finetune-transformers-with-pytorch.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oaNu3HJtTTo"
      },
      "source": [
        "# Fine-tune 🤗 transformers with PyTorch "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xFqG3VVtxpi"
      },
      "source": [
        "## Installs\n",
        "\n",
        "Installation of **[transformers](https://github.com/huggingface/transformers)** and **[ml_things](https://github.com/gmihaila/ml_things)**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUz_vmR8tyoc",
        "outputId": "42b4a658-fabb-4db0-e5df-bd2cc4b6ba33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git\n",
        "\n",
        "!pip install -q git+https://github.com/gmihaila/ml_things.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 101 kB 11.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 65.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 56.1 MB/s \n",
            "\u001b[?25h  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 53 kB 2.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 11.2 MB 55.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 944 kB 61.5 MB/s \n",
            "\u001b[?25h  Building wheel for ml-things (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install matplotlib 3.1.3"
      ],
      "metadata": {
        "id": "a08TPwdwpPpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip uninstall matplotlib\n",
        "!pip install matplotlib==3.1.3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "iHpUfcw6pO06",
        "outputId": "1ca8eb90-2a08-4555-9593-9b02994b3e9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: matplotlib 3.5.2\n",
            "Uninstalling matplotlib-3.5.2:\n",
            "  Would remove:\n",
            "    /usr/local/lib/python3.7/dist-packages/matplotlib-3.5.2-py3.7-nspkg.pth\n",
            "    /usr/local/lib/python3.7/dist-packages/matplotlib-3.5.2.dist-info/*\n",
            "    /usr/local/lib/python3.7/dist-packages/matplotlib/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axes_grid/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axes_grid1/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axisartist/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/mplot3d/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/tests/*\n",
            "    /usr/local/lib/python3.7/dist-packages/pylab.py\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled matplotlib-3.5.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting matplotlib==3.1.3\n",
            "  Downloading matplotlib-3.1.3-cp37-cp37m-manylinux1_x86_64.whl (13.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.1 MB 31.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib==3.1.3) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib==3.1.3) (1.15.0)\n",
            "Installing collected packages: matplotlib\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ml-things 0.0.1 requires matplotlib>=3.4.0, but you have matplotlib 3.1.3 which is incompatible.\u001b[0m\n",
            "Successfully installed matplotlib-3.1.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vF3UbENTtz2_"
      },
      "source": [
        "## Imports\n",
        "\n",
        "Import necessary libraries\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjr_J342tOPq"
      },
      "source": [
        "import io\n",
        "import os\n",
        "import torch\n",
        "import urllib\n",
        "from tqdm.notebook import tqdm\n",
        "from collections import defaultdict\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn import functional as F\n",
        "from ml_things import plot_dict, plot_confusion_matrix, fix_text\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from transformers import (AutoConfig, \n",
        "                          AutoModelForSequenceClassification, \n",
        "                          AutoTokenizer, AdamW, \n",
        "                          get_linear_schedule_with_warmup,\n",
        "                          set_seed,\n",
        "                          )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Declaration of parameters\n"
      ],
      "metadata": {
        "id": "yO93STIzl2Vi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Declare parameters used in this notebook"
      ],
      "metadata": {
        "id": "HlrGCdd-bC9n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set seed for reproducibility\n",
        "set_seed(123)\n",
        "\n",
        "# Number of training epochs \n",
        "epochs = 5\n",
        "\n",
        "# Number of batch_size - depending on the max sequence length and avaialable GPU memory\n",
        "# For 512 sequence length batch of 10 works without cuda memory issues\n",
        "# For small sequence length one can try batch of 32 or higher\n",
        "batch_size = 32\n",
        "\n",
        "# Padd or truncate text sequences to a specific length\n",
        "# if `None` it will use maximum sequence of word piece tokens allowed by model\n",
        "max_length = None\n",
        "\n",
        "# Look for gpu to use. Will use `cpu` by default if no gpu found\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Name of the transformers model - will use already pretrained model\n",
        "# Path of transformer model - will load your own model from local disk\n",
        "#model_name_or_path = \"monsoon-nlp/hindi-bert\"\n",
        "\n",
        "model_name_or_path = \"cardiffnlp/twitter-xlm-roberta-base\"\n",
        "\n",
        "# Dicitonary of labels and their id - this will be used to convert string labels to number.\n",
        "labels_ids = {'negative': 0, 'neutral': 1, 'positive':2}\n",
        "\n",
        "# How many labels are we using in training\n",
        "# This is used to decide size of classification head\n",
        "n_labels = len(labels_ids)"
      ],
      "metadata": {
        "id": "k10-1eOOl-Uu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78UA1RvdxDTp"
      },
      "source": [
        "## Load the UMSAB dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zB6DIS61xE2j"
      },
      "source": [
        "files = \"\"\"test_labels.txt\n",
        "test_text.txt\n",
        "train_labels.txt\n",
        "train_text.txt\n",
        "val_labels.txt\n",
        "val_text.txt\"\"\".split('\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fetch_data(language, files):\n",
        " dataset = defaultdict(list)\n",
        " for infile in files:\n",
        "   thisdata = infile.split('/')[-1].replace('.txt','')\n",
        "   dataset_url = f\"https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/{language}/{infile}\"\n",
        "   print(f'Fetching from {dataset_url}')\n",
        "   with urllib.request.urlopen(dataset_url) as f:\n",
        "     for line in f:\n",
        "       if thisdata.endswith('labels'):\n",
        "         dataset[thisdata].append(int(line.strip().decode('utf-8')))\n",
        "       else:\n",
        "         dataset[thisdata].append(line.strip().decode('utf-8'))\n",
        " return dataset"
      ],
      "metadata": {
        "id": "MqOZdzvFlI0l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "umsab_dataset = fetch_data('german', files)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOKY9_51m7IN",
        "outputId": "04f53c54-28d5-44d2-f228-17e86378254c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fetching from https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/german/test_labels.txt\n",
            "Fetching from https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/german/test_text.txt\n",
            "Fetching from https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/german/train_labels.txt\n",
            "Fetching from https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/german/train_text.txt\n",
            "Fetching from https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/german/val_labels.txt\n",
            "Fetching from https://raw.githubusercontent.com/cardiffnlp/xlm-t/main/data/sentiment/german/val_text.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_text = umsab_dataset ['train_text']\n",
        "train_labels = umsab_dataset ['train_labels']\n",
        "\n",
        "valid_text = umsab_dataset ['val_text']\n",
        "valid_labels = umsab_dataset ['val_labels']\n",
        "\n",
        "test_text = umsab_dataset ['test_text']\n",
        "test_labels = umsab_dataset ['test_labels']"
      ],
      "metadata": {
        "id": "XYdbfItthDYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdnKsoFbt1Wd"
      },
      "source": [
        "## Define helper class and functions\n",
        "\n",
        "Classes and functions that are used in this notebook:\n",
        "\n",
        "* MyDataset\n",
        "* train\n",
        "* validation"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define `MyDatset` class"
      ],
      "metadata": {
        "id": "jR1SDf1k7q_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)"
      ],
      "metadata": {
        "id": "U4tFUqm6ihFs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define `train` function:**\n",
        "\n",
        "\n",
        "Train a pytorch model on a single pass through the data loader. It will use the global variable `model` which is the transformer model loaded on `_device` that we want to train on.\n",
        "\n",
        "  Arguments:\n",
        "\n",
        "      dataloader (:obj:`torch.utils.data.dataloader.DataLoader`):\n",
        "          Parsed data into batches of tensors.\n",
        "\n",
        "      optimizer_ (:obj:`transformers.optimization.AdamW`):\n",
        "          Optimizer used for training.\n",
        "\n",
        "      scheduler_ (:obj:`torch.optim.lr_scheduler.LambdaLR`):\n",
        "          PyTorch scheduler.\n",
        "\n",
        "      device_ (:obj:`torch.device`):\n",
        "          Device used to load tensors before feeding to model.\n",
        "\n",
        "  Returns:\n",
        "\n",
        "      :obj:`List[List[int], List[int], float]`: List of [True Labels, Predicted\n",
        "        Labels, Train Average Loss].\n"
      ],
      "metadata": {
        "id": "NczDESB_774r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "See `Hugging Face` documentation on [training and fine-tuning transformers](https://huggingface.co/transformers/v3.5.1/training.html) in native PyTorch."
      ],
      "metadata": {
        "id": "RC3rW6h3GkWt"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDEubgJIt23C"
      },
      "source": [
        "def train(dataloader, optimizer_, scheduler_, device_):\n",
        "\n",
        "  # Use global variable for model\n",
        "  global model\n",
        "\n",
        "  # Tracking variables\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "  # Total loss for this epoch\n",
        "  total_loss = 0\n",
        "\n",
        "  # Put the model into training mode\n",
        "  model.train()\n",
        "\n",
        "  # For each batch of training data we run the loop\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "\n",
        "    # Add original labels - use later for evaluation.\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "    \n",
        "    # move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "    \n",
        "    # Always clear any previously calculated gradients before performing a\n",
        "    # backward pass.\n",
        "    model.zero_grad()\n",
        "\n",
        "    # Perform a forward pass (evaluate the model on this training batch)\n",
        "    # This will return the loss (rather than the model output) because we have provided the `labels`.\n",
        "    # The documentation for a bert model function is here: \n",
        "    # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
        "    outputs = model(**batch)\n",
        "\n",
        "    # The call to `model` always returns a tuple, so we need to pull the loss value out of the tuple along with the logits. \n",
        "    # We will use logits later to calculate training accuracy.\n",
        "    #loss, logits = outputs[:2] # UNCOMMENT THIS IF YOU DON'T WANT TO CALCULATE THE LOSS YOURSELF\n",
        "\n",
        "    # Alternatively, you can just get the logits and calculate the loss yourself as follows\n",
        "    labels = batch.pop(\"labels\")\n",
        "    loss = F.cross_entropy(outputs.logits, labels)\n",
        "    logits = outputs.logits\n",
        "\n",
        "\n",
        "    # Accumulate the training loss over all of the batches so that we can calculate the average loss at the end. \n",
        "    # `loss` is a Tensor containing a single value; the `.item()` function just returns the Python value from the tensor.\n",
        "    total_loss += loss.item()\n",
        "\n",
        "    # Perform a backward pass to calculate the gradients\n",
        "    loss.backward()\n",
        "\n",
        "    # Clip the norm of the gradients to 1.0. This is to help prevent the \"exploding gradients\" problem.\n",
        "    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "    # Update parameters and take a step using the computed gradient.\n",
        "    # The optimizer dictates the \"update rule\"--how the parameters are modified based on their gradients, the learning rate, etc.\n",
        "    optimizer.step()\n",
        "\n",
        "    # Update the learning rate\n",
        "    scheduler.step()\n",
        "\n",
        "    # Move logits and labels to CPU\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "\n",
        "    # Convert these logits to list of predicted labels values\n",
        "    predictions_labels += logits.argmax(axis=-1).flatten().tolist()\n",
        "\n",
        "  # Calculate the average loss over the training data\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "  \n",
        "  # Return all true labels and prediction for future evaluations\n",
        "  return true_labels, predictions_labels, avg_epoch_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define `validation` function:**\n",
        "\n",
        "Validation function to evaluate model performance on a separate set of data. This function will return the true and predicted labels so we can use later to evaluate the model's performance.\n",
        "\n",
        "  Arguments:\n",
        "\n",
        "    dataloader (:obj:`torch.utils.data.dataloader.DataLoader`):\n",
        "          Parsed data into batches of tensors.\n",
        "\n",
        "    device_ (:obj:`torch.device`):\n",
        "          Device used to load tensors before feeding to model.\n",
        "\n",
        "  Returns:\n",
        "    \n",
        "    :obj:`List[List[int], List[int], float]`: List of [True Labels, Predicted\n",
        "        Labels, Train Average Loss]\n"
      ],
      "metadata": {
        "id": "j9LBGdet8cLH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def validation(dataloader, device_):\n",
        "\n",
        "  # Use global variable for model.\n",
        "  global model\n",
        "\n",
        "  # Tracking variables\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "  #total loss for this epoch\n",
        "  total_loss = 0\n",
        "\n",
        "  # Put the model in evaluation mode (the dropout layers behave differently during evaluation)\n",
        "  model.eval()\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "\n",
        "    # Add original labels\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "\n",
        "    # Move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "\n",
        "    # Telling the model not to compute or store gradients, saving memory and speeding up validation\n",
        "    with torch.no_grad():        \n",
        "\n",
        "        # Forward pass, calculate logit predictions. This will return the logits rather than the loss because we have not provided labels.\n",
        "        # `token_type_ids` is the same as the \"segment ids\", which differentiates sentence 1 and 2 in 2-sentence tasks.\n",
        "        # The documentation for this `model` function is here: \n",
        "        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
        "        outputs = model(**batch)\n",
        "\n",
        "        # The call to `model` always returns a tuple, so we need to pull the loss value out of the tuple along with the logits. \n",
        "        # We will use logits later to to calculate training accuracy.\n",
        "        # loss, logits = outputs[:2] # UNCOMMENT THIS IF YOU DON'T WANT TO CALCULATE THE LOSS YOURSELF\n",
        "\n",
        "        # Alternatively, you can just get the logits and calculate the loss yourself as follows\n",
        "        labels = batch.pop(\"labels\")\n",
        "        loss = F.cross_entropy(outputs.logits, labels)\n",
        "        logits = outputs.logits\n",
        "        \n",
        "        # Move logits and labels to CPU\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "\n",
        "        # Accumulate the training loss over all of the batches so that we can calculate the average loss at the end. \n",
        "        # `loss` is a Tensor containing a single value; the `.item()` function just returns the Python value from the tensor.\n",
        "        total_loss += loss.item()\n",
        "        \n",
        "        # get predicitons to list\n",
        "        predict_content = logits.argmax(axis=-1).flatten().tolist()\n",
        "\n",
        "        # update list\n",
        "        predictions_labels += predict_content\n",
        "\n",
        "  # Calculate the average loss over the training data.\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "\n",
        "  # Return all true labels and prediciton for future evaluations.\n",
        "  return true_labels, predictions_labels, avg_epoch_loss"
      ],
      "metadata": {
        "id": "1H2wtmA6c1cG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7JS0QZ0tLvi"
      },
      "source": [
        "## Load config, tokenizer and the model\n",
        "\n",
        "Loading the three esential parts of pretrained transformers: \n",
        "\n",
        "* configuration, \n",
        "* tokenizer and \n",
        "* model. \n",
        "\n",
        "We also need to load the model to the device we're planning to use (GPU / CPU)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4IPr-6LtZNW",
        "outputId": "3a6e922d-3895-4f4d-b375-6fa264d5c71d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403,
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            "5597d18f9a01409a819c7a41a5530b88"
          ]
        }
      },
      "source": [
        "# Get model configuration\n",
        "print('Loading model configuraiton...')\n",
        "model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_name_or_path, \n",
        "                                          num_labels=n_labels)\n",
        "\n",
        "# Get model's tokenizer\n",
        "print('Loading model tokenizer...')\n",
        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name_or_path)\n",
        "\n",
        "# Get the actual model\n",
        "print('Loading the model...')\n",
        "model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name_or_path, \n",
        "                                                           config=model_config)\n",
        "\n",
        "# Load model to the defined device\n",
        "model.to(device)\n",
        "print('Model loaded to `%s`'%device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model configuraiton...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/652 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06db9cfa7a6e483eb9a0c84fc21f712e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model tokenizer...\n",
            "vocab_file sentencepiece.bpe.model\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c0dcba5fb5843498613071d71e7e12b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tokenizer_file tokenizer.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f880294a06194633935f17764d7492d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "added_tokens_file added_tokens.json\n",
            "special_tokens_map_file special_tokens_map.json\n",
            "tokenizer_config_file tokenizer_config.json\n",
            "Loading the model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30c46ce1e1ef48b4af30f1201f004438"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at cardiffnlp/twitter-xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.decoder.bias', 'lm_head.decoder.weight']\n",
            "- This IS expected if you are initializing XLMRobertaForSequenceClassification 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 XLMRobertaForSequenceClassification 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 XLMRobertaForSequenceClassification were not initialized from the model checkpoint at cardiffnlp/twitter-xlm-roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded to `cuda`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ySH5UUcu4YN"
      },
      "source": [
        "## Data encoding and create DataLoader object\n",
        "\n",
        "Create data encodings"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_encodings = tokenizer(train_text, truncation=True, padding=True)\n",
        "valid_encodings = tokenizer(valid_text, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(test_text, truncation=True, padding=True)\n",
        "\n",
        "train_dataset = MyDataset(train_encodings, train_labels)\n",
        "valid_dataset = MyDataset(valid_encodings, valid_labels)\n",
        "test_dataset = MyDataset(test_encodings, test_labels)"
      ],
      "metadata": {
        "id": "EsN5iXYYn_t0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f82fb52-107b-45c6-d19b-ac0dcbc5e586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create DataLoader objects that will be fed into our model"
      ],
      "metadata": {
        "id": "qJczXnGZseQn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader (train_dataset, batch_size=batch_size, shuffle=True)\n",
        "print('Created `train_dataloader` with %d batches!'%len(train_dataloader))\n",
        "\n",
        "test_dataloader = DataLoader (test_dataset, batch_size=batch_size, shuffle=False)\n",
        "print('Created `test_dataloader` with %d batches!'%len(test_dataloader))\n",
        "\n",
        "valid_dataloader = DataLoader (valid_dataset, batch_size=batch_size, shuffle=False)\n",
        "print('Created `valid_dataloader` with %d batches!'%len(valid_dataloader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLhJ-7UwiFcl",
        "outputId": "f9565da8-9146-46c1-f768-edb0b02f8310"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created `train_dataloader` with 58 batches!\n",
            "Created `test_dataloader` with 28 batches!\n",
            "Created `valid_dataloader` with 11 batches!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTRPqYQQ5TWc"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "**Steps:**\n",
        "\n",
        "1. Create optimizer and scheduler used by PyTorch in training\n",
        "\n",
        "2. Loop through the number of defined epochs and call the **train** and **validation** functions\n",
        "\n",
        "3. Store the outputs, which are similar info after each epoch as in Keras: *train_loss,   val_loss,   train_acc, valid_acc*\n",
        "\n",
        "4. After training, plot train and validation loss and accuracy curves to check how the training went"
      ]
    },
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            "feea3df1aaaa43ed8894d21af5022862",
            "91b4f50ec4af430dbc0c78985eefe949",
            "418ff0f98eec46f181a1bbd5506f3a64"
          ]
        }
      },
      "source": [
        "optimizer = AdamW (model.parameters(),\n",
        "                  lr = 1e-4, # args.learning_rate - default is 5e-5\n",
        "                  eps = 1e-8, # args.adam_epsilon  - default is 1e-8.\n",
        "                  no_deprecation_warning=True\n",
        "                  )\n",
        "\n",
        "# Total number of training steps is number of batches * number of epochs\n",
        "# `train_dataloader` contains batched data so `len(train_dataloader)` gives us the number of batches\n",
        "total_steps = len(train_dataloader) * epochs\n",
        "\n",
        "# Create the learning rate scheduler\n",
        "scheduler = get_linear_schedule_with_warmup (optimizer, \n",
        "                                            num_warmup_steps = 10, # Default value in run_glue.py\n",
        "                                            num_training_steps = total_steps)\n",
        "\n",
        "# Store the average loss after each epoch so we can plot them\n",
        "all_loss = {'train_loss':[], 'val_loss':[]}\n",
        "all_acc = {'train_acc':[], 'val_acc':[]}\n",
        "\n",
        "\n",
        "# Loop through each epoch\n",
        "print('Epoch')\n",
        "for epoch in tqdm (range(epochs)):\n",
        "  print()\n",
        "\n",
        "  print('Training on batches...')\n",
        "  # Perform one full pass over the training set\n",
        "  train_labels, train_predict, train_loss = train (train_dataloader, optimizer, scheduler, device)\n",
        "  train_acc = accuracy_score (train_labels, train_predict)\n",
        "\n",
        "  # Get prediction form model on validation data\n",
        "  print('Validation on batches...')\n",
        "  valid_labels, valid_predict, val_loss = validation (valid_dataloader, device)\n",
        "  val_acc = accuracy_score (valid_labels, valid_predict)\n",
        "\n",
        "  # Print loss and accuracy values to see how training evolves\n",
        "  print(\"  train_loss: %.5f - val_loss: %.5f - train_acc: %.5f - valid_acc: %.5f\"%(train_loss, val_loss, train_acc, val_acc))\n",
        "  print()\n",
        "\n",
        "  # Store the loss values for plotting the learning curve\n",
        "  all_loss['train_loss'].append(train_loss)\n",
        "  all_loss['val_loss'].append(val_loss)\n",
        "\n",
        "  # Store the accuracy values\n",
        "  all_acc['train_acc'].append(train_acc)\n",
        "  all_acc['val_acc'].append(val_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da71a753be624e7196470591e9daed34"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/58 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "350647d5cd624dc5a20423ec3e5ac480"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0b2e61ce8084795bde7844413939e87"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.86235 - val_loss: 0.69828 - train_acc: 0.62425 - valid_acc: 0.71914\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/58 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bf29c1962074eed8c8e73ac8fbed9d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc28576187454bcbb111c1c6884762ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.53634 - val_loss: 0.63294 - train_acc: 0.80587 - valid_acc: 0.75926\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/58 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69934fea5ad54e7ba500c9fece6a1ced"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b44b4d4641044f9895a03ae40e51fcc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.33669 - val_loss: 0.73787 - train_acc: 0.89396 - valid_acc: 0.75309\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/58 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "088b2b86ae944d67805051add2d59760"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f501864ac1ee4ba296498464adc0f1b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.17167 - val_loss: 0.95033 - train_acc: 0.95324 - valid_acc: 0.76235\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/58 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc066bab90de41c19a79eecec6dbfd91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdf8f26f2c294127bbd268a077c07064"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.09160 - val_loss: 1.03445 - train_acc: 0.97934 - valid_acc: 0.75926\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the loss and accuracy curves"
      ],
      "metadata": {
        "id": "9Cl6AZ2X5fNn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss curves\n",
        "plot_dict(all_loss, use_xlabel='Epochs', use_ylabel='Value', use_title='Loss curves', use_linestyles=['-', '--'], magnify=0.2)\n",
        "\n",
        "# Plot the accuracy curves\n",
        "plot_dict(all_acc, use_xlabel='Epochs', use_ylabel='Value', use_title='Accuracy curves', use_linestyles=['-', '--'], magnify=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        },
        "id": "zZdBFXRhz4ec",
        "outputId": "7b013281-fe6e-46c1-96ee-c6be8a66c21b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3888x864 with 1 Axes>"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3888x864 with 1 Axes>"
            ],
            "image/png": 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f9nn5tS8AAAAAAE4ttqxEWr+mvnd6UW2w076TzLDLnYNIe6XJBIcEtlAAQKvVouH6gAED9OGHH6qiokJhYWGSpM8++0wRERHq06fPYeeHhYX5DiedM2eOEhMTdcYZZzR5/d69e7VhwwZddNFFx2cBAAAAAADghLBer/T9d7Jrs2SzV0lbNkherxQeIfUdIHPlBJm0gTJxiYEuFQAASS0crl988cX66KOP9Nxzz+nKK69Ubm6u3nvvPY0bN07BwcG+6+69917169dPd911lyQpNzdXn376qXr27CmPx6OVK1dq6dKl+uUvfym32y1J2r59u958802dd955vl9VmDdvnowxuvTSS1tymQAAAAAA4AjY/Xtl162S1mbJrvtaKt7nDHTpKTNqnLM7vVtvmaBT4sg4AEAr06L/dYqOjtavf/1rTZ8+Xc8884yioqI0ZswYjR492u86r9crr9dbX2RQkL755hvNnz9fXq9XPXr00OOPP66+ffv63VuS3nzzTRUXFys8PFz9+vXTQw89RMN9AAAAAABOAtbjkbZsrN2dniVt3+wMRLWRSRsopWfK9Bso06bp89UAADhZGGutDXQRJ4ucnJxAlxBQrf1wAgCQeC8EAIn3QgDHly3cI5u9yumbvn61VF4quVxS91SZ9EwnVO/cQ8blCnSpfngvBABHa38/PGkONAUAAAAAAKc3W10lfZtd3zs953tnIDZB5szBMmmZUt/+MhFRgS0UAIBjRLgOAAAAAACOmrVWyttV3+pl4xqpqkoKCpJ6pckMvkgmbZCU0knGmECXCwDAcUO4DgAAAAAAmsVWlEsbv6kP1PfkOgNJKTIXjHRavfQ5QyY0LLCFAgBwAhGuAwAAAACAQ7LWSju3y2ZnOb3Tv10neWqk0DAptb/MxWNk0gbKJLUPdKkAALQYwnUAAAAAANCALS2WXbdayl7p9E7fW+gMdOgiM+JKp3d6z34ywcGBLRQAgAAhXAcAAAAAALJej7T9u/pWL1s2SdYrRUTK9B0gpWfKpGXKxMYHulQAAE4KhOsAAAAAALRSdl+Rsys9O0t23SqppFgyRuraS+by62TSM53P3e5AlwoAwEmHcB0AAAAAgFbC1tRI322QzV7p9E7fsdUZaBMjc8aZUlqmTL+BMtFtAlsoAACnAMJ1AAAAAABOY7Ygz2n1sjZL2rBaqiiX3G6pR6rM1Tc6u9M7dpNxuQJdKgAApxTCdQAAAAAATiO2qlLalC2bXRuo5/7gDMQlypw9xAnTU/vLhEcEtlAAAE5xhOsAAAAAAJzCrLVS7k6n1Uv2KmnjWqm6SgoKlvqkywy5RCZtkNSug4wxgS4XAIDTBuE6AAAAAACnGFteJm1Y47R7yc6SCvKcgXYdZH50ibM7vVe6TGhoYAsFAOA0RrgOAAAAAMBJzlor7dha3+rlu/WSxyOFhkt9M2RGXSOTNlAmsV2gSwUAoNUgXAcAAAAA4CRkS/bLrvtaWpslu26VtK/IGejUTWbkGKfVS48+MkHBgS0UAIBWinAdAAAAAICTgPV6pK3f1rd62fatZK0UGS3Tb4CUninTb6BMTFygSwUAACJcBwAAAAAgYOzeAucQ0rVZzi71shLJuKTuvWWumOD0Tu/aU8blDnSpAADgIITrAAAAAAC0EFtTLW1eX787/YdtzkDbOJmB50hpg2T6ZchERge0TgAAcHiE6wAAAAAAnEB2T279QaQb1kiVFZI7SOrZV+aam5zd6R26yhgT6FIBAEAzEK4DAAAAAHAc2cpKadM3zu70tVlSXo4zkJAsc94wmbRMKfUMmbCIwBYKAACOCeE6AAAAAADHwFor7dpR3+plU7ZUUy2FhEh9+ssMv8LZnZ7Unt3pAACcRgjXAQAAAABoJltWKm1YXR+oF+Y7A+07yQy7zAnTe6XJBIcEtlAAAHDCEK4DAAAAAHAY1uuVdmypD9O/2yB5vVJ4hNQ3Q+by8TJpmTLxiYEuFQAAtBDCdQAAAAAAGmGL98lmr5Kys5w/i/c5A116yoy6xumd3r2PTBD/aw0AQGvE3wAAAAAAAJBkPR5p68b6g0i//06yVopqI5M2UErPlOk3QKZNbKBLBQAAJwHCdQAAAABAq2UL82Wza1u9rFstlZdKxiX16CNz1fVO7/TOPWRcrkCXCgAATjKE6wAAAACAVsNWV0vfZjuB+tosKed7ZyAmXmbQ+U6YnpohExkV2EIBAMBJj3AdAAAAAHBas3k59a1eNn4jVVVKQUFSrzSZ8y9yAvWUzjLGBLpUAABwCiFcBwAAAACcVmxFubRxrWz2SidQ35PrDCS1lxk8wgnT+5whExoW2EIBAMApjXAdAAAAAHBKs9ZKO7fXt3rZvE6qqZFCQqXU/jIXj5ZJGyiTlBLoUgEAwGmEcB0AAAAAcMqxpSXS+q+ddi/ZWdLeQmegQxeZ4Vc6u9N79pMJDg5soQAA4LRFuA4AAAAAOOlZr1fa/l19q5ctmyTrlSIiZfoOkNIzZfoNlIlLCHSpAACglSBcBwAAAACclOz+Itnsr6W1WbLrVkkl+yVjpC49ZS6/ViYtU+rWW8btDnSpAACgFSJcBwAAAACcFGxNjbRlQ32rl++3OAPRbWXSB9XuTh8gE902sIUCAACIcB0AAAAAEEC2IK/+INL1q6WKcsnlknr2lbn6Rmd3eqduMi5XoEsFAADwQ7gOAAAAAGgxtrpK2pRdvzt91w5nIC5R5uwfOWF6an+ZiMjAFgoAAHAYhOsAAAAAgBPGWivt3lkbpq+SNn0jVVVJQcFS73SZC0fKpGdK7TrKGBPocgEAAI4Y4ToAAAAA4LiyFWXShjVOoL42SyrIcwbadZC58BJnd3rvdJnQ0MAWCgAAcAwI1wEAAAAAx8RaK/2wrb7Vy+Z1kscjhYZLffvLjLpGJm2gTGK7QJcKAABw3BCuAwAAAACazZbsl12/WqoL1PcVOQMdu8lcPMZp9dIjVSYoOLCFAgAAnCCE6wAAAACAw7Jej7T1W9ns2lYv2zZL1itFRMmkDZTSMmXSBsjExAe6VAAAgBZBuA4AAAAAaJTdW+gcQppdexhpWYlkjNStt8wV453d6V17yrjcgS4VAACgxRGuAwAAAAAkSbamWvpuQ/1BpD9sdQbaxsoMOEdKz5TpmyET1SawhQIAAJwECNcBAAAAoBWze3KdVi/Zq6T1a6TKcsntlnr2kxl7k7M7vWNXGWMCXSoAAMBJhXAdAAAAAFoRW1kpbVpb3zt9905nID5J5ryhMmmZUuoZMmERgS0UAADgJEe4DgAAAACnMWutlPtDfauXTWulmmopOETqc4bMsMucQD05hd3pAAAAzUC4DgAAAACnGVtWKm1YU787vXCPM9C+k8zQy5xWL736yYSEBrZQAACAUxjhOgAAAACc4qzXK+3YKrt2pWx2lvTdBsnrlcIjpL4ZMpdfK5OWKROfFOhSAQAAThuE6wAAAABwCrLF+2TXfS2tzXIC9eJ9zkDnHjKjrnFavXTvIxPE//YBAACcCPwtCwAAAABOAdbjkbZuqm/1sn2zZK0UFS3TL1NKz5RJGyDTJjbQpQIAALQKhOsAAAAAcJKyhflOmJ6dJa1fLZWVSsYlde8tc9WPZdIGSV26y7jcgS4VAACg1SFcBwAAAICThK2uljavk61r9bJzuzMQEy+Teb5zEGlqhkxkVGALBQAAAOE6AAAAAASSzdtV3+plwxqpqlIKCpJ6pcmcN9wJ1FM6yxgT6FIBAABwAMJ1AAAAAGhBtrJC2vhN/e70vF3OQGI7mcEXOa1e+qTLhIUHtlAAAAAcEuE6AAAAAJxA1lop5/v6MP3bbKmmRgoJlVL7y4y4SiZtoExSSqBLBQAAQDMQrgMAAADAcWbLSqT1q51AfW2WtLfAGejQRWb4lU6rl579ZIKDA1soAAAAjhrhOgAAAAAcI+v1St9/V787fctGyeuVwiOlfhkyaZnOR1xCoEsFAADAcUK4DgAAAABHwe7fK7tulbQ2SzZ7lVSyXzJG6tJT5rJrZdIGSt36yLjdgS4VAAAAJwDhOgAAAAAcAVtTI23ZKJtd2+rl+++cgei2MumDpPRMmX4DZKLbBrZQAAAAtAjCdQAAAABogi3Y44Tp2VnS+tVSeZnkckk9UmXGTHRC9U7dZFyuQJcKAACAFka4DgAAAAC1bHWV9G12/UGku3Y4A3EJMmde4BxEmpohExEZ2EIBAAAQcITrAAAAAFota620O6e+1cumb6SqKikoWOqdLnPhSCdQb9dRxphAlwsAAICTCOE6AAAAgFbFVpRJG76pD9TzdzsDyR1kLrxEJi3TCdZDQwNbKAAAAE5qhOsAAAAATmvWWmnntvpWL5vXS54aKTRMSu0vc8nVMmmZMontAl0qAAAATiGE6wAAAABOO7a0WHbd11J2luzaVdK+QmegYzeZEVc5rV569pUJCg5soQAAADhltXi4/v3332v69OnavHmzoqKidPHFF2vs2LGH7V+4YcMGzZw5U9u2bVNUVJSGDh2q6667Tm632++6f/zjH5o/f74KCwvVuXNnTZo0SX379j2RSwIAAAAQYNbrkbZtdnanZ2dJW7+VrFeKiJJJGyilZcqkDZCJiQ90qQAAADhNtGi4XlxcrKeeekpdunTRww8/rJycHM2cOVNut1tjxoxpcl5eXp6eeuopZWRk6KGHHlJOTo5mzZqlyspKTZ482XfdsmXL9Le//U3jx49X7969tWTJEv32t7/Vs88+q3bt+BVPAAAA4HRi9xU5QfraLGeXemmxZIzUtZfMFdc5vdO79ZJxuQ9/MwAAAKCZWjRcX7x4sTwejx588EGFhYWpf//+Kikp0bx583T55ZcrOLjxX8mcN2+e4uLi9MADD8jtdmvAgAEyxmjGjBkaPXq0YmNjJUlz587V8OHDNXbsWElS3759df/99+uDDz7QHXfc0WLrBAAAAHD82Zpq6buNstkrnd7pO7Y6A21iZPqfJaVnyvQbIBPVJrCFAgAAoFVo0XD966+/1oABAxQWFuZ77Pzzz9fbb7+tjRs3Kj09vdF527ZtU1paml8LmIyMDHk8Hq1evVpDhw5Vbm6ucnNzddttt/mucbvdOuecc/Tpp5+euEUBAAAAOGFs/u76Vi/r10iV5ZLbLfXsJzP2JqflS8euMi5XoEsFAABAK9Oi4XpOTo4yMjL8HmvXrp3cbrdycnKaDNerq6sb7GoPCnJK37lzp+/ektShQwe/6zp27Kj8/HxVVlYqNDT0uKwDAAAAwIlhqyqlTWvrA/Vc5+/7ik+SOXeIcxBpn/4y4RGBLRQAAACtXouG66WlpYqMjPR7zOVyKSIiQiUlJU3OS05O1ubNm/0eq/u6bl5paakkNbh/3delpaUNwvUlS5ZoyZIlkqTf//73SkhIaO6STitBQUGt/nsAALwXAkDLvhdaa+XZuV2Vq/6jqlWfqyp7lVRVJYWEKCQtU6GXjVNI5rlyp3SWMaZFagIAib8XAkAd3g+b1qLh+tEaOXKkpk2bprlz52rkyJHKzc3VG2+8IZfLdUx/wR4xYoRGjBjh+zo/P/94lHvKSkhIaPXfAwDgvRAATvx7oS0vk9avls1e5exOL8hzBtp3kvnRpU6rl95p8oSEqkxSmSQVFJywegCgMfy9EAAcrf39MCUlpcmxFg3XIyMjVVZW5veY1+tVWVmZoqKimpzXv39/TZgwQe+8847efvttud1ujRs3Th999JFiYmJ895aksrIyv57uTe1oBwAAANAyrNcr/bC1vtXLdxskj0cKC5f6ZshcNk4mLVMmPinQpQIAAABHrEXD9ZSUFF+P9Dq7d++Wx+M55E8AJGns2LG67LLLlJeXp7i4OHm9Xs2ePVu9e/f23VtyerDHxcX55u3cuVMJCQn0WwcAAABakC3eL7tulZSdJZu9Stq/1xno3F1m5NVO7/TuqTJBp8Qv0wLA/2fvzsOrKu+1j9/P2nsnmwwQIIEwBZllDoMoiAwCDlCB4oAj1dpjaytah2q1B7XFiXp4fSsOtX319NRKq22VIxJE0ARQBikIyAyKoIQAIUDmaa/n/WOHDSkEo5KsDN/PdXlp9rPW3neirivca/gBAHCKWv1NNjU1VQsWLFBxcXHk6vIVK1YoJiZGPXr0+Nr9g8GgUlJSJEl///vflZSUpL59+0oKD0ZNTk7WypUrI6+5rqvVq1crNTW1hr4jAAAAAJJkQyHpi50nrk7/YqdkrRQXL9NrgNR7oEzvATLNmnsdFQAAADgrarVcHzdunBYuXKjZs2friiuuUFZWlt566y1dddVVCgQCke2mT5+uXr166fbbb5ckZWVl6cMPP1TXrl0VCoW0du1apaen68EHH5TP54vsd9VVV+mFF15QUlKSunXrpg8++ECHDx/WxIkTa/PbBAAAABoFe+RwuEjftE526wapMF8yjtS5u8zE62R6D5I6dpZxfF//ZgAAAEA9U6vlenx8vGbMmKFXXnlFs2bNUlxcnCZPnqxJkyZV2s51XbmueyKk369PP/1U8+fPl+u66tKlix5++GH17Nmz0n4jRoxQcXGx3n77bf3jH/9QSkqKHnroISUnJ9fK9wcAAAA0ZLasTNq1RXbzOtlN66R9e8ILCS1kBlwQftRLz1SZ2KrnKQEAAAANhbHWWq9D1BWZmZleR/BUY5/8CwASx0IAkCofC+2hrBOPetm2USoplnx+qVsvmT4DZXoPlNp1lDHG49QAcHbxeyEAhDX24+GZZoUyPQgAAABAhC0rU8naFXJXZISvTj9YcQFKUrLMsIvDZXqPvjLBJp7mBAAAALxGuQ4AAABA9vAh2WXvyi5/T0fzjklR0eESfcz3wleot6r6ih0AAACgMaJcBwAAABopa620dYPc9DRpw8fhF/sNVsIV1yi37TkygShvAwIAAAB1GOU6AAAA0MjYwgLZlR/IZqRJWfukuKYyl31fZsRlMomtFZ2YKNOIn6sJAAAAVAflOgAAANBI2K92y6anya7KkEpLpE7dZX54t8zgC7lKHQAAAPiGKNcBAACABsyWl8muWymbnibt2iIFomSGjJAZeIwAcgAAIABJREFUPV6mY1ev4wEAAAD1FuU6AAAA0ADZnGzZ5Ytkly2Sco9KSckyV98ic+FYmdh4r+MBAAAA9R7lOgAAANBAWGulbRvlZqRJ61dL1kp9BskZPUHqPUDGcbyOCAAAADQYlOsAAABAPWeLCisGlC6U9n8pxcbLjJskM/JymaRkr+MBAAAADRLlOgAAAFBP2X17ZTMWyK7MkEqKpI5dZW6+S+a84TJR0V7HAwAAABo0ynUAAACgHrHl5dL6VXLT06QdmyR/QOa8i2RGT5Dp1M3reAAAAECjQbkOAAAA1AP26GHZZe/JLl8kHc2RWraSufIHMheOk4lv6nU8AAAAoNGhXAcAAADqKGuttGOzbPoC2fWrpFAoPKD0xp9JfQfKOD6vIwIAAACNFuU6AAAAUMfY4kLZVRnhAaX79kgxcTJjrggPKG3Vxut4AAAAAES5DgAAANQZdv+X4avUV6ZLxUVSSheZH0yXOW+ETDQDSgEAAIC6hHIdAAAA8JANhaT1q+WmL5C2fyr5/TKDh8uMGi917iFjjNcRAQAAAJwG5ToAAADgAXvsiOzyRbJLF0lHD0stkmS+f5PM8HEyTRO8jgcAAADga1CuAwAAALXEWivt2hp+9Mu6lVKoXOo1QM4NP5H6DWZAKQAAAFCPUK4DAAAANcwWF8l+vFQ2PU366gupSazM6PHhAaXJ7byOBwAAAOBboFwHAAAAaojN+ko2Y6HsivelokKpfSeZm34mc/5Imeig1/EAAAAAfAeU6wAAAMBZZEMhaeOa8IDSrRskn19m0DCZ0eOlLj0ZUAoAAAA0EJTrAAAAwFlgc4/KLn9Pdtm7Uk621DxRZvKNMheNk2na3Ot4AAAAAM4yynUAAADgW7LWSp9tk01Pk137UXhAac/+cqb+h9R/iIyPAaUAAABAQ0W5DgAAAHxDtqSkYkDpAunL3VKTGJmRl8mMGi/Tpr3X8QAAAADUAsp1AAAAoJrsgcyKAaVLpMICqV1HmRtul7lglEywidfxAAAAANQiynUAAADgDKwbkj5dGx5QuvkTyeeTGThMZtR4qVsvBpQCAAAAjRTlOgAAAHAaNu+Y7IeLZZe+Kx0+KCW0kJl4vcxFl8gktPA6HgAAAACPUa4DAAAAFay10u4d4QGl//pQKi+TevSVc/UtUv/zZfz8+gwAAAAgjD8dAAAAoNGzpSWya5bLpqdJe3ZJ0U1kho8LDyhtl+J1PAAAAAB1EOU6AAAAGi17KCs8oPSjJVJBntSmg8z1P5EZOkomGON1PAAAAAB1GOU6AAAAGhXrhqRN6+RmLJQ2rZWMkQZcIGf0BKl7HwaUAgAAAKgWynUAAAA0CjY/V/ajJbIZC6XsA1Kz5jITpsqMuFSmeUuv4wEAAACoZyjXAQAA0KDZL3aGB5SuWS6VlUrde8tM+YHMgAsYUAoAAADgW+NPEwAAAGhwbFlpeEBpxkJp9w4pOigz7OLwgNL253gdDwAAAEADQLkOAACABsNmH6gYULpYys+TktvLXHubzNDRMjGxXscDAAAA0IBQrgMAAKBes64rbflEbnqa9Om/JBkpdUh4QOm5/RhQCgAAAKBGUK4DAACgXrIF+eEBpUsXSgf3S/HNZC6/WmbkpTItkryOBwAAAKCBo1wHAABAvWL3fhYeUPrxUqm0VOraU2bi9TKDhsn4A17HAwAAANBIUK4DAACgzrNlZbJrPwwPKP1smxQVLXP+qPCA0pTOXscDAAAA0AhRrgMAAKDOsocPyS5dKPvhYinvmNS6nczUH8kMu1gmJs7reAAAAAAaMcp1AAAA1CnWdaVtG8IDSjesCb/Y/zw5o8dL5/aXcRxvAwIAAACAKNcBAABQR9jCfNkV78tmvCsd2BceUHrZFJmRl8m0bOV1PAAAAACohHIdAAAAnrJf7pZNXyC7eqlUWiJ17iFz690yg4bLBBhQCgAAAKBuolwHAABArbPlZbJrV8hmpEm7tkqBKJkhI2RGT5Dp2MXreAAAAADwtSjXAQAAUGtsTrbssndll78n5R6VkpJlrv6hzIVjZGLjvY4HAAAAANVGuQ4AAIAaZa2Vtm2Um5EmrV8tWSv1HRweUNprAANKAQAAANRLlOsAAACoEbawQHZlevjRL1lfSXHxMuMmhweUJiV7HQ8AAAAAvhPKdQAAAJxVdt+e8IDSVRlSSbHUqbvMLT+XOW+4TCDK63gAAAAAcFZQrgMAAOA7s+Xlsp+sDF+lvmOz5A9UDCgdL3NON6/jAQAAAMBZR7kOAACAb80eOSy7bJHs8kXSsSNSYmuZq26WuXCsTFxTr+MBAAAAQI2hXAcAAMA3Yq2VdmySm75A+mRVeEBp74Fypo2X+gyUcXxeRwQAAACAGke5DgAAgGqxxYWyKzPCj37J3CvFxMmMnRQeUNqqjdfxAAAAAKBWUa4DAADgjGzmXtmMNNmV6VJxkdSxq8zNd8qcd5FMVLTX8QAAAADAE5TrAAAAOIUtL5c2rJabniZt/1Ty+2UGXyQzerzUqbuMMV5HBAAAAABPUa4DAAAgwh7NkV3+nuyyd6WjOVLLVjJTpskMHycT38zreAAAAABQZ1CuAwAANHLWWmnnZtmMhbLrVkihkNR7gJwbfyr1HcSAUgAAAAA4Dcp1AACARsoWF8muqhhQum+PFBMrM/p7MqMul2nd1ut4AAAAAFCnUa4DAAA0Mnb/VxUDSj+QigqlDp1kpt0hM2SkTDQDSgEAAACgOijXAQAAGgEbCkkbPpabkSZt3SD5/DKDL5QZNV7qci4DSgEAAADgG6JcBwAAaMBs7hHZ5YvDA0pzsqUWiTKTb5S56BKZpglexwMAAACAeotyHQAAoIGx1kqfbZVNXyi79iMpVC717C/n2tukfufJ+BhQCgAAAADfFeU6AABAA2FLimVXLw0PKP1yt9QkJjycdNTlMsntvY4HAAAAAA0K5ToAAEA9Z7P2yS5dKPvR+1JRgdSuo8yNP5U5f6RMsInX8QAAAACgQar1cn3v3r165ZVXtGvXLsXFxWncuHGaMmXK1w7R2rVrl+bOnavdu3fLcRx17dpVN9xwg1JSUiLbPProo9qyZcsp+/71r3+Vj9ufAQBAA2LdkLRxjdz0NGnLesnnkxk4TGb0BKlrTwaUAgAAAEANq9VyPS8vTzNnzlTHjh11//33KzMzU6+++qp8Pp8mT55c5X7Z2dmaOXOmunfvrjvvvFOhUEhvvvmmZs6cqWeeeUZxcXGRbfv27aupU6dW2p9iHQAANBQ275js8vdkl74r5RySElrKTLpe5qJLZZo19zoeAAAAADQatVquL168WKFQSPfdd5+CwaD69eun/Px8zZs3TxMmTFAgEDjtfuvWrVNpaanuvfdeBYNBSVJKSoruuOMObdmyRUOGDIlsGx8fr+7du9fK9wMAAFAbrLXS59tlM9Jk//WhVF4u9egr55pbpdTzGVAKAAAAAB6o1XJ9/fr1Sk1NjRTkkjRs2DC98cYb2r59u/r06XPa/UKhkHw+n6KioiKvxcTESJJc163Z0AAAAB6xJSWya5bJpqdJez+Tgk3CV6iPHi/TpoPX8QAAAACgUavVcj0zM1P9+/ev9FpycrJ8Pp8yMzOrLNeHDBmi119/Xa+99pomTZqkUCik1157Ta1atVJqamqlbT/55BPdcMMNMsaoZ8+euummmyo9lx0AAKCuswczZZe+K/vhEqkwX2qbInPDT2QuGCUTjPE6HgAAAABAtVyuFxQUKDY2ttJrjuMoJiZG+fn5Ve7XsmVLPfzww5o1a5bmz58vSUpKStKMGTMqXQXfq1cvjRw5UsnJycrOztabb76pRx55RE8//bQSExNr5psCAAA4C6wbkj5dJzdjgbRpXXhAaeoF4QGl3XszoBQAAAAA6phaLde/rSNHjuiZZ55Rt27dNG7cOIVCIb3zzjt68skn9dhjjyk+Pl6SdM0111Tar1+/frrrrruUlpamadOmnfK+S5Ys0ZIlSyRJTz31VKMv4P1+f6P/GQAAx0LUNjf3mIren6+iRfMUOpApp3mimkz9oZpcMkm+Fklex0MjxbEQADgWAsBxHA+rVqvlemxsrAoLCyu95rquCgsLFRcXV+V+b7/9tiTp7rvvlq9iYFevXr10++23a/HixZoyZcpp92vWrJnOPfdc7d69+7TrY8eO1dixYyNfZ2dnf6Pvp6FJTExs9D8DAOBYiNpid++UTV8gu2a5VF4mde8jM+lGacAFKvb7VexK4r9FeIRjIQBwLASA4xr78bBt27ZVrtVqud62bVvt27ev0msHDhxQKBQ6Y8jMzEx16NAhUqxLUjAYVHJysg4cOPC1n8tt1AAAoC6wpSWyaz6UTV8g7dklRTeRGT5WZtR4mXYdvY4HAAAAAPgGarVcT01N1YIFC1RcXBx5VvqKFSsUExOjHj16VLlfy5YttXHjRoVCoUjBXlhYqKysLA0aNKjK/Y4ePapt27ZpzJgxZ/cbAQAA+AbsoSzZpQvDA0oL8qQ2HWSuu01m6MUyTRhQCgAAAAD1Ua2W6+PGjdPChQs1e/ZsXXHFFcrKytJbb72lq666SoFAILLd9OnTI499kaQxY8bogw8+0OzZszV27Fi5rqv58+crFApp5MiRkqQ9e/Zo7ty5Gjp0aORWhXnz5skYo8svv7w2v00AAABZ15U2fyI3fYG0aa1kjJR6gZxRl0vn9uPOOgAAAACo52q1XI+Pj9eMGTP0yiuvaNasWYqLi9PkyZM1adKkStu5rivXdSNfd+nSRQ899JD+/ve/a86cOXIcR506ddIjjzyipKSkyHtL0ty5c5WXl6cmTZqoV69e+sUvfsED9wEAQK2xBXmyHy2RzVgoHcqSmibITLhG5qJLZVrwOwkAAAAANBTGWmu9DlFXZGZmeh3BU419OAEASBwL8e3ZPbvCA0o/Xi6VlUpde8mMHi8zcKiMP/D1bwDUIRwLAYBjIQAc19iPh3VmoCkAAEBDYstKZf/1UXhA6e4dUlS0zNDR4QGlHTp5HQ8AAAAAUIMo1wEAAL4he/hgeEDp8sVSfq7Uup3M1B/JDLtYJibO63gAAAAAgFpAuQ4AAFAN1nWlrRvCA0o3/iv8Yv8hckaPDw8odRxvAwIAAAAAahXlOgAAwBnYgnzZFe+HB5QezJTim8lcfqXMiMtkWiZ5HQ8AAAAA4BHKdQAAgNOwez+XzUiTXZ0hlZZKXc6VueJamUEXygQYUAoAAAAAjR3lOgAAQAVbVia7bkV4QOln26SoKJnzR8mMulwmpYvX8QAAAAAAdQjlOgAAaPRsziHZpe/KLn9PyjsmtWojc82tMsPGyMQyoBQAAAAAcCrKdQAA0ChZaysGlKZJGz6WZKV+58kZNV7qlcqAUgAAAADAGVGuAwCARsUWFsiu/EA2I03K2ifFxctc+n2ZkZfJJLb2Oh4AAAAAoJ6gXAcAAI2C/eoL2fSKAaUlxVKn7jK3/FzmvOEygSiv4wEAAAAA6hnKdQAA0GDZ8jLZdSvDV6nv3CIFomSGXCQzeoJMx65exwMAAAAA1GOU6wAAoMGxRw7LLqsYUHrsiJSULHPVLTIXjpGJa+p1PAAAAABAA0C5DgAAGgRrrbT90/CA0vWrJGulPoPkjB4v9R7IgFIAAAAAwFlFuQ4AAOo1W1QouypdNj1N2v+lFBsvM26SzMjLZZKSvY4HAAAAAGigKNcBAEC9ZPftlc1YILsyQyopkjp2lbn5rvCA0qhor+MBAAAAABo4ynUAAFBv2PJyaf2q8KNfdmyS/IFwmT56gkyn7l7HAwAAAAA0IpTrAACgzrNHc2SXLZJdvkg6miO1bCUz5Qcyw8fJxDOgFAAAAABQ+yjXAQBAnWStlXZulk1Pk/1kpRQKSX0GyrnxZ1LfgTKOz+uIAAAAAIBGjHIdAADUKba4UHZVhmzGQmnfHikmVubi78mMulymVVuv4wEAAAAAIIlyHQAA1BF2/5fhq9RXfiAVF0kpnWWm3SEzZKRMNANKAQAAAAB1C+U6AADwjA2FpA2rwwNKt22U/H6ZQRfKjJ4gde4hY4zXEQEAAAAAOC3KdQAAUOvssSOyyxfJLl0kHT0stUiS+f5N4QGlTRO8jgcAAAAAwNeiXAcAALXCWivt2iqbvkB23UopVC71SpVzw4+lfucxoBQAAAAAUK9QrgMAgBplS4plV2fIpqdJX30hNYkNDycdNV4muZ3X8QAAAAAA+FYo1wEAQI2wWftkM9JkV3wgFRVI7TvJ3PQzmfNHykQHvY4HAAAAAMB3QrkOAADOGhsKSZ+uCQ8o3bJe8vllBg2TGT1e6tKTAaUAAAAAgAaDch0AAHxnNveo7PL3ZJctknIOSc0TZSbdIHPRJTLNmnsdDwAAAACAs45yHQAAfCvWWunz7eEBpWs/ksrLpXP7yZl6q9T/fBkfA0oBAAAAAA0X5ToAAPhGbEmJ7MdLZTPSpL2fS8EmMhddKjN6vEybDl7HAwAAAACgVlCuAwCAarEHM2XTF8quWCIVFkjtOsrccLvMBaNkgk28jgcAAAAAQK2iXAcAAFWybkj6dK3c9AXS5k8kn09mwNDwgNJuvRlQCgAAAABotCjXAQDAKWxeruyHi2WXLpQOH5QSWshMvD48oDShhdfxAAAAAADwHOU6AACIsLt3hAeUrvlQKi+TevSVc/Ut4QGlfn5tAAAAAADgOP6UDABAI2dLS2TXLJdNT5P27JKim8gMHyczarxMuxSv4wEAAAAAUCdRrgMA0EjZQ1myGQtlP1oiFeRJbTrIXP9jmQtGyzSJ8ToeAAAAAAB1GuU6AACNiHVdafM6uelp0qa1kjFS6gVyRo+XevRlQCkAAAAAANVEuQ4AQCNg83NlP1oim7FQyj4gNWsuM2FqeEBpi0Sv4wEAAAAAUO9QrgMA0IDZL3bKpqfJrlkulZVK3XrJTJkmM+ACGX/A63gAAAAAANRblOsAADQwtqxUds2Hshlp0u4dUnRQZujFMqPHy7Q/x+t4AAAAAAA0CJTrAAA0EDb7gOzSd2U/XCzl50rJ7WSuvU1m6GiZmFiv4wEAAAAA0KBQrgMAUI9Z15W2fCI3Y6G0cY0kI6UOkTN6gnRuPwaUAgAAAABQQyjXAQCoh2xBfnhA6dKF0sH9Unwzmcuvlhl5qUyLJK/jAQAAAADQ4FGuAwBQj9i9n4UHlH68VCotlbqcKzPxepmBw2QCDCgFAAAAAKC2UK4DAFDH2bIy2bUfhQeUfrZNioqWOX+UzKjxMimdvY4HAAAAAECjRLkOAEAdZQ8fkl26MDygNO+Y1KqtzNRbZYaNkYmJ8zoeAAAAAACNGuU6AAB1iHVdadsGuelp0oY14Rf7DQ4PKO3ZX8ZxvA0IAAAAAAAkUa4DAFAn2MJ82RUfyGYslA7sk+Kaylz2fZmRl8u0bOV1PAAAAAAA8G8o1wEA8JD9and4QOmqDKm0ROrcQ+bWu2UGDWdAKQAAAAAAddg3Ltf379+vL7/8Un369FFMTExNZAIAoEGz5WWya1eEB5Tu2ioFomSGjJAZPUGmYxev4wEAAAAAgGqodrn+4osv6vHHH1dmZqaMMVqzZo0GDhyoq6++WiNGjND06dNrMicAAPWezcmWXfau7PL3pNyjUlKyzNW3yFw4ViY23ut4AAAAAADgG6hWuT5r1iw9+uij+uUvf6lRo0Zp9OjRkbURI0Zo7ty5lOsAAJyGtVbatlFuRpq0frVkrdRnUHhAae8BDCgFAAAAAKCeqla5/vzzz+uxxx7Tvffeq1AoVGmtR48e2rFjR42EAwCgvrJFhbIrKwaU7v9SiouXGTdZZuRlMknJXscDAAAAAADfUbXK9UOHDik1NfW0a9ZaFRcXn9VQAADUV3bfHtn0BeEBpSXF0jndZG65S2bwcJmoaK/jAQAAAACAs6Ra5XqPHj307rvvasyYMaesLVmyRP379z/rwQAAqC9sebnsJ6tkMxZIOzZL/oDMeReFB5R26uZ1PAAAAAAAUAOqVa7/8pe/1I033qiSkhJdeeWVMsZo69atWrRokebMmaM33nijpnMCAFDn2KOHZZctkl32nnQsR0psLXPVzTLDxsrEN/U6HgAAAAAAqEHVKtevvfZaFRcX61e/+pWee+45SdJNN92k1q1b649//KMmTpxYoyEBAKgrrLXSjk1y0xdIn6wKDyjtPVDOtJ9JfQbKOD6vIwIAAAAAgFpQrXJdkm6++WZNmzZN27ZtU3Z2tlq0aKFevXrJcZyazAcAQJ1giwtlV2bIZqRJmXulmDiZsRNlRl4u06qN1/EAAAAAAEAtq3a5LkmO46hXr141lQUAgDrHZu6VzUiTXZkuFRdJKV1kfjBd5rwRMtEMKAUAAAAAoLGqVrn+xBNPfO02Dz300HcOAwBAXWBDIWn96vCjX7Z/Kvn9MoMvkhk9XurUXcYYryMCAAAAAACPVatcf+aZZ055LTc3V2VlZYqJiVFMTAzlOgCg3rMFecr/YL7chW9KRw9LLZJkpkyTGT5OJr6Z1/EAAAAAAEAdUq1y/dChQ6e85rqu0tPTdc899+iVV14568EAAKhNNusruc/+RgWHsqReA+Tc8BOp32AGlAIAAAAAgNP6Rs9cP5njOBozZozuvvtu3X777fr444/PZi4AAGqN3b5J7gtPSD6fmj/5knITGVAKAAAAAADOzPmub9CmTRtt2bLlbGQBAKDWuavS5T7zsNQ0Qc6DTyvq3L5eRwIAAAAAAPVAta5cd133lNdKS0u1detW/frXv1bPnj3PejAAAGqStVZ2/t9k5/9V6tFXzu0PysTGeR0LAAAAAADUE9Uq1/1+v4wxp11r3bq15s2bV+0P3Lt3r1555RXt2rVLcXFxGjdunKZMmVLl+x+3a9cuzZ07V7t375bjOOratatuuOEGpaSkVNruvffe0/z585WTk6OUlBRNmzaN8h8AUIktK5P983Oyq9Jlho2RuemnMv6A17EAAAAAAEA9Uq1y/Q9/+MMp5XcwGFT79u01dOhQRUVFVevD8vLyNHPmTHXs2FH333+/MjMz9eqrr8rn82ny5MlV7pedna2ZM2eqe/fuuvPOOxUKhfTmm29q5syZeuaZZxQXF77ScNmyZXr55Zc1depUde/eXUuWLNETTzyhp59+WsnJydXKCABo2GxBntwXnpR2bJKZdIPMhGu+9gQvAAAAAADAv6tWuf6jH/3orHzY4sWLFQqFdN999ykYDKpfv37Kz8/XvHnzNGHCBAUCp79qcN26dSotLdW9996rYDAoSUpJSdEdd9yhLVu2aMiQIZKkf/zjH7r44os1ZcoUSVLPnj11zz336O2339Ztt912Vr4HAED9ZQ/ul/vsb6TDB2R+dK+c80d6HQkAAAAAANRTVQ40dV33G/1VHevXr1dqamqkIJekYcOGqbCwUNu3b69yv1AoJJ/PV+kK+ZiYmEhOScrKylJWVpaGDh0a2cbn8+n888/X+vXrq5UPANBw2V1b5T75Cyk/V849j1GsAwAAAACA76TKct3v9ysQCFT7r+rIzMxUu3btKr2WnJwsn8+nzMzMKvcbMmSI/H6/XnvtNeXm5urIkSP605/+pFatWik1NTXy3pJOef/27dsrOztbJSUl1coIAGh43DXL5c7+TykmVs6DT8t06+V1JAAAAAAAUM9V+ViY0z1n/bsqKChQbGxspdccx1FMTIzy8/Or3K9ly5Z6+OGHNWvWLM2fP1+SlJSUpBkzZkSugi8oKJCkU97/+NcFBQWKjo6utLZkyRItWbJEkvTUU08pMTHxO3x39Z/f72/0PwMADYu1VoVvvqr8v/xegZ79lfDLJ+U0TTjjPhwLAYBjIQBIHAsB4DiOh1Wrslw/W89ZPxuOHDmiZ555Rt26ddO4ceMUCoX0zjvv6Mknn9Rjjz2m+Pj4b/W+Y8eO1dixYyNfZ2dnn63I9VJiYmKj/xkAaDhsebnsay/KfrhYZsgIhW6+Uzml5dLXHOc4FgIAx0IAkDgWAsBxjf142LZt2yrXqjXQ9GyJjY1VYWFhpddc11VhYaHi4uKq3O/tt9+WJN19993y+XySpF69eun222/X4sWLNWXKlMgV6oWFhZWe6V7VFe0AgIbLFhbI/f1T0tYNMt+bKjPx+rN+NxYAAAAAAGjcql2uv/nmm3r55Ze1c+dOFRcXn7K+d+/er32Ptm3bat++fZVeO3DggEKh0BnPAGRmZqpDhw6RYl2SgsGgkpOTdeDAgch7S9K+ffvUokWLyHb79u1TYmLiKY+EAQA0TPbwQbm/+7V0MFPm5rvkXDjG60gAAAAAAKABqnKg6cn++te/6rrrrlObNm20a9cujRkzRqNGjVJhYaECgYBuvvnman1Yamqq1q9fX6mcX7FihWJiYtSjR48q92vZsqX27t2rUCgUea2wsFBZWVlKSkqSFB6MmpycrJUrV0a2cV1Xq1evjgw9BQA0bHb3TrlP3CcdzZFz16MU6wAAAAAAoMZUq1yfNWuWZsyYoZdeekmSNH36dP35z3/W7t271bp1azVt2rRaHzZu3Dg5jqPZs2dr48aNeu+99/TWW29p8uTJCgQCke2mT5+uF198MfL1mDFjlJ2drdmzZ2vdunX617/+pVmzZikUCmnkyJGR7a666ip98MEHeuutt7Rp0yY999xzOnz4sCZOnFitfACA+suuWyn3vx6UAlFyHvytTM/+XkcCAAAAAAANWLXK9V27dmn48OHy+Xzy+/3Kzc2VJMXHx+uBBx7QnDlzqvVh8fHxmjFjhsrKyjRr1iz985//1OTJkzVp0qRK27muK9d1I1936dJFDz30kPLy8jRnzhy9+OKLCgQCeuSRRyJXrkvSiBEj9MMf/lDvv/++nnzySe3fv18PPfSQkpOTq5UPAFD/WGvlvjcv/Iz1dufIeei/ZNp08DoWAAAAAABo4Kr1zPWEhITIo1zat2+vjRs3atQ7aWyQAAAgAElEQVSoUZKkoqIiHTlypNofmJKSokcfffSM2zz//POnvNavXz/169fva9//kksu0SWXXFLtPACA+suGQrJ/+6NsRpo0cJicW++WiWLGBgAAAAAAqHnVKteHDh2qdevW6bLLLtPVV1+tRx55RLm5uQoEAvrd735X6dEsAADUBltcKPelp6VNa2UunSIzZZqMU60bsgAAAAAAAL6zapXrjzzyiPbt2xf556NHj2rOnDkqKirSmDFj9MILL9RoSAAATmZzsuXOmSll7pG56adyRlzmdSQAAAAAANDIGGutPd1Cly5ddO2112rq1KnVehxLQ5CZmel1BE8lJiYqOzvb6xgAcEZ272fhYr24SM6PH5DpM/Csvj/HQgDgWAgAEsdCADiusR8P27ZtW+ValffPDx06VM8995wGDBig3r17a+bMmdqxY0eNBAQAoDrsxjVyf/ug5DhyHph11ot1AAAAAACA6qqyXP/LX/6igwcP6o033lDv3r01a9Ys9ezZUwMHDtTTTz+tvXv31mZOAEAj56YvkPvc41LrdnIefFqm/TleRwIAAAAAAI3YGSe/RUdH68orr9Qbb7yhgwcP6i9/+YtSUlL08MMPq1OnTho2bJieffZZZWVl1VZeAEAjY92Q3Nf/n+zcl6R+g+Xc/6RMQkuvYwEAAAAAgEbujOX6yWJiYnTddddp3rx5OnDggP77v/9bCQkJuueee5SSklKTGQEAjZQtKZb74lOyS96WGXOFnJ8+KBMd9DoWAAAAAACA/N9mp927d2vbtm3asWOHXNdVUlLS2c4FAGjk7NEcuc89Ju39XOba2+SM+Z7XkQAAAAAAACKqXa5v3rxZr7/+ut544w3t3LlTCQkJmjJlil566SWNHj26JjMCABoZu2+P3Gd/IxXkyfnZr2T6n+d1JAAAAAAAgErOWK5v3749Uqhv3bpVcXFxmjhxombPnq1LL71Ufv+3uvAdAIAq2c2fyH1plhQVlPOLJ2U6dvE6EgAAAAAAwCmqbMf79eunzZs3KxgMavz48fr1r3+tCRMmKBjkWbcAgJrhLlsk+9qLUtsUOdNnyLTgsWMAAAAAAKBuqrJc79ixox544AFNmjRJcXFxtZkJANDIWNeVfetV2Xf/KfUZKOfH98sEY7yOBQAAAAAAUKUqy/X58+fXZg4AQCNlS0tkX/m/sms/khl5mcx1P5bx+byOBQAAAAAAcEY8NB0A4Bmbe1Tu849Lu3fIXH2LzLjJMsZ4HQsAAAAAAOBrUa4DADxh938p99nfSLlH5PzkAZmBw7yOBAAAAAAAUG2U6wCAWme3bZT74pOSzy/nvidkOnX3OhIAAAAAAMA3QrkOAKhV7or3Zf/8vNSqjZw7H5ZJbO11JAAAAAAAgG+Mch0AUCustbJv/1X2nb9JPfuHHwUTE+d1LAAAAAAAgG+Fch0AUONsWZnsn56V/XipzIVjZW68XcYf8DoWAAAAAADAt0a5DgCoUTY/V+4LT0g7t8h8/yaZy6+SMcbrWAAAAAAAAN8J5ToAoMbYg5lyf/cbKeeQzH/cJ2fICK8jAQAAAAAAnBWU6wCAGmF3bpH7wuOSJOfemTJde3mcCAAAAAAA4OyhXAcAnHXu6qWyf/qd1LK1nDtnyLRq63UkAAAAAACAs4pyHQBw1lhrZdP+LjvvL1L33nJuf1AmrqnXsQAAAAAAAM46ynUAwFlhy8tk//KC7Efvy1wwSmbadJlAwOtYAAAAAAAANYJyHQDwndnCfLkvPiVt2yhzxbUyV1wnY4zXsQAAAAAAAGoM5ToA4Duxh7LkzpkpHdwvc8vP5Qy72OtIAAAAAAAANY5yHQDwrdnPt8t97jEpVC7n7l/L9OjrdSQAAAAAAIBaQbkOAPhW7NoVcl/+P1JCCznTH5Zp097rSAAAAAAAALWGch0A8I1Ya2Xfmyf7zz9JnXvI+dmvZOKbeR0LAAAAAACgVlGuAwCqzYZCsnNfkl32rsygC2V++HOZqGivYwEAAAAAANQ6ynUAQLXYokK5L82SNn8ic/lVMpNvlHEcr2MBAAAAAAB4gnIdAPC1bM4huc/+Rtr/pcy0O+RcdInXkQAAAAAAADxFuQ4AOCO75zO5c2ZKpcVy7npEptcAryMBAAAAAAB4jnIdAFAlu+FjuX94WoprKueBWTLtOnodCQAAAAAAoE6gXAcAnJb7/nzZ11+WUjrLmT5DpllzryMBAAAAAADUGZTrAIBKrBuSff1l2Q/ekVIvkPOje2Sig17HAgAAAAAAqFMo1wEAEba4SO7/my1t+Fhm3CSZq26WcXxexwIAAAAAAKhzKNcBAJIke/Sw3DmPSV/ulrn+J3JGj/c6EgAAAAAAQJ1FuQ4AkP1qt9xnZ0qFBXKm/6dM38FeRwIAAAAAAKjTKNcBoJGzm9bKfem3UjBGzv1PyqR09joSAAAAAABAnUe5DgCNmLv0Xdm5v5fadZRzxwyZFoleRwIAAAAAAKgXKNcBoBGyriv7z/+Rfe8tqe9gObfdJxOM8ToWAAAAAABAvUG5DgCNjC0pkfvKM9K6FTKjx8tM/Q8Zn8/rWAAAAAAAAPUK5ToANCI294jc5x6XvtgpM/VWmTETZYzxOhYAAAAAAEC9Q7kOAI2Ezdwr99nfSHlH5dz+oMyAC7yOBAAAAAAAUG9RrgNAI2C3bpD74lNSICDnF0/KnNPN60gAAAAAAAD1GuU6ADRw7kdLZF99XmrdTs6dD8u0bOV1JAAAAAAAgHqPch0AGijrurL/O1c27Q2pV6qcHz8gExPrdSwAAAAAAIAGgXIdABogW1Yq+9+/k12zXOaiS2Su/4mMn0M+AAAAGp+Qa1VY5qq43FVJuauiclcl5Vbtm0UpIejXoYIyffxVvorLT2xTXG510wUxipf0eU6x3t15VD5H8hkjn2PkGOmybglqHRelL44U61/7CuQYRdZ8jtHwlHg1Dfr1VW6Jdh0uls8YOcffwxj1TY5R0O/oUEGZDhaUhfc76f1TmkXL5xjllYRUWBaSzzEV+0qOMYqNcmSMUci1MhWvAQBqF00LADQwNi9X7guPS7u2ykz5gcxlU2T4RRsAAAB1WMi1kXK7uNyqpNxV06BPiTEBFZe7WrE376RiPLzNoLax6pccq0MFZXp+dVbF6yfe4wcDkjSqUzPtyinW/Yv2nPKZ917YViPOaar9eaX6w78OSJKMpGi/o6DfaHxBqeJjpJyicq36Kk+ua+VaKWStQq40pH2cWsdF6fMjJXp1w6FT3r9XUhM1Dfq1YX9h5P1P9odJnRWMi9LSL3L16vpT9//zlV3VLOjXvK05+sfmw6es//3a7oryGb287qAWbD8iI8nnhEv2KJ/Ra1d3lyS9vPaAPtqbFynlfY5RXJRPv720oyTp1fWH9OmBQvlOOjnQvIlfdw9rK0l6Y1O29hwtqSj+w++RGOPXdf2SJEnvbM9RdkF5pRMLSTF+jemSIEnK2H1MBaVuZM1npJYxAaW2Cd9V+8n+ApWF3Eg2n5ESgn6lJERLCp/ckE46cWGMYqIcJQTDldax4vKKfcNrzknfJwDUNMp1AGhAbNY+uXN+I+Vky/nx/TKDh3sdCQAAAA2Ea23Fld3hv/sdo6TYgCRpzVf5KigLRdaLyl11bBatoSnxstbq6Q8zTynPR5zTVFP7Jqq43NXU13ec8nnX9GmpG/onqbjc1e9W7q+0FuUzahnjV7/kWBkj5ZeGFPQ7ahnjryjHHSXGhLO1jgvoR4NaRV6P9hsF/Y46VpS3PZOa6H+u7KomfkdRPhO5MCUxMUHZ2dka3C5Of76yW5U/l1GdmuqijvEK2fBJguMFfFyUL7I+oE1sRSl/Yr1Fk3AlM6JjU3VrGay05rpSTMCRJA1LiVeb+EB47aRtfBU5B7eNVdMoX3i/im1O1jEhWoVlbqV9o3xOZD3gMwr6TSR/qZWKytzI+oH8Mu0+UlKxf/jEQtumUZH1VV/ma3t2kVxrVV6xW4/EJpFy/Z+bD2vvsdJKmVLbxEbK9edX7dehwvJK60M7xOuXI9pJkh5+f6/ySt1K6xd3bqq7hobL/x++tSvyucdN6J6g285LVlnI6ro3dpwo3isK+ok9WuiqPi2VXxLSLxbtqVT8+xyj8d2b6+LOzXSkqFzPrMgMl/VGciq2GdclQYPaxelQQZn+ujG7crHvGI08p6m6tWyiQwVlWvLZ0Uqf7TNGQ9rHqU18lLILy7R+f0Hk/Y+fQOjVKkYJQb+OFpXri6MlkZMax09wtG8arSYBR/mlIR0rDp1010P47/HRPvkco/KKf2fHs3HRFXD2Ua4DQANhd2yW+8ITkjFy7ntcpsu5XkcCAABALbPWqiQULr9DrlXLioJ55+Ei5RSVR4rx4nJXcVE+Xdy5mSRp7sZD2pdbGrkqvLjcVafm0frZ+W0kST+b/7m+yq1ckJ7fPk4PjWwvSXp21X7lloQqrY/q1FRDU+JljFFmXqkcEy5xE4I+Bf0BJcaEK4kon9H1/RIV/Lfyu32zcIEbH+XT7yd2rrR+8iNQEmMC+q/LzqnyZ5IQ9OuKc1tUuR7wOUo4qWz+phxj5PiMAlWsx0b5FFtRtJ9Oq7iAWsVVtbfUpUVQXVoEq1wf2DZOA9vGVbk+tkuCxlYU3adzbd/EKtckafoFbc64/tjYlEpfuxUl/3FPXdKxouRV5ASD/6Sryh8e3UGloYrivuLEQnz0iZ/XPRe2VWnIRu4YcK1V69gTP69bB7U+ceLADW93/OdljHTFuc0rrYVcRf7bMkbq0iJaIavIiQPXWkX5TOR7KSm3CllXbsU2risVVJx8KCp3tSGrILx/xfu7VurWIqhuLZvoYEGZ/vbpqXcdtI4LqE18lL44UqI5q7JOWf/NmA5KSPZr08FCPf1h5inrv720o3okNtHKvXl6bvWp+8/5XielNItW2o4jenntwcjrx6/q//3EzkqKDWje1sN6c3NO5KTB8TsAZl92jmKjfJq/LUfpu3Mjxf/xbR69uIN8jtHCHUf0yf6CEycmjFGU30SOGx98fkyf5xSf9Mgko5iAoyt7t5QkrfoyTwfyy06620CKi/JpeMemkqRPDxQotyQUeZSSY8L/P52b1ESS9MWRYpWGbKW7GoJ+J/L/09GicrlSJJvjSAHHKPAd/n8H/h3lOgA0AO6qDNn/eVZKbC3nzkdkkpK9jgQAAIAqWGtV5loVl1vFVzw3+0B+qQ4WlIWv+i5zVRJyVe5aXdatuaRwSbX1UGGk+C4udxXtM/rPUR0kSbM/ytTqL/NUEjrRaraJD+j3E7tIkv70ySFtOlBYKUen5tGRcv2zw8XKzCtTsKLYjo/yqVn0icpgXNdmKi6zkeI72u8o+aRC+PGxKfI7JrIe9DuVHsvxf8d3qvLn4RijqWcoeH2OUZv4qCrXUbc4FSXocWc6sSAp8viXqpzpxIEkje/evMo1v2P0gwGtqlyPjfLpvuHtqlxvGRPQrIrH55xOSrNovfz9rlWu924Vo3nX96j0OKGQtYquKHf7to7RHyd1qTixcPyuBRsph/u2jtET41IqFf+uldpV3DnQp3WM7hnWJnJy4PhJhBYVj8zpmdREN6UmRYr/4+9x/K6IDk2jNSwlvlI211Xk5EeTgKPmQd+JbFYqr3jGvyTllYaUlV8WObnh2sonTrYdKtKHe3IrfXZ8tC9Sri/57JjW7Muv9DNrEx+IlOt/+/TwKcetzs2j9UzF8WTOqiztqnhs0HG9kproyUvC/84eWrJX+/7tpOCgtrF6eHT4uHnrW7t0tOKxQsfL/aEd4iMnlO5csFsl5W6l8n5oSnzkhNSvFu+RMSfWHCNd0CFe47omqCzk6tlVWSedmAivD24Xp8Ht4lRYFtI/N+ec2LfiroO+rWPUPbGJCkpDWvZFbqXP9jlG3VoG1SY+SgWlIW09VFTppIfPhO8qaRb0q6jM1cGCskonTRxj1DTap2i/o3LXRr6349twd8O3Q7kOAPWYtVb2nddl354r9egr5/YHZWLP/MsnTrV09zFtOlioz3NKdKTkc/lk1bVlUA9cFP5Fe86q/couKFPA5yjgM4pyjM5pHq3v9wr/Uvi/W3NUVO4qyjEK+Iz8jlHbplHqnxy+1XVDVoGMTlwlEeUL36rZvOJW4KKy8G3VfodfZgAAqEvKQjZyxWVeSUgHC8pUXDEYszjkqrjM1dCUeMUEfPr0QIHWfJUfeeTJ8fX7L2qn2Cif3txyWG9vzQmvh9zIlb3/uLa7Aj6j/92aowU7jlb6fMdIl3ZNkDFGOw8Xac1X+QoGKq7e9jmRgkwKl3DNg77Io0+CfkfNgidKzdsGt1ZZyIbL84CjoC9ckB83o6Jsqsrkni3PuP51BSnQWJnjj3yRkf7tPEO031GruKqvom4W9KtZsOrqrk181BlPPHVr2UTdWjapcn1QuzgNavft73q4pk+irulT9Ymxn56frJ+eX/nCL2tPnAC8/6K2KndPFPvh4bwn/jx01wVtVFRxF87xcj9wUnn/H4NbK780FDkp4FqruJPueri+X6LySkKR4j9krVqddNfDhO7NVRB5ZFL4BEen5ieOZecmNgnfBXTyyYGKk0XWWqlioHCplVzryrVWBWXhO3jKXWlHdtFJj3oKv39SbECD28WpuNxq3tbDCrnSyQ9yumVgkronNtGR4nL9fs2psxp+OiRZbeKjtD+vTDMzvjpl/Z5hbTSyUzPtyinSfy758pT1X41spyHt47UuM1+PL913yvrMMR3ULzlWH+3J1fOrsyqGNzfXz8ee+Q6XxoxyHQDqKVteJvvn52VXfiAzdLTMtDtk/FXfTtrYHSsu1+4jJfo8p1i7j5QorzSkRy8O/yFy+Z5cbT1UpM7Ngzq3TTMVFBZFnh8qhX+JKip3lVsSUmkofKXZyb8AvbP9iA4WlFX6vGEp8ZFy/bfL9yn/DM+JvPEfO/4/e3ceH1V1/3/8fe7MZCbJZJkkQEjYEnZkd4e6g3Wpiqh1a2u1C3XBVqHa1qqtaIttrb9C3arl69JNq6JVq1XUbuBapC64gIkgRNYkZJ1kMvf8/phkyBQCKJCb5fX8h0fuNp8byAHe99zPSfaJTPMZBRyjE4bn6iuT+ioWt5rzzEfJ7YHWX48Ykq2jS3LUGHN13xubktvTfI78vsSMh5EF6WqMuXp1Xa3SWh8MtB1XlJWm3HS/YnGr6mhL4qFB6z6/Ywj5AQDdhrWJMKY57mpLfUu7vt6J3t8jCkLKzwiooqZZ//yoJmV/tMXq3PEFGpwb1Ovr6/Tb/2xKCcbjVvrliUM0NC+kpWtrdMerOwYdI/ukKyPgU1llk55ZVZ0MvxPhdaLnsZQIwQ4qDieD7ZDfUShglFhCMzH79vBBWclZ4Yljtv99POvgQs06uOO3E48f1nEAJinZXxwAvNT+/xlpPke7erFhV+2SJCXbw3SkbQZ8R2YesOuHhv/7YKA9Y4xu+p+WSO2lBxzdddrQDvfnpfv1yLmJVq7t3zpoa3nVP5yme2cO22GthtzWN4oG5KTp558fnLLWgmu3P+gcmBPU1UcUbX8jofWYkkgouf+iyX2TDybirQ8f+oXb1qpI07GlOYpbq5I8/v7YFcJ1AOiGbH2d3Dt+Kr3/lsyp58l84WzC0FbWWm2qj6msqkmHDgjLMUb/t3yTHnu3MnlMnwy/SvMSi0b5HKM5U4sV8icC5YKCAm3ZsiXlmm0heEfunjFUcTcRusdaw3dfu9+OHx07UE0tie3NcVctcauCduH9lyf2SYT28e3nD81P/KPHtVaF4YBaXKvmeOL18To3rvrWsL6pxdXStbVqjlu1uG4ypL9gUh+NLEhXVWOLfrksdQEwSfrWwf104oiIPt7WpCue/miH/W0zHt7f0qif/2t9ayi/PaD/ysQ+GtM3Q2WVUS1eWbk9uG8N6I8flqv+WWlaX9Os/26oV1rrjP601uuM6ZuuzDSfaqItqmxsSbwV0Lrf70v0YnT4Mw0APYprrWqicTW2uCl9vdt6D9c3x7Xkw21qanGTxzS2WB05JLEYZEVNs36xtKL13O3XuOTQQh1bmqPVW6P6/nNrd/jcq48o0pRBAW2sj+mPb21Rms8o6HeU7k/82tA6yzCc5lNpXjAZbqe39vbObZ39Pal/pn5wVPH2vt++RPuTtr/TTxudp9NGd9zX+/CBWTp8YFaH+wfkBDUghwADAHqbtpYs7Vvq+ByTfNN5Z0J+RyMKOn64kBvya8qgjh8u9M9K2+XfWcPyQxqW3/FaD9iOcB0Auhm7eYPcBT+WtmyU+doVcg47xuuSPFdWGdUL5dtU3jorvW2BodtPKVVxdpomF2Uqku5TaSSkIZGQsoOp0yPSA3u/oI3PSfTA29lbm7t6FVPa9WvWQb+TXChsZ3LT/XrgzOHJr11rk6+wS4nXDm87pUQtcZucdR+L22SfxoIMvy47tHD7g4G4VbPrJme3ZQQcjSvMTJzvuslj2oLv2ua4VlU2pjwYiMWtDioOq39Wmj7Y0qi7dvI6460nDlFpnk9L19bu9HXHtt+7v7xXqT/8d0sydG8L4G+cNkg5Ib9eKNumf6+p2R7ct4b03ziorwI+R8sr6vRhZTT14YBjdHRJtowxWrutSbXRuPztZu6n+bYvgtS+HQAA9AZN/zOrO9riKiPgaGBr6Pv8h9VqiLWF34n9w/NDOrokR3HX6voXPk4JvqMtrk4YnqvzJ/RRY8zVBY+u3uEzzx1XoHPGFyja4mrR8sTCe34nsfBl0O9oTOvMxIAvEXQH/YHWgDsRbg9sXZhwQHaarpjSf3tbFF+i/UnbLLzx/TL06LkjOxzTR/VJ16g+Hfde7hdOU78wfb8BAMB2hOsA0I3YD9+Te9tNkuvKueIGmRFjvS6p0zTGXH1UHVVZZZPKqhIh+tcP7KsxfTO0qT6mv62q1pDcoI4Ykq2SSFClkVCyn96Ewsxki5aezjGJhcTaBHxGA7I7ngWXHfJr+i5eIx+YE9S3D+/f4f4JhZnJhdJ25nODszSpKHN7cB93FXO3h/uT+mfq6iOKUoL5mGuTswSH5AY1fVhOcnvbDP9A66sBTe3b9cStYq3X/8ZB/SRJr62v01//p3+tz0jHtC7etnjlVr1QVpOyP5zm6PdnjZCUWBzupY9r5TNK9twvDAf0ixOGSJJ+81piEaWAz0n23C8MB3TRgYnPf+K9Sm1paElp69Mnc/siTW98Uq/mFjc56z/N5yg76Ev2zmxbYKntfEJ+AO0XwmwLwq0Si+pJ0opP6rWlIZYMx5taXOWG/Dp5ZGLBv7te26D1Nc0p548sSNeVUxNvaV38lzJtbWxJ+cypg7J0Ves6JL9dvin59pRjEjPnJOnokhz5HCNrrcJpPhVk+JOzv0vzEjPfQn5Hsw7ulxKMh/zbw+9Iul9/OGu4gn4nZfZemz6ZgeQidDuTHfLr6JKcDvczhgIAgH2NcB0Augn7+r/l/vZWKZIv5/LrZQo7nlnV3VVHW1RWGVVBZkCDcoIqq4zqyqc/SvY5zwr6VBoJJr8+qDisP31xBP9p7oICPke5vo7fDCjMSlPhLhZhGl+YqfG7eDBy4oiIThwR6XD/Nw/qp4sm91OsddZ9on3O9o75ZxyQr2NKclJm9bd35JAsDYkEU4L7jMD2Nx/SAz6l+x01x61qW9wdzn9lXZ3e35KY2d+2Z0yf9GS4fs/rG7WupjnlnMn9M3V963oAc57+SFsatodcjkn0jpzTGoJ956/lyYcNbbP6Dx4QTr4N8auXPml9MNDaj98xOqBvuiYXhRV3rZ77sDq5Pa119n7bwlhx12p9TfP24N9JvD0Q9Dn8rAF7oMW1KTO42x4qflQV1fra5uSs72jMlSvpzNa+r0++X6m3NzaosV34nRlwdNP0wZKkeX9fp/9U1Kd81oDsNN12Sqkk6cG3tmjl5sbkPiNpdJ/0ZLhe0xRXtMUq3W+UG0rMAC9t10v17HEFanGtgu3C74KM7f9tXHhyidJae4HvbI2Otjp3xucYnbSLMdsxRpm7ar4LAADQxRCuA0AXZ62VfeZR2Ufvk4aNlnPJNTJZu16YpbuJxV09+NZWlVVFVVbVpKrWGXMzx+Tpgkl9VZydpnPGFyRnpBdk+FP+M7+z2W2AlFhoKOCTAj6ftJP1kAZkB3c5s3/KoGxN2cX1vzyxzy4//8bWRY6stYpbqTnuyrbL3685aoAaW9zEjP7W2ffhdm2Lzp/QR/XN8ZSZ/W3tDyRpWF5IDTE3Oau/Je4q3tp337VWb29sUMxNbG97gNDi5mlyUVhNcXenC/OdMy5f547vo+poi2Y/Vb7D/q9O6qPTx+Trk9rmxGK7bQvttrbkOWdcgQ4flKWKmmYtWr4xub3tAcC0obkalh/SprqY/rWmJuXBQMDn6IC+6crPCKimKa7125paW/Zsv0ZuyKeAz5FrrYzEehPYK3HXqimeCL+zgz75HaMtDTGtrW7aHn63uGqKuzp+WK4yAj69vr5OS9fWJFqexLbvv2naYKUHHP1uxWYtfndrcg2MNm3tSJ5eVa1nVqW+UZPmM8lwfXN9iypqYslwO5wRUF67nqtHDcnW6D7p2/t++52UdmdXTCmSlW3tF+4ozZcagH/3c7t+OP/54bteFDM/g8XTAQAA2hCuA0AXZltaZP9wp+y/npU5+AiZC78tE+ievT5jcat1NU0qa+2LXlYV1eDcoGYdXCi/Y/Tsh9XKDfo1oTBDpZFQIkhvfY086Hd0zrgCj+8A+OyMMfIbye+kzsgsyt71z/OxpR23N5Ckyw7ruGWPY4zunrFjyx63Nd0P+R0tOn3oDi152kK8cJpP3/1cUXK2f9sDgLH9MpLnH12S09rP300eF2pdw6A57mprQ0tye6Kfv9XE/pkalh9SRW2z7l+xeYf6rjt6gPIzAnpnU4Pm/3P9Dvt/Mn2QDuibob+X1+hXL32SshBummN03bEDNSgnqOMcQOMAACAASURBVGVra/TEe1XJ7W199792YF/lpvv15oZ6rfikPuXBQJrP6NjSHAX9jtZua9Kmuli7fv6JhwCDcoNyjFFjzJVrt781QMi//7jWqqnFyu8k3oZpiMW1trq53axwV01xq0n9M9UnM6CPqqJ6tm1RzFjrMXGrbxzYV0MiIS1dU6M7XtuoppbEn9s2vzppiIZEQnrl4zr95vUdHzwdOiBLGQGfNtQ1660NDdv7evuNckN+xVt/tkb1SddpNm/7opiBxMKXbc4Yk68Th+emhOPt23ldOLmvLpzc8ffjqF20PZGUXDMCAAAA+x/hOgB0UbahXu5dN0srV8ic9EWZ086TcfZ+4c3O0BCL66OqJm1riuvwgVmSpO/+7SOVVzVJkoI+oyGRkPLTEwGAMUb/d/owWk0AnaBtMVrHmF3OQA36nWT7mp2JpPv1zdbe9jszJBLSrSeVdLh/fGGGHjp7RDJ0j7WG9201je6Trh8dOzC5vW12fnFrG6EhuUGdPS4/2e4ncUxi4cW2+/M5Rs0tVvXu9tn/bQHo6q1RPf5eVUqbIEk6YnC2gn7pxbJtenRl5Q51P3LuSDlGun/FppR+/n7HKCPgJBcYvu+NTfrP+vqUxXJzQj7NbZ01/NT7VVq7rWn7rH6fUU5we1/s5RV1qmmKt7bkSfT7D6f5NCw/8dBxc31M1ioZ7rddw/Eo5LfWyrWJth9tLYWSs75bZ4APzg1qUG5QNU1x/eXdypT9ja2LXh5UHNaa6ibd+Pd1KcG5JF05pb+OKsnRh5VR/XDJxzvU8IMji9UnM6DKxhb9vXybQj5HoUAi/A76HLX9VvfJDOhzg7IS4Xa7/ZHWB0uHDQyrNBLcHp4HtvcHl6QvjMzTF0bmdfi9OKg4rIOKwx3uT4TfBOAAAAA9AeE6AHRBdutmuQtvkDask/nq5XKmTvO6pN3610c1eunjWpVXRfVJbUxWUnbQp8MGhGWM0cwxidfdS/OC6h9O2yFIJ1gHepe2xXeDHfxrNDfk16T+Hf9TtTQvlHy7ZWcOG5ilw1of7u3MzAPyNfOAfLm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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-ytwooIVV1f"
      },
      "source": [
        "## Evaluation on the test dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6rmJ-tj0mHn",
        "outputId": "c59bdf4b-bafd-4420-8eba-bfaec9cefa8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609,
          "referenced_widgets": [
            "d20c4cc37d014b5d83c70595888f921b",
            "cb067d23c5d3453bb5a6411e1c2ade7a",
            "5270afa20b904bd7b871cccffbee55eb",
            "51b0159cf4404625a1ca7fcef9a38468",
            "cdf6a62d4635489da044788af291ccb4",
            "dc66cce01241472bae92660e06de941c",
            "aff709e673244076beeb736d2648f3b4",
            "a0564611b13e4962ad38e5b52c3bde8b",
            "8da3ae2d6851456a8eb56a1fcbacb98c",
            "5d3f396553674b4490d40c66fef7a5f5",
            "bee7a244972946888d198dab1e67eff3"
          ]
        }
      },
      "source": [
        "# Get model predictions on the test data\n",
        "true_labels, predictions_labels, avg_epoch_loss = validation (test_dataloader, device)\n",
        "\n",
        "# Create the evaluation report\n",
        "evaluation_report = classification_report (true_labels, predictions_labels, labels=list(labels_ids.values()), target_names=list(labels_ids.keys()))\n",
        "\n",
        "# Print the evaluation report\n",
        "print(evaluation_report)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_true=true_labels, y_pred=predictions_labels, \n",
        "                      classes=list(labels_ids.keys()), normalize=True, \n",
        "                      magnify=0.1, \n",
        "                      use_title='Normalized confusion matrix'\n",
        "                      );"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d20c4cc37d014b5d83c70595888f921b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.70      0.79      0.74       290\n",
            "     neutral       0.76      0.70      0.73       290\n",
            "    positive       0.78      0.76      0.77       290\n",
            "\n",
            "    accuracy                           0.75       870\n",
            "   macro avg       0.75      0.75      0.75       870\n",
            "weighted avg       0.75      0.75      0.75       870\n",
            "\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save and load the entire PyTorch model"
      ],
      "metadata": {
        "id": "Vbu0_YdyZrL_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "PATH = \"/content/full_model.pt\"\n",
        "torch.save(model, PATH)"
      ],
      "metadata": {
        "id": "S2cXrYAaZt2n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "the_model = torch.load(PATH)"
      ],
      "metadata": {
        "id": "qfwoYVZyaO6y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "the_model.eval()"
      ],
      "metadata": {
        "id": "l6tdTZXNu4vh",
        "outputId": "665be459-0b58-4979-e379-f766d89f6e3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XLMRobertaForSequenceClassification(\n",
              "  (roberta): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(250002, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): RobertaClassificationHead(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "    (out_proj): Linear(in_features=768, out_features=3, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    }
  ]
}