiIiKqQJzjU3YREREYM2YMjhw5AkEQkJiYiA0bNmDChAkYOXJkRcRIREREZBblHuqaMmUK9Ho9Xn31VeTm5uKll16CQqHAhAkTjB4uSkRERFUTb2BYDoIgYPr06Zg4cSIuX76M7OxsBAUFwcHBoSLiIyIiInPjIyvKTy6XIyiId+MlIiKi6qPcc3zat2+PV1555aELERERVXHmuJS9HD0+q1evRtOmTeHk5AQnJycEBwfj559/NqzPz89HZGQkXF1d4eDggN69eyMlJcWojRs3bqBr166ws7ODh4cHJk6cCK1WW+5DL3ePT/PmzY1eFxYWIi4uDmfOnEF4eHi5AyAiIqJKVslDXbVr18aiRYsQEBAAURSxfv16dO/eHSdOnECjRo0wbtw4/PTTT/juu++gUqkwatQo9OrVC3/++ScAQKfToWvXrlCr1Th06BCSkpIwaNAg2NjYlPvmyeVOfJYtW1Zq+Zw5c5CdnV3e5oiIiKgay8zMNHqtUCigUCiMyl5//XWj1/Pnz8fq1atx+PBh1K5dG9HR0di4caNh5GjdunVo2LAhDh8+jDZt2mDPnj04d+4cfv31V3h6eqJ58+aYO3cuJk+ejDlz5kAuL/vzKc32xLgBAwZg7dq15mqOiIiIKooZ7+Pj7e0NlUplWBYuXPjIXet0OmzatAk5OTkIDg7GsWPHUFhYiJCQEEOdBg0aoE6dOoiNjQUAxMbGokmTJvD09DTUCQ0NRWZmJs6ePVuuQ3/iyc0Pio2NhdLEJ70SERFRxTPn5ewJCQlGT2d/sLen2OnTpxEcHIz8/Hw4ODhg27ZtCAoKQlxcHORyOZydnY3qe3p6Ijk5GQCQnJxslPQUry9eVx7lTnx69epl9FoURSQlJeHo0aOYOXNmeZsjIiKiaqx4wvLjBAYGIi4uDhkZGfjPf/6D8PBw/Pbbb5UQobFyJz4qlcrotZWVFQIDAxEVFYWOHTuaLTAiIiJ6esjlcvj7+wMAWrZsib///huffPIJ3njjDRQUFCA9Pd2o1yclJQVqtRpA0eOy/vrrL6P2iq/6Kq5TVuVKfHQ6HYYMGYImTZqgRo0a5doRERERVRFV4AaGer0eGo0GLVu2hI2NDfbt24fevXsDKHog+o0bNxAcHAwACA4Oxvz585GamgoPDw8AwN69e+Hk5FTuewqWK/GRyWTo2LEjzp8/z8SHiIiomqrsR1ZMnToVnTt3Rp06dZCVlYWNGzfiwIED+OWXX6BSqTBs2DCMHz8eLi4ucHJywujRoxEcHIw2bdoAADp27IigoCAMHDgQS5YsQXJyMmbMmIHIyMiHzil6mHIPdTVu3Bjx8fHw8/Mr76ZEREQkQampqRg0aBCSkpKgUqnQtGlT/PLLL+jQoQOAolvlWFlZoXfv3tBoNAgNDcWqVasM28tkMuzcuRMjR45EcHAw7O3tER4ejqioqHLHUu7EZ968eZgwYQLmzp2Lli1bwt7e3mh9WSY4ERERkYVV4rO2oqOjH7leqVRi5cqVWLly5UPr+Pj4YNeuXSbHUubEJyoqCu+//z66dOkCAOjWrRsEQTCsF0URgiBAp9OZHBQRERFVoCowx8dSypz4fPDBB3jnnXewf//+ioyHiIiIqMKUOfERxaLUrl27dhUWDBEREVW8yp7cXJWUa47P/UNbREREVE1xqKts6tev/9jkJy0tzaSAiIiIiCpKuRKfDz74oMSdm4mIiKh64VBXGb355puGOyYSERFRNSXhoS6rslbk/B4iIiKq7sp9VRcRERFVcxLu8Slz4qPX6ysyDiIiIqoknONDTxV9bh70gtbSYdBTQNXlsqVDqBaubGxu6RCqhXpvxVk6hKpNrMQnH0i4x6fMc3yIiIiIqjv2+BAREUmNhHt8mPgQERFJjJTn+HCoi4iIiCSDPT5ERERSw6EuIiIikgoOdRERERFJAHt8iIiIpIZDXURERCQZEk58ONRFREREksEeHyIiIokR/reY2kZ1xMSHiIhIaiQ81MXEh4iISGJ4OTsRERGRBLDHh4iISGo41EVERESSUk0TF1NxqIuIiIgkgz0+REREEiPlyc1MfIiIiKRGwnN8ONRFREREksEeHyIiIonhUBcRERFJB4e6iIiIiJ5+7PEhIiKSGA51ERERkXRIeKiLiQ8REZHUSDjx4RwfIiIikgz2+BAREUkM5/gQERGRdHCoi4iIiOjpxx4fIiIiiRFEEYJoWpeNqdtbChMfMrvXBt1B14F34OldAAC4flGJDcvUOLrfycKRVS1vjErBC10y4O2vQUG+Fc4dtUP0/Jq4eUVp6dCqnMats9H33dsIaJILV7UWc4b6Ina3ytJhWYzzjhS4bkpCeic33B1UGwAgFOjhuiERDrH3IBSKyG3qiDtDa0OnsjFsZ32nAG5rb8L2XBZEpQxZbWvg7ptegEyw1KFYzOuD76DPyFS4uGsRf84Wq2bUwoU4O0uHVXk41PX0Gzx4MARBgCAIsLGxgZ+fHyZNmoT8/Hyjejdv3oRcLkfjxo1LbUcURXzxxRdo3bo1HBwc4OzsjFatWmH58uXIzc0FAMyZMwfNmzc3bJObm4upU6eiXr16UCqVcHd3R7t27fDDDz/g2rVrhrgetsTExFTUaakQt5NssHahF0Z1DsToLvVx8k9HzFl7FT718ywdWpXSNDgHP8a4YexrAZj6Zl3IrEUs+DYeCludpUOrcpR2esSfVeKzabUtHYrFKa7kwmnfXWjqGCfIrv93C3bHM5Ayxhe3ZvrD+l4hPJdd+7eCXoT6w3gIWj1uzQlA6jt14Ph7Gly+S6rcA6gC2nW7h+GzE7HhYzUiQ+sj/pwS8zfGQ+VaaOnQqBJIJvEBgE6dOiEpKQnx8fFYtmwZPv/8c8yePduoTkxMDPr164fMzEwcOXKkRBsDBw7E2LFj0b17d+zfvx9xcXGYOXMmfvjhB+zZs6fU/b7zzjvYunUrPv30U/zzzz/YvXs3+vTpg7t378Lb2xtJSUmG5f3330ejRo2Myt54440KOR8V5cheFf7+rxMSrypwK16JmMU1kZ9jhQbP5Fo6tCplelhd7N3igusXlYg/Z4ulY+vAs3YhApoyQXzQ0f1OWL+kJg5JuJcHAIR8HTxWXsftt72ht5cZyq1ydXA6kIa7A2ohr5EjCuraIXVEHdhezIHiUg4AwO5UFuQ385Ea6YMCXzvkNndCWt+acNp7B9DqLXVIFtFr+B3s3uiCPZtdcOOSEism14YmT0Bo/zRLh1Zpiq/qMnUpq4ULF+LZZ5+Fo6MjPDw80KNHD1y4cMGoTn5+PiIjI+Hq6goHBwf07t0bKSkpRnVu3LiBrl27ws7ODh4eHpg4cSK0Wm25jl1SQ10KhQJqtRoA4O3tjZCQEOzduxeLFy8GUNSbs27dOqxatQq1a9dGdHQ0Wrdubdh+y5Yt2LBhA7Zv347u3bsbyn19fdGtWzdkZmaWut8dO3bgk08+QZcuXQz1W7ZsaVhfHBMAODg4wNra2qisOrOyEtH2tXQo7PQ4f8ze0uFUafZORT09Wemyx9QkqXJfdxO5LZyQ18QRNbYnG8oVV3Mh6ETkNXYwlBXWUqLQzQbKSznQBNhDcSkHBXWURkNfuU0d4b5WD/nNfBT4SmOYx9pGj4Cmudj0mYehTBQFnDjoiKCWEvpxVslDXb/99hsiIyPx7LPPQqvVYtq0aejYsSPOnTsHe/ui74Zx48bhp59+wnfffQeVSoVRo0ahV69e+PPPPwEAOp0OXbt2hVqtxqFDh5CUlIRBgwbBxsYGCxYsKHMskkp87nfmzBkcOnQIPj4+hrL9+/cjNzcXISEhqFWrFp5//nksW7bM8I+yYcMGBAYGGiU9xQRBgEpV+q9RtVqNXbt2oVevXnB0dDTbMWg0Gmg0GsPrhyVeluDbIA/Ld1yCXKFHXo4Vot72w41LnLvyMIIg4p0PbuHMX3a4fsHW0uFQFeRw6B7k1/Jwa279Eutk6VqI1gL09sYf6TonG8gyin4NW6droXOyMV7/vyTIOl2LggqKu6pxctFBZg2k3zY+V/fuWMPbX/OQrehRHvzuUSgUUCgURmW7d+82eh0TEwMPDw8cO3YML730EjIyMhAdHY2NGzfilVdeAQCsW7cODRs2xOHDh9GmTRvs2bMH586dw6+//gpPT080b94cc+fOxeTJkzFnzhzI5fIyxSupoa6dO3fCwcEBSqUSTZo0QWpqKiZOnGhYHx0djTfffBMymQyNGzdG3bp18d133xnWX7p0CYGBgeXe7xdffIFDhw7B1dUVzz77LMaNG2fIYE2xcOFCqFQqw+Lt7W1ym+Zy84oC73YMxHuv1cfOr90wYfl11AnIf/yGEjVqwS34NMjHwpE+j69MkiO7WwDXr28hNdIHolxSH9tUQcw51OXt7W30XbRw4cLH7j8jIwMA4OLiAgA4duwYCgsLERISYqjToEED1KlTB7GxsQCA2NhYNGnSBJ6enoY6oaGhyMzMxNmzZ8t87JJ6B7Vv3x5xcXE4cuQIwsPDMWTIEPTu3RsAkJ6ejq1bt2LAgAGG+gMGDEB0dLThtfiEl+699NJLiI+Px759+9CnTx+cPXsWbdu2xdy5c006nqlTpyIjI8OwJCQkmNSeOWkLrZB4TYHLp+2wbpEXrp6zRY+3b1s6rCopcv5NtO6QiUl96uFOUtl+sZC0KOJzYZ2pRe1pF1B3QBzqDoiD7fkcqH65g7oD4qBTWUPQirDKMZ7rIMsshE5V1LOhdbaGLNN48q4so9CwTioy02TQaQFnd+NzVcNNi3u3pXMeDENdpi4AEhISjL6Lpk6d+shd6/V6jB07Fi+88ILhQqLk5GTI5XI4Ozsb1fX09ERycrKhzv1JT/H64nVlJaF/ZcDe3h7+/v4AgLVr16JZs2aIjo7GsGHDsHHjRuTn5xvN6RFFEXq9HhcvXkT9+vVRv359/PPPP0+0bxsbG7Rt2xZt27bF5MmTMW/ePERFRWHy5Mll7p57UGndiVWVYAXYyKU1gfLxRETOv4XnO2VgYh9/pCRUj39Lqnx5jR2RsNi4t9n98xso9FIi/XUPaF3lEGUCbM9mI+c5ZwCATWI+bO4UIj+gaKheE2CPGttTIMsoNAxx2Z7Ogs7WCgW1pDMMrS20wqVTdmjxYpbhlgiCIKL5i9nYEeNq4egqjzkfWeHk5AQnp7LfriQyMhJnzpzBH3/8YVoAT0hSPT73s7KywrRp0zBjxgzk5eUhOjoa77//PuLi4gzLyZMn0bZtW6xduxYA8NZbb+HixYv44YcfSrQniqKh664sgoKCoNVqS1xO/zQYMiURjVtnw7O2Br4N8jBkSiKaBmdj/1YXS4dWpYxacAuv9LqHRZE+yMu2Qg33QtRwL4RcyQTxQUo7Heo2ykPdRkVXvKm9C1C3UR7ca0ljZopoK0OBt63RIiqsoHMoKtfbyZD5sgtcv7kF5dksyONz4fH5DeQH2EHzv8Qnt6kjCmor4bHqBuTX82B7MhOu3yUjs4MbYCOtr4KtX7ih81tpCOmbBm//fIxedBNKOz32bOJnVEUbNWoUdu7cif3796N27X9vT6FWq1FQUID09HSj+ikpKYaLfdRqdYmrvIpfl+eCIEn1+Dyob9++mDhxIlauXInjx49jw4YNaNCggVGd/v37IyoqCvPmzUO/fv2wbds29O/fHzNmzEDHjh3h7u6O06dPY9myZRg9ejR69OhRYj8vv/wy+vfvj1atWsHV1RXnzp3DtGnT0L59+3JlydWFs5sWEz+5DhcPLXKzZLh6Xonpb9XD8YPmm9j9NHh98F0AwEdbrxiVfzTWG3u38AP4fvWb5eHD7/89T+98kAgA2LO5BpaOq2OpsKqUuwNrAVYC1MuvQdD+7waGQ+6775GVgOQJdeG2NgG1Zl+EqLBC1ksuSOtb03JBW8hvO2pA5arDoInJqOGuRfxZW0wP80P6HZvHb/y0qOSrukRRxOjRo7Ft2zYcOHAAfn5+RutbtmwJGxsb7Nu3zzAF5cKFC7hx4waCg4MBAMHBwZg/fz5SU1Ph4VF0Vd7evXvh5OSEoKCgMsci6cTH2toao0aNwtSpU+Hr61si6QGAnj17YtSoUdi1axe6deuGjRs34osvvsDatWsxf/58WFtbIyAgAIMGDUJoaGip+wkNDcX69esxbdo05ObmwsvLC6+99hpmzZpV0YdoEcsm8IuoLEK9mlk6hGrjVKwDz9cDEmcGGL0W5Va4M6S2cbLzAK27HMmT61V0aNXCjnVu2LHOzdJhWFRlPl09MjISGzduxA8//ABHR0fDnByVSgVbW1uoVCoMGzYM48ePh4uLC5ycnDB69GgEBwejTZs2AICOHTsiKCgIAwcOxJIlS5CcnIwZM2YgMjKyXNM+BPFJZ+xSlZOZmQmVSoWXhR6wFiT0y+VJ8M+ezOjKxuaWDqFaqPdWnKVDqNK0YiEO4AdkZGRU2GhA8fdEy37zYW1j2twubWE+jm2ZXqZ4BaH0x6KsW7cOgwcPBlB0A8P3338f3377LTQaDUJDQ7Fq1SqjYazr169j5MiROHDgAOzt7REeHo5FixbB2rrs/TiS7vEhIiKSJFE0/QdgObYvSx+LUqnEypUrsXLlyofW8fHxwa5du8q839Iw8SEiIpIYc17VVd1Iayo/ERERSRp7fIiIiKSmkq/qqkqY+BAREUmMoC9aTG2jOuJQFxEREUkGe3yIiIikhkNdREREJBVSvqqLiQ8REZHUVPJ9fKoSzvEhIiIiyWCPDxERkcRwqIuIiIikQ8KTmznURURERJLBHh8iIiKJ4VAXERERSQev6iIiIiJ6+rHHh4iISGI41EVERETSwau6iIiIiJ5+7PEhIiKSGA51ERERkXToxaLF1DaqISY+REREUsM5PkRERERPP/b4EBERSYwAM8zxMUsklY+JDxERkdTwzs1ERERETz/2+BAREUkML2cnIiIi6eBVXURERERPP/b4EBERSYwgihBMnJxs6vaWwsTnKSTIZBAEmaXDqNJErdbSIdBTpF7YSUuHUC1c/baZpUOo0vS5+cDQHyppZ/9bTG2jGuJQFxEREUkGe3yIiIgkhkNdREREJB0SvqqLiQ8REZHU8M7NRERERE8/9vgQERFJDO/cTERERNLBoS4iIiKipx97fIiIiCRG0BctprZRHTHxISIikhoOdRERERE9/djjQ0REJDW8gSERERFJhZQfWcGhLiIiIpIMJj5ERERSUzy52dSlHH7//Xe8/vrr8PLygiAI2L59+wMhiZg1axZq1qwJW1tbhISE4NKlS0Z10tLSEBYWBicnJzg7O2PYsGHIzs4uVxxMfIiIiKRGBKA3cSnnSFdOTg6aNWuGlStXlrp+yZIlWLFiBdasWYMjR47A3t4eoaGhyM/PN9QJCwvD2bNnsXfvXuzcuRO///47hg8fXq44OMeHiIhIYiwxx6dz587o3LlzqetEUcTy5csxY8YMdO/eHQDw9ddfw9PTE9u3b8ebb76J8+fPY/fu3fj777/RqlUrAMCnn36KLl264KOPPoKXl1eZ4mCPDxERET2xzMxMo0Wj0ZS7jatXryI5ORkhISGGMpVKhdatWyM2NhYAEBsbC2dnZ0PSAwAhISGwsrLCkSNHyrwvJj5ERERSI8IMc3yKmvL29oZKpTIsCxcuLHc4ycnJAABPT0+jck9PT8O65ORkeHh4GK23traGi4uLoU5ZcKiLiIhIasx45+aEhAQ4OTkZihUKhWntVjD2+BAREdETc3JyMlqeJPFRq9UAgJSUFKPylJQUwzq1Wo3U1FSj9VqtFmlpaYY6ZcHEh4iISGpMvaKreDETPz8/qNVq7Nu3z1CWmZmJI0eOIDg4GAAQHByM9PR0HDt2zFDnv//9L/R6PVq3bl3mfXGoi4iISGIscVVXdnY2Ll++bHh99epVxMXFwcXFBXXq1MHYsWMxb948BAQEwM/PDzNnzoSXlxd69OgBAGjYsCE6deqEiIgIrFmzBoWFhRg1ahTefPPNMl/RBTDxISIiokpw9OhRtG/f3vB6/PjxAIDw8HDExMRg0qRJyMnJwfDhw5Geno4XX3wRu3fvhlKpNGyzYcMGjBo1Cq+++iqsrKzQu3dvrFixolxxMPEhIiKSGjNObi6rl19+GeIjthEEAVFRUYiKinpoHRcXF2zcuLFc+30QEx8iIiKpsUDiU1VwcjMRERFJBnt8iIiIpEbCPT5MfIiIiKRGD0AwQxvVEBMfIiIiibHE5exVBef4EBERkWSwx4cqhK29DoMmJOL50HQ4uxXiyhk7rJnjjYun7C0dWpXxxqgUvNAlA97+GhTkW+HcUTtEz6+Jm1eUj99YYhq3zkbfd28joEkuXNVazBnqi9jdKkuHVaX1i0zBsGlJ2PaVG9bMrm3pcCxC9UMKXDYlI6OTG9LCawEAhAI9XL5JhH1sOoRCEXnNHHFnSC3onW0M2/n1P1mirdTRdZDzfI1Ki73CSXiOj+R6fG7fvo2RI0eiTp06UCgUUKvVCA0NxZ9//gkA8PX1hSAIJZZFixYBAHbt2gW5XI7jx48btbt06VK4ubkZnhA7ePBgw90mH7ffAwcOlLrP+5cDBw5Uyvkxl7FLruOZtpn4cKwv3ukQhOMHnbBw40W4ehZYOrQqo2lwDn6MccPY1wIw9c26kFmLWPBtPBS2OkuHVuUo7fSIP6vEZ9Ok+QVeXvWb5aLrgLuIPyfdJFp+JReO+9KgqWN8Dlz+LxF2xzOROsYHSbPqQXavEJ7LrpXY/vY73rixOsiw5LZ6yhJtvWiepRqSXI9P7969UVBQgPXr16Nu3bpISUnBvn37cPfuXUOdqKgoREREGG3n6OgIAOjSpQsGDRqEQYMG4dixY1AoFDh37hxmzJiBmJiYhz4o7VH77dSpE5KSkgx1x4wZg8zMTKxbt85Q5uLiYs7TUKHkCj1e7HwPH7ztjzN/FZ23b5Z5oXVIBl4beBvrP6pl4QirhulhdY1eLx1bB1vOnEVA0zycOeJgoaiqpqP7nXB0v9PjKxKUdjpM/uw6lk/yRv/3ki0djkUI+Tp4fHYddyJqw3nbvw+9FHJ1cNyfhtTRdZDfuOiz6c4Ib9SecAGKSznQBPzbI623k0F3Xy8QPT0klfikp6fj4MGDOHDgANq1awcA8PHxwXPPPWdUz9HR8ZFPel22bBmaNGmC2bNnY968eQgPD8frr7+ON95444n3e//+bG1todFoyvW02apEZi1CZg0UaIwvGSjIF9Do2WwLRVX12TsV9fRkpcssHAlVZ6MW3MRf+5xw4qCjZBMf17W3kNvCCflNHIH7Eh9FfC4EnWhIegCgsJYSWjcbKC7lGiU+rutuwu2LBGg95ch81RXZL7sAgqmXQVUhEh7qklTi4+DgAAcHB2zfvh1t2rSBQqF4onYcHR2xdu1ahIaG4urVq0hISMDu3bsrfL8P0mg00Gg0hteZmZlmaddUeTkynDtqj7feS8KNy0qk37bBy93T0OCZHCRdM8+xP20EQcQ7H9zCmb/scP2CraXDoWqqXbd78G+ch9Fd61s6FIuxP3QPimt5SJwXUGKdLEML0VqA3t74x4VOZQ1ZeqHh9b2+auQ1coAoF2B7Ohuu627BSqNHZif3Co+/8pgh8UH1THwkNcfH2toaMTExWL9+PZydnfHCCy9g2rRpOHXqlFG9yZMnG5KV4uXgwYNGdV555RX06dMHW7ZswYoVK+Dq6mryfstr4cKFUKlUhsXb29uk9szpw3F+gABs/Ps0frx8HN2HpOK3H1yg1z9Fv5jMaNSCW/BpkI+FI30sHQpVU+5eBRgZdQuLR/ugUCOpj3YD2d0CuK5PRGpkHYjyJz8H6b08oQm0R4GfHTK6eSDjdQ+ofrxtxkjJkiT37ujduzcSExOxY8cOdOrUCQcOHMAzzzyDmJgYQ52JEyciLi7OaGnVqpVRO7du3cLu3bthZ2dXIil60v2W19SpU5GRkWFYEhISnrgtc0u6rsCkfoHoHtgcA9s0xZhuDSGzEZF8Q27p0KqcyPk30bpDJib1qYc7STw/9GT8m+SihrsWK3dfwK7rcdh1PQ7Nns9B96F3sOt6HKysquev8/JQxOdBlqlFrWkX4Rt2Er5hJ2F7PgdOv9yBb9hJ6FTWELQirHKMLyCQZWgfOZ9HU88O1mmFQGE1vWNfaYqHukxdqiFJDXUVUyqV6NChAzp06ICZM2fi7bffxuzZszF48GAAgJubG/z9/R/ZRkREBFq2bInp06ejQ4cO6NOnj2H+zpPut7wUCoXZhs0qiiZPBk2eDA4qLVq+lInohZzY/C8RkfNv4flOGZjYxx8pCVX735Kqtrg/HDH8lUCjsvc/voGEK0psWekhid7WvMYOuLnEeJjPfU0CCr2USO/mDq2rHKJMgPJMFnJbOwMAbBLzYX2nEJoAu4e2K7+eB529DLB5ivoK9CJMHqriVV3VV1BQELZv317m+l999RX++OMPnD59Gj4+Phg5ciSGDh2KU6dOwd6+7PepKe9+q5OWL2UAAnAzXgkvXw3ennYTCVeU2LPFzdKhVRmjFtxC+573MGeIH/KyrVDDvWiOQU6WDAX5T9EHrBko7XTw8vv3Vghq7wLUbZSHrHQZbt9iLxlQNLfuwflh+blWyLpXsvxpJdrKUOhtfKx6hRV0Dv+WZ7V3ges3idA7WENvawXXmFvID7AzTGy2PZYBWYYWmgA7iDZWsD2dBecfUpHR9Wma3yNtkkp87t69i759+2Lo0KFo2rQpHB0dcfToUSxZsgTdu3c31MvKyjLcj6eYnZ0dnJyccP36dYwfPx4fffQRfHyK5mMsXrwYP//8M6ZMmYJPP/30iff7NLFz0mHI5FtwUxciO0OGP3bVQMyHtaDTPv2/Osvq9cFFt1D4aOsVo/KPxnpj75bqc/uCylC/WR4+/P7f8/TOB4kAgD2ba2DpuDqWCouqobSBXoAAeCy7BkErIq+pI+4Ova8nWibAac8d2PxfASAChWo50gZ4IeuVp+w9KeqLFlPbqIYEUaymg3RPQKPRYM6cOdizZw+uXLmCwsJCeHt7o2/fvpg2bRpsbW3h6+uL69evl9h2xIgRWL16NTp06ACZTIZffvnFaP0ff/yBl19+Gfv27UO7du0wePBgpKenY/v27WXa7/3u37Y8MjMzoVKp0N66N6wF3n/iUUSt1tIh0NPkabrMuQJd3djU0iFUafrcfFwdOh8ZGRlwcqqY+1YVf0+EeI+EtZVpw+tavQa/Jqyu0HgrgqQSn6cdE5+yY+JDZsXEp0yY+DxapSY+td4xT+Jza021S3w4kYCIiIgkQ1JzfIiIiAi8czMRERFJiAgzJD5miaTScaiLiIiIJIM9PkRERFLDoS4iIiKSDL0egIn34dFXz/v4cKiLiIiIJIM9PkRERFLDoS4iIiKSDAknPhzqIiIiIslgjw8REZHU6EWYfCMeffXs8WHiQ0REJDGiqIdo4tPVTd3eUpj4EBERSY0omt5jwzk+RERERFUbe3yIiIikRjTDHJ9q2uPDxIeIiEhq9HpAMHGOTjWd48OhLiIiIpIM9vgQERFJDYe6iIiISCpEvR6iiUNd1fVydg51ERERkWSwx4eIiEhqONRFREREkqEXAUGaiQ+HuoiIiEgy2ONDREQkNaIIwNT7+FTPHh8mPkRERBIj6kWIJg51iUx8iIiIqFoQ9TC9x4eXsxMRERE91MqVK+Hr6wulUonWrVvjr7/+qvQYmPgQERFJjKgXzbKUx+bNmzF+/HjMnj0bx48fR7NmzRAaGorU1NQKOsrSMfEhIiKSGlFvnqUcPv74Y0RERGDIkCEICgrCmjVrYGdnh7Vr11bQQZaOc3yeIsUTzbRioYUjqfpEUWvpEOipIlg6gGpBn5tv6RCqNH2eBkDlTBrWotDk+xdqUfRdk5mZaVSuUCigUCiMygoKCnDs2DFMnTrVUGZlZYWQkBDExsaaFkg5MfF5imRlZQEADup2WDgSIompnhe3VL6h2y0dQbWQlZUFlUpVIW3L5XKo1Wr8kbzLLO05ODjA29vbqGz27NmYM2eOUdmdO3eg0+ng6elpVO7p6Yl//vnHLLGUFROfp4iXlxcSEhLg6OgIQagav0AzMzPh7e2NhIQEODk5WTqcKovn6fF4jsqG56lsquJ5EkURWVlZ8PLyqrB9KJVKXL16FQUFBWZpTxTFEt83D/b2VDVMfJ4iVlZWqF27tqXDKJWTk1OV+XCpynieHo/nqGx4nsqmqp2niurpuZ9SqYRSqazw/dzPzc0NMpkMKSkpRuUpKSlQq9WVGgsnNxMREVGFksvlaNmyJfbt22co0+v12LdvH4KDgys1Fvb4EBERUYUbP348wsPD0apVKzz33HNYvnw5cnJyMGTIkEqNg4kPVSiFQoHZs2dX+TFfS+N5ejyeo7LheSobnqfK98Ybb+D27duYNWsWkpOT0bx5c+zevbvEhOeKJojV9WEbREREROXEOT5EREQkGUx8iIiISDKY+BAREZFkMPEhIiIiyWDiQwCAwYMHo0ePHob/FwQBixYtMqqzffv2Enfo/PLLL9GsWTM4ODjA2dkZLVq0wMKFC0tt934HDhyAIAhIT08HAMTExMDZ2dmojq+vLwRBeOgyePBgUw/7iSQkJGDo0KHw8vKCXC6Hj48PxowZg7t37+LatWuPjFkQBMTExJQ4/vv5+vpi+fLlRq9La6f43+fBfbq4uKBdu3Y4ePCgycda/LcgCALkcjn8/f0RFRUFrVZrOIbixd3dHV26dMHp06fLdc7u9/LLL5d6rFrtv89WGzFiBGQyGb777rsS+5kzZw4EQcA777xjVB4XFwdBEHDt2jWjcxYXF2fY5lFLSEgIQkNDS+xv1apVcHZ2xs2bN006tzY2NvDz88OkSZOQn2/8PKubN29CLpejcePGpbYjiiK++OILtG7d2vA+bNWqFZYvX47c3FzDeWnevLlhm9zcXEydOhX16tWDUqmEu7s72rVrhx9++KHMf8OV6fbt2xg5ciTq1KkDhUIBtVqN0NBQ/PnnnwAe/x7ZtWsX5HI5jh8/btTu0qVL4ebmhuTkZAAlP68etd8H//5LWw4cOFAp54fKh5ezU6mUSiUWL16MESNGoEaNGqXWWbt2LcaOHYsVK1agXbt20Gg0OHXqFM6cOWOWGP7++2/odDoAwKFDh9C7d29cuHDBcJdVW1tbs+ynPOLj4xEcHIz69evj22+/hZ+fH86ePYuJEyfi559/RmxsLJKSkgz1P/roI+zevRu//vqroUylUuHIkSPl2m9UVBQiIiKMyhwdHY1e//rrr2jUqBHu3LmD+fPn47XXXsPFixdNvlS0U6dOWLduHTQaDXbt2oXIyEjY2NgYbjpW/G+SmJiIiRMnomvXrrh8+TLkcjmAx5+zw4cPw8XFxbC/iIgIREVFGcVgbV30UZWbm4tNmzZh0qRJWLt2Lfr27VsiXqVSiejoaLz//vsICAh47PFNmDDBKFF69tlnMXz4cKPzXVhYiCZNmuDzzz/HiBEjAABXr17FpEmTsHr16ie+Y3rxuS0sLMSxY8cQHh4OQRCwePFiQ52YmBj069cPv//+O44cOYLWrVsbtTFw4EBs3boVM2bMwGeffQZ3d3ecPHkSy5cvh6+vb6k/PN555x0cOXIEn376KYKCgnD37l0cOnQId+/ehbe3d5n+hitT7969UVBQgPXr16Nu3bpISUnBvn37jBLnR71HunTpgkGDBmHQoEE4duwYFAoFzp07hxkzZiAmJuahdw5+1H47depkdJ7GjBmDzMxMrFu3zlB2/981VSEikSiK4eHhYvfu3Q3//9prr4kNGjQQJ06caKizbds28f4/me7du4uDBw8uc7v3279/vwhAvHfvniiKorhu3TpRpVI9tJ0H61tKp06dxNq1a4u5ublG5UlJSaKdnZ34zjvvGJXPnj1bbNasWYl2HnU8Pj4+4rJlyx76+kFXr14VAYgnTpwwlJ06dUoEIP7www9lOayHKu3fr0OHDmKbNm1KPYYdO3aIAMSTJ08ayspzztq1ayeOGTPmofHExMSIbdq0EdPT00U7Ozvxxo0bRuuLz3eHDh3Evn37GspPnDghAhCvXr0qimLp56zYw853TEyM6ODgIMbHx4t6vV5s37692LNnz4fG+jilndtevXqJLVq0MLzW6/Vi3bp1xd27d4uTJ08WIyIijOpv3rxZBCBu3769RPt6vV5MT08XRbHk36FKpRJjYmLKFOfD/oYry71790QA4oEDBx5a53HvEVEUxczMTNHHx0ecPHmyWFhYKLZq1crob0QUjf9NyrLfh21LVRuHuqhUMpkMCxYswKeffvrQbny1Wo3Dhw/j+vXrlRydZaSlpeGXX37Bu+++W6K3Sa1WIywsDJs3b4Zo4Vtj5eXl4euvvwYAQ6+LOdna2pb6gMOMjAxs2rTJaL/mPmfR0dEYMGAAVCoVOnfu/NAhl0WLFuH777/H0aNHy3FkjxYeHo5XX30VQ4cOxWeffYYzZ87g888/N1v7Z86cwaFDh4z+zfbv34/c3FyEhIRgwIAB2LRpE3JycgzrN2zYgMDAQHTv3r1Ee4IgPLRnRq1WY9euXcjKyjJb/BXFwcEBDg4O2L59OzQazRO34+joiLVr12Lp0qUICwtDQkICVq9eXeH7paqHiQ89VM+ePdG8eXPMnj271PWzZ8+Gs7MzfH19ERgYiMGDB2PLli3Q6/WVHGnluHTpEkRRRMOGDUtd37BhQ9y7dw+3b98uc5u1a9c2fMAWLzdu3ChRb/LkySXqPTiH5/nnn4eDgwPs7e3x0UcfoWXLlnj11VfLd5CPIIoifv31V/zyyy945ZVXShyDs7MzNm7ciG7duqFBgwYAnuycrVq1yug433//fUNbhw8fxhtvvAEAGDBgANatW1dq0vTMM8+gX79+mDx5stmOHwC++OILnDlzBmPHjsUXX3wBd3d3k9rbuXMnHBwcoFQq0aRJE6SmpmLixImG9dHR0XjzzTchk8nQuHFj1K1b12hu06VLlxAYGPhEx3Ho0CG4urri2Wefxbhx4wzzZaoaa2trxMTEYP369XB2dsYLL7yAadOm4dSpU0b1yvIeeeWVV9CnTx9s2bIFK1asgKurq8n7peqHiQ890uLFi7F+/XqcP3++xLqaNWsiNjYWp0+fxpgxY6DVahEeHo5OnTo9tckPALP26Bw8eBBxcXFGi5eXV4l6EydOLFGvVatWRnU2b96MEydO4Pvvv4e/vz9iYmJgY2Njcoz3fzl37twZb7zxBubMmWN0DMeOHUNMTAzq16+PNWvWlGijPOcsLCzM6DinTp0KoGhOWWhoKNzc3AAUzdvIyMjAf//731LbmTdvHg4ePIg9e/aU42gfzcPDAyNGjEDDhg1LnTtTXu3bt0dcXByOHDmC8PBwDBkyBL179wYApKenY+vWrRgwYICh/oABAxAdHW14/aR/iy+99BLi4+Oxb98+9OnTB2fPnkXbtm0xd+5c0w6ogvTu3RuJiYnYsWMHOnXqhAMHDuCZZ54x6vEry3vk1q1b2L17N+zs7Mo0+b8s+6Xqh4kPPdJLL72E0NBQw5dPaRo3box3330X33zzDfbu3Yu9e/fit99+AwA4OTkhIyOjxDbp6emQyWSwt7evsNjNzd/fH4IglJoEAsD58+dRo0aNcvUC+Pn5wd/f32gpnsh7Pzc3txL1Hhw68vb2RkBAAHr27IkFCxagZ8+eZumiL/5yvnTpEvLy8rB+/Xqjfzc/Pz8EBgYiPDwcb7/9tqFHBniyc6ZSqYyO083NDTqdDuvXr8dPP/0Ea2trWFtbw87ODmlpaVi7dm2pbderVw8RERGYMmWKWZPV4v2bg729Pfz9/dGsWTOsXbsWR44cMSQ2GzduRH5+Plq3bm3Y5+TJk/HHH3/g4sWLAID69evjn3/+eaJ929jYoG3btpg8eTL27NmDqKgozJ07t9RhzKpAqVSiQ4cOmDlzJg4dOoTBgwcb9UaX5T0SERGBli1bYufOnVi9erXhc8qU/VL1w8SHHmvRokX48ccfERsb+9i6QUFBAGCYhxAYGIizZ8+W+AI+fvw4/Pz8zNIjUVlcXV3RoUMHrFq1Cnl5eUbrkpOTsWHDBrzxxhslLvm3hD59+sDa2hqrVq0yua3iL+c6deo89gs/MjISZ86cwbZt2wCY75wVz0c5ceKE0S/6b7/9Flu3bi31tgAAMGvWLFy8eNEw96gqs7KywrRp0zBjxgzk5eUZrky7/3hPnjyJtm3bGpK9t956CxcvXsQPP/xQoj1RFEv90fEwQUFB0Gq1JS6nr6qCgoKM5js9zldffYU//vgD0dHRaN++PUaOHImhQ4eWq40n2S9VPUx86LGaNGmCsLAwrFixwqh85MiRmDt3Lv78809cv34dhw8fxqBBg+Du7m641DksLAyCIBguI718+TLWrl2L5cuXG+ZuFNPpdCW6qh/WU2Apn332GTQaDUJDQ/H7778jISEBu3fvRocOHVCrVi3Mnz+/QvablZWF5ORkoyUzM/Oh9QVBwHvvvYdFixYZ7uVSGezs7BAREYHZs2cbelnMcc6io6PRtWtXNGvWDI0bNzYs/fr1g7OzMzZs2FDqdp6enhg/fnyJv92qqm/fvpDJZFi5ciWOHz+Ot99+2+h4GzdujP79+2P9+vXQarXo168f3njjDfTv3x8LFizA0aNHcf36dezcuRMhISHYv39/qft5+eWX8fnnn+PYsWO4du0adu3ahWnTpqF9+/aG20VUFXfv3sUrr7yCb775BqdOncLVq1fx3XffYcmSJUaTuh/1Hrl+/TrGjx+Pjz76CD4+PgCKhvEFQcCUKVNM2i9VP0x8qEyioqJKzNsJCQnB4cOH0bdvX9SvXx+9e/eGUqnEvn37DJMGnZ2dcfDgQRQWFqJbt25o3rw5VqxYgY8//thwP5Ri2dnZaNGihdHy+uuvV9oxlkVAQACOHj2KunXrol+/fqhXrx6GDx+O9u3bIzY2tsLu2zFr1izUrFnTaJk0adIjtwkPD0dhYSE+++yzConpYUaNGoXz588bJuGaes5SUlLw008/Gea+3M/Kygo9e/Y0mvfyoAkTJsDBwcG0g6ok1tbWGDVqFKZOnQpfX1/DJPH79ezZE6mpqdi1axcEQcDGjRvx8ccfY/v27WjXrh2aNm2KOXPmoHv37qXedBEAQkNDsX79enTs2BENGzbE6NGjERoaii1btlT0IZabg4MDWrdujWXLluGll15C48aNMXPmTERERBj9bT/sPSKKIoYNG4bg4GAMHz7cUN/Ozg4xMTEPHfIq636p+hFES197S0RERFRJ2ONDREREksHEh4iIiCSDiQ8RERFJBhMfIiIikgwmPkRERCQZTHyIiIhIMpj4EBERkWQw8SEiIiLJYOJDRGY1ePBgoyeXv/zyyxg7dmylx3HgwAEIgvDQ53gBRY/22L59e5nbnDNnDpo3b25SXNeuXYMgCIiLizOpHSJ6Mkx8iCRg8ODBEAQBgiBALpfD398fUVFR0Gq1Fb7vrVu3Yu7cuWWqW5ZkhYjIFI9+1DIRPTU6deqEdevWQaPRYNeuXYiMjISNjQ2mTp1aom5BQQHkcrlZ9ltRzy8jInoS7PEhkgiFQgG1Wg0fHx+MHDkSISEh2LFjB4B/h6fmz58PLy8vBAYGAgASEhIMT0B3cXFB9+7dce3aNUObOp0O48ePh7OzM1xdXQ0Phbzfg0NdGo0GkydPhre3NxQKBfz9/REdHY1r166hffv2AIAaNWpAEAQMHjwYAKDX67Fw4UL4+fnB1tYWzZo1w3/+8x+j/ezatQv169eHra0t2rdvbxRnWU2ePBn169eHnZ0d6tati5kzZ6KwsLBEvc8//xze3t6ws7NDv379kJGRYbT+q6++QsOGDaFUKtGgQQOsWrWq3LEQUcVg4kMkUba2tigoKDC83rdvHy5cuIC9e/di586dKCwsRGhoKBwdHXHw4EH8+eefcHBwQKdOnQzbLV26FDExMVi7di3++OMPpKWlYdu2bY/c76BBg/Dtt99ixYoVOH/+PD7//HM4ODjA29sb33//PQDgwoULSEpKwieffAIAWLhwIb7++musWbMGZ8+exbhx4zBgwADDU7UTEhLQq1cvvP7664iLi8Pbb7+NKVOmlPucODo6IiYmBufOncMnn3yCL7/8EsuWLTOqc/nyZWzZsgU//vgjdu/ejRMnTuDdd981rN+wYQNmzZqF+fPn4/z581iwYAFmzpyJ9evXlzseIqoAIhE99cLDw8Xu3buLoiiKer1e3Lt3r6hQKMQJEyYY1nt6eooajcawzf/93/+JgYGBol6vN5RpNBrR1tZW/OWXX0RRFMWaNWuKS5YsMawvLCwUa9eubdiXKIpiu3btxDFjxoiiKIoXLlwQAYh79+4tNc79+/eLAMR79+4ZyvLz80U7Ozvx0KFDRnWHDRsm9u/fXxRFUZw6daoYFBRktH7y5Mkl2noQAHHbtm0PXf/hhx+KLVu2NLyePXu2KJPJxJs3bxrKfv75Z9HKykpMSkoSRVEU69WrJ27cuNGonblz54rBwcGiKIri1atXRQDiiRMnHrpfIqo4nONDJBE7d+6Eg4MDCgsLodfr8dZbb2HOnDmG9U2aNDGa13Py5ElcvnwZjo6ORu3k5+fjypUryMjIQFJSElq3bm1YZ21tjVatWpUY7ioWFxcHmUyGdu3alTnuy5cvIzc3Fx06dDAqLygoQIsWLQAA58+fN4oDAIKDg8u8j2KbN2/GihUrcOXKFWRnZ0Or1cLJycmoTp06dVCrVi2j/ej1ely4cAGOjo64cuUKhg0bhoiICEMdrVYLlUpV7niIyPyY+BBJRPv27bF69WrI5XJ4eXnB2tr47W9vb2/0Ojs7Gy1btsSGDRtKtOXu7v5EMdja2pZ7m+zsbADATz/9ZJRwAEXzlswlNjYWYWFh+OCDDxAaGgqVSoVNmzZh6dKl5Y71yy+/LJGIyWQys8VKRE+OiQ+RRNjb28Pf37/M9Z955hls3rwZHh4eJXo9itWsWRNHjhzBSy+9BKCoZ+PYsWN45plnSq3fpEkT6PV6/PbbbwgJCSmxvrjHSafTGcqCgoKgUChw48aNh/YUNWzY0DBRu9jhw4cff5D3OXToEHx8fDB9+nRD2fXr10vUu3HjBhITE+Hl5WXYj5WVFQIDA+Hp6QkvLy/Ex8cjLCysXPsnosrByc1EVKqwsDC4ubmhe/fuOHjwIK5evYoDBw7gvffew82bNwEAY8aMwaJFi7B9+3b8888/ePfddx95Dx5fX1+Eh4dj6NCh2L59u6HNLVu2AAB8fHwgCAJ27tyJ27dvIzs7G46OjpgwYQLGjRuH9evX48qVKzh+/Dg+/fRTw4Thd955B5cuXcLEiRNx4cIFbNy4ETExMeU63oCAANy4cQObNm3ClStXsGLFilInaiuVSoSHh+PkyZM4ePAg3nvvPfTr1w9qtRoA8MEHH2DhwoVYsWIFLl68iNOnT2PdunX4+OOPyxUPEVUMJj5EVCo7Ozv8/vvvqFOnDnr16oWGDRti2LBhyM/PN/QAvf/++xg4cCDCw8MRHBwMR0dH9OzZ85Htrl69Gn369MG7776LBg0aICIiAjk5OQCAWrVq4YMPPsCUKVPg6emJUaNGAQDmzp2LmTNnYuHChWjYsCE6deqEn376CX5+fgCK5t18//332L59O5o1a4Y1a9ZgwYIF5Trebt26Ydy4cRg1ahSaN2+OQ4cOYebMmSXq+fv7o1evXujSpQs6duyIpk2bGl2u/vbbb+Orr77CunXr0KRJE7Rr1w4xMTGGWInIsgTxYbMQiYiIiJ4y7PEhIiIiyWDiQ0RERJLBxIeIiIgkg4kPERERSQYTHyIiIpIMJj5EREQkGUx8iIiISDKY+BAREZFkMPEhIiIiyWDiQ0RERJLBxIeIiIgk4/8BGvp5hQM9Dw0AAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "report = pd.DataFrame(classification_report(test_labels, prediction_scores, output_dict=True))"
   ],
   "metadata": {
    "id": "dndo7QMD2Td9"
   },
   "execution_count": 61,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "report = report.rename(columns={\n",
    "                                '0':'INSULT',\n",
    "                                '1':'OTHER',\n",
    "                                '2':'PROFANITY',\n",
    "                                '3':'RACIST',\n",
    "                                '4':'SEXIST'})\n",
    "report"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "43LMvHsE2TgD",
    "outputId": "0e56d372-2706-47be-9cd4-755557b7a165"
   },
   "execution_count": 62,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               INSULT       OTHER   PROFANITY      RACIST      SEXIST  \\\n",
       "precision    0.905930    0.975255    0.969499    0.913242    0.957447   \n",
       "recall       0.924843    0.941011    0.930962    0.985222    0.961995   \n",
       "f1-score     0.915289    0.957827    0.949840    0.947867    0.959716   \n",
       "support    479.000000  712.000000  478.000000  406.000000  421.000000   \n",
       "\n",
       "           accuracy    macro avg  weighted avg  \n",
       "precision  0.946715     0.944275      0.947758  \n",
       "recall     0.946715     0.948807      0.946715  \n",
       "f1-score   0.946715     0.946108      0.946833  \n",
       "support    0.946715  2496.000000   2496.000000  "
      ],
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       "      <th></th>\n",
       "      <th>INSULT</th>\n",
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       "      <th>PROFANITY</th>\n",
       "      <th>RACIST</th>\n",
       "      <th>SEXIST</th>\n",
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       "      <th>precision</th>\n",
       "      <td>0.905930</td>\n",
       "      <td>0.975255</td>\n",
       "      <td>0.969499</td>\n",
       "      <td>0.913242</td>\n",
       "      <td>0.957447</td>\n",
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       "      <td>0.947758</td>\n",
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       "      <th>recall</th>\n",
       "      <td>0.924843</td>\n",
       "      <td>0.941011</td>\n",
       "      <td>0.930962</td>\n",
       "      <td>0.985222</td>\n",
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       "      <td>0.947867</td>\n",
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       "      <td>0.946108</td>\n",
       "      <td>0.946833</td>\n",
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       "      <td>478.000000</td>\n",
       "      <td>406.000000</td>\n",
       "      <td>421.000000</td>\n",
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       "      <td>2496.000000</td>\n",
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       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-5737ed72-6a14-4184-85fc-898669e3c394 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-5737ed72-6a14-4184-85fc-898669e3c394');\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",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 62
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "vBuNRGnYuC1p"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "ckElQhdf3aXp"
   },
   "execution_count": null,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "colab": {
   "provenance": [],
   "machine_shape": "hm"
  },
  "accelerator": "GPU",
  "gpuClass": "premium"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}