HopeSpeechEDI_XLMR_English.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elqe9PQrVH6P"
      },
      "source": [
        "### Performing checks for the resources available"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSELLChxa5su",
        "outputId": "6c03f63f-1810-486e-a20c-dfa8bae5e83b"
      },
      "source": [
        "import torch\r\n",
        "\r\n",
        "# If there's a GPU available...\r\n",
        "if torch.cuda.is_available():    \r\n",
        "\r\n",
        "    # Tell PyTorch to use the GPU.    \r\n",
        "    device = torch.device(\"cuda\")\r\n",
        "\r\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\r\n",
        "\r\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\r\n",
        "\r\n",
        "# If not...\r\n",
        "else:\r\n",
        "    print('No GPU available, using the CPU instead.')\r\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla T4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SokVowWo3cc",
        "outputId": "b367ddf1-9c6c-4c74-c691-dc510c7b1eb1"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Feb  5 06:38:51 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.39       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8    12W /  70W |     10MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-gbOgflVRxg"
      },
      "source": [
        "### Installing huggingface transformers library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DbDuxKy0a9Zl",
        "outputId": "755b964c-bf41-42c5-bdc2-abe0cbfdf5ae"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/88/b1/41130a228dd656a1a31ba281598a968320283f48d42782845f6ba567f00b/transformers-4.2.2-py3-none-any.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 15.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Collecting tokenizers==0.9.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 41.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 55.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=62cf2b076502f91f127face9610c73f447c1ad52740a7e83438fff3dcc012e21\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.2.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPwaYLiRSNPC"
      },
      "source": [
        "## Importing necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XdFrhr8SRG0"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np \r\n",
        "import random\r\n",
        "from sklearn.preprocessing import LabelEncoder\r\n",
        "from sklearn.utils.class_weight import compute_class_weight\r\n",
        "import time\r\n",
        "import datetime\r\n",
        "from sklearn.metrics import classification_report,confusion_matrix\r\n",
        "import random\r\n",
        "import time\r\n",
        "import torch.nn as nn\r\n",
        "from transformers import AutoModel , AutoModelForSequenceClassification, AutoConfig , AutoTokenizer , AdamW ,get_linear_schedule_with_warmup \r\n",
        "import torch\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "from matplotlib import rc\r\n",
        "from matplotlib.ticker import MaxNLocator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dROX3juhVCxC"
      },
      "source": [
        "## Setting some configuration values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kzafv4BzVCC9"
      },
      "source": [
        "# Use plot styling from seaborn.\r\n",
        "sns.set(style='darkgrid')\r\n",
        "\r\n",
        "# Increase the plot size and font size.\r\n",
        "sns.set(font_scale=1.5)\r\n",
        "plt.rcParams[\"figure.figsize\"] = (16,12)\r\n",
        "\r\n",
        "# Set the seed value all over the place to make this reproducible.\r\n",
        "seed_val = 42\r\n",
        "\r\n",
        "random.seed(seed_val)\r\n",
        "np.random.seed(seed_val)\r\n",
        "torch.manual_seed(seed_val)\r\n",
        "torch.cuda.manual_seed_all(seed_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2v1AIN_OVX4t"
      },
      "source": [
        "## Importing drive into the colaboratory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lofimyh2a_tp",
        "outputId": "a79f4325-488c-4e01-fc9a-b6423278c4ca"
      },
      "source": [
        "from google.colab import drive \r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOK-M00dVeW2"
      },
      "source": [
        "## Importing dataset directories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-y-JRPKBbCTv"
      },
      "source": [
        "from os import getcwd , listdir \r\n",
        "from os.path import join\r\n",
        "curr_dir = getcwd()\r\n",
        "drive_dir = join(curr_dir , 'drive','MyDrive')\r\n",
        "dataset_dir = join(drive_dir,'Codalab','HopeSpeech Detection For Equality,DiversityAnd Inclusion','Dataset')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YU1h8WhmqvK",
        "outputId": "1b8847dd-a83e-4ba1-e4d8-5e08b0012afb"
      },
      "source": [
        "listdir(dataset_dir)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['english_hope_dev.csv',\n",
              " 'english_hope_train.csv',\n",
              " 'malayalam_hope_dev.csv',\n",
              " 'malayalam_hope_train.csv',\n",
              " 'tamil_hope_first_dev.csv',\n",
              " 'tamil_hope_first_train.csv',\n",
              " 'tamil_hope_test.csv',\n",
              " 'malayalam_hope_test.csv',\n",
              " 'english_hope_test.csv',\n",
              " 'english_hope_test _labels.csv',\n",
              " 'malayalam_hope_test _labels.csv',\n",
              " 'tamil_hope_test _labels.csv',\n",
              " 'malayalam_hope_test_labels.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUPkZxjLVjLK"
      },
      "source": [
        "## Loading training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "joone3H4bINR",
        "outputId": "32785158-7ee7-4d3b-87f1-94446016d5e1"
      },
      "source": [
        "train_df_path = join(dataset_dir,'english_hope_train.csv')\r\n",
        "# Load the dataset into a pandas dataframe.\r\n",
        "train_df = pd.read_csv(train_df_path)\r\n",
        "\r\n",
        "# Report the number of sentences.\r\n",
        "print('Number of training sentences: {:,}\\n'.format(train_df.shape[0]))\r\n",
        "\r\n",
        "# Display 10 random rows from the data.\r\n",
        "train_df.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training sentences: 22,762\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22434</th>\n",
              "      <td>I want you to know that I watched this video w...</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9303</th>\n",
              "      <td>2020 is definitely the worst year I've witness...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14935</th>\n",
              "      <td>My grandmother was superbutch and straight.</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9722</th>\n",
              "      <td>@Doc LMAO Liberals to have to fight alongside ...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13345</th>\n",
              "      <td>​@Everything EVERYTHING!! Ironic how black peo...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7445</th>\n",
              "      <td>Me and my frnd literally had this discussion d...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5391</th>\n",
              "      <td>OMG sounds like a bunch of unattractive women ...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18821</th>\n",
              "      <td>To the ones who think or feel... not all relig...</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1847</th>\n",
              "      <td>that girl that doesn’t “agree” with homosexual...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11902</th>\n",
              "      <td>It would have been nice. I'm a female engineer...</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text            label\n",
              "22434  I want you to know that I watched this video w...      Hope_speech\n",
              "9303   2020 is definitely the worst year I've witness...  Non_hope_speech\n",
              "14935        My grandmother was superbutch and straight.  Non_hope_speech\n",
              "9722   @Doc LMAO Liberals to have to fight alongside ...  Non_hope_speech\n",
              "13345  ​@Everything EVERYTHING!! Ironic how black peo...  Non_hope_speech\n",
              "7445   Me and my frnd literally had this discussion d...  Non_hope_speech\n",
              "5391   OMG sounds like a bunch of unattractive women ...  Non_hope_speech\n",
              "18821  To the ones who think or feel... not all relig...      Hope_speech\n",
              "1847   that girl that doesn’t “agree” with homosexual...  Non_hope_speech\n",
              "11902  It would have been nice. I'm a female engineer...      Hope_speech"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4VW351225YK",
        "outputId": "d1529ff7-ba09-45b4-a879-bc72bada3bb8"
      },
      "source": [
        "train_df['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Non_hope_speech    20778\n",
              "Hope_speech         1962\n",
              "not-English           22\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooOv9QZtnLIF"
      },
      "source": [
        "\r\n",
        "le = LabelEncoder()\r\n",
        "train_df['label'] = le.fit_transform(train_df['label'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a89Bf4LpnigU",
        "outputId": "b60fc5a8-ba0c-4ab6-b071-2f44e9f36cd5"
      },
      "source": [
        "train_df['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    20778\n",
              "0     1962\n",
              "2       22\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvNeR4lTVnK0"
      },
      "source": [
        "### Loading `train_sentences` and `train_labels`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EufQZx2dbKMT"
      },
      "source": [
        "# Get the lists of sentences and their labels.\r\n",
        "train_sentences = train_df['text'].values\r\n",
        "train_labels = train_df['label'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnZSg5f7ZWs_"
      },
      "source": [
        "## Saving Class Names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83tTT-wbZa1o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4632c995-05c8-4444-f9c0-a6bb38500a88"
      },
      "source": [
        "class_names = le.inverse_transform(range(0,len(train_df['label'].unique())))\r\n",
        "class_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hope_speech', 'Non_hope_speech', 'not-English'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToUbAgZ9ap63"
      },
      "source": [
        "## Loading the validation data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "Dle8VzoAjOLS",
        "outputId": "8ed98309-bc0c-4c48-b873-c7d3ffbcd52b"
      },
      "source": [
        "val_df_path = join(dataset_dir,'english_hope_dev.csv')\r\n",
        "# Load the dataset into a pandas dataframe.\r\n",
        "val_df = pd.read_csv(val_df_path)\r\n",
        "\r\n",
        "# Report the number of sentences.\r\n",
        "print('Number of training sentences: {:,}\\n'.format(val_df.shape[0]))\r\n",
        "\r\n",
        "# Display 10 random rows from the data.\r\n",
        "val_df.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training sentences: 2,843\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2834</th>\n",
              "      <td>She is the last truly 'intellectual' pop star</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1189</th>\n",
              "      <td>Yeah bicth and ur coming with us hahaha</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1426</th>\n",
              "      <td>BLM are certainly doing their best to exaspera...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1620</th>\n",
              "      <td>Professor the police where called on him for a...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2687</th>\n",
              "      <td>WHY COULDN'T THEY FIX HER MIC</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2240</th>\n",
              "      <td>@Phoebe Reynolds Also</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>God does not hate gaysnHe does not hate anyone...</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2420</th>\n",
              "      <td>@TheMighty Derp nnStop trying to convince your...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842</th>\n",
              "      <td>@CLIP IT! woah woah woahhhhh chill. I'm bisexu...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>The brain mainly develops in the first 5 years...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text            label\n",
              "2834      She is the last truly 'intellectual' pop star  Non_hope_speech\n",
              "1189            Yeah bicth and ur coming with us hahaha  Non_hope_speech\n",
              "1426  BLM are certainly doing their best to exaspera...  Non_hope_speech\n",
              "1620  Professor the police where called on him for a...  Non_hope_speech\n",
              "2687                      WHY COULDN'T THEY FIX HER MIC  Non_hope_speech\n",
              "2240                              @Phoebe Reynolds Also  Non_hope_speech\n",
              "566   God does not hate gaysnHe does not hate anyone...      Hope_speech\n",
              "2420  @TheMighty Derp nnStop trying to convince your...  Non_hope_speech\n",
              "842   @CLIP IT! woah woah woahhhhh chill. I'm bisexu...  Non_hope_speech\n",
              "1862  The brain mainly develops in the first 5 years...  Non_hope_speech"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AypCmtBcDV6v",
        "outputId": "86f82aa4-e264-4b4f-b37c-a8be6335761e"
      },
      "source": [
        "val_df['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Non_hope_speech    2569\n",
              "Hope_speech         272\n",
              "not-English           2\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CgK81lGDPKC"
      },
      "source": [
        "val_df['label'] = le.transform(val_df['label'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yfgn-E6JDZQZ",
        "outputId": "5388dceb-8ec8-4c31-b8cd-7239872145ea"
      },
      "source": [
        "val_df['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2569\n",
              "0     272\n",
              "2       2\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMdNNVqrDCuN"
      },
      "source": [
        "### Loading `val_sentences` and `val_labels`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvGhjUkaDCuO"
      },
      "source": [
        "# Get the lists of sentences and their labels.\r\n",
        "val_sentences = val_df['text'].values\r\n",
        "val_labels = val_df['label'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mgM6b0yL0NM"
      },
      "source": [
        "## Loading the test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "d7tVWiIeL0NN",
        "outputId": "1664f78e-e9f5-4b6b-beff-343d88c132e6"
      },
      "source": [
        "test_df_path = join(dataset_dir,'english_hope_test _labels.csv')\r\n",
        "# Load the dataset into a pandas dataframe.\r\n",
        "test_df = pd.read_csv(test_df_path , sep = ';')\r\n",
        "\r\n",
        "# Report the number of sentences.\r\n",
        "print('Number of test sentences: {:,}\\n'.format(test_df.shape[0]))\r\n",
        "\r\n",
        "# Display 10 random rows from the data.\r\n",
        "test_df.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of test sentences: 2,846\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2740</th>\n",
              "      <td>BLM-- They have to be the victim's!!</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2476</th>\n",
              "      <td>We must..... Abandon hope!</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1526</th>\n",
              "      <td>Jesus wants to change your heart.nBLM is hurti...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1189</th>\n",
              "      <td>It was 80 YEARS after black people were given ...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1743</th>\n",
              "      <td>I think you need to run in the future keep in ...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2276</th>\n",
              "      <td>Someone's appearance doesn't define their sexu...</td>\n",
              "      <td>Hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1770</th>\n",
              "      <td>I think the statue spread coronanIt had to be ...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>Olivia Tyler same?  no I’m already out to some...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2318</th>\n",
              "      <td>if this was reversed</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1477</th>\n",
              "      <td>we're doing a promotional video on why ppl sho...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text  ... Unnamed: 2\n",
              "2740               BLM-- They have to be the victim's!!  ...        NaN\n",
              "2476                         We must..... Abandon hope!  ...        NaN\n",
              "1526  Jesus wants to change your heart.nBLM is hurti...  ...        NaN\n",
              "1189  It was 80 YEARS after black people were given ...  ...        NaN\n",
              "1743  I think you need to run in the future keep in ...  ...        NaN\n",
              "2276  Someone's appearance doesn't define their sexu...  ...        NaN\n",
              "1770  I think the statue spread coronanIt had to be ...  ...        NaN\n",
              "276   Olivia Tyler same?  no I’m already out to some...  ...        NaN\n",
              "2318                               if this was reversed  ...        NaN\n",
              "1477  we're doing a promotional video on why ppl sho...  ...        NaN\n",
              "\n",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkt73boTL0NO",
        "outputId": "5d3a83f6-1766-4715-edc8-a54a1dceecd2"
      },
      "source": [
        "test_df['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Non_hope_speech    2593\n",
              "Hope_speech         250\n",
              "not-English           3\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ProewaglL0NP"
      },
      "source": [
        "test_df['label'] = le.transform(test_df['label'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYR2XokSL0NP",
        "outputId": "cf826d14-b03b-488d-8912-bd355fe381a1"
      },
      "source": [
        "test_df['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2593\n",
              "0     250\n",
              "2       3\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCCNrKNmL0NQ"
      },
      "source": [
        "### Loading `test_sentences` and `test_labels`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvBSOGdkL0NR"
      },
      "source": [
        "# Get the lists of sentences and their labels.\r\n",
        "test_sentences = test_df['text'].values\r\n",
        "test_labels = test_df['label'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fS2UcOiEVuhm"
      },
      "source": [
        "## Handling class imbalance using `sklearn's compute_class_weight`\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WL4tP_YkI1mY",
        "outputId": "98c0fd9b-1688-4106-f66d-3fe09a28b56f"
      },
      "source": [
        "\r\n",
        "#compute the class weights\r\n",
        "class_wts = compute_class_weight('balanced', np.unique(train_labels), train_labels)\r\n",
        "\r\n",
        "print(class_wts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.85137769 0.68428184 2.74689784]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_WjMTuibsrf"
      },
      "source": [
        "## Helper Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bef28eHNV-_d"
      },
      "source": [
        "#### 1) Update Interval Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBK0ujfEbMir"
      },
      "source": [
        "def good_update_interval(total_iters, num_desired_updates):\r\n",
        "    '''\r\n",
        "    This function will try to pick an intelligent progress update interval \r\n",
        "    based on the magnitude of the total iterations.\r\n",
        "\r\n",
        "    Parameters:\r\n",
        "      `total_iters` - The number of iterations in the for-loop.\r\n",
        "      `num_desired_updates` - How many times we want to see an update over the \r\n",
        "                              course of the for-loop.\r\n",
        "    '''\r\n",
        "    # Divide the total iterations by the desired number of updates. Most likely\r\n",
        "    # this will be some ugly number.\r\n",
        "    exact_interval = total_iters / num_desired_updates\r\n",
        "\r\n",
        "    # The `round` function has the ability to round down a number to, e.g., the\r\n",
        "    # nearest thousandth: round(exact_interval, -3)\r\n",
        "    #\r\n",
        "    # To determine the magnitude to round to, find the magnitude of the total,\r\n",
        "    # and then go one magnitude below that.\r\n",
        "\r\n",
        "    # Get the order of magnitude of the total.\r\n",
        "    order_of_mag = len(str(total_iters)) - 1\r\n",
        "\r\n",
        "    # Our update interval should be rounded to an order of magnitude smaller. \r\n",
        "    round_mag = order_of_mag - 1\r\n",
        "\r\n",
        "    # Round down and cast to an int.\r\n",
        "    update_interval = int(round(exact_interval, -round_mag))\r\n",
        "\r\n",
        "    # Don't allow the interval to be zero!\r\n",
        "    if update_interval == 0:\r\n",
        "        update_interval = 1\r\n",
        "\r\n",
        "    return update_interval"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eMGycwGAWFyO"
      },
      "source": [
        "#### 2) Format time function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9zVyNQLm-3q"
      },
      "source": [
        "def format_time(elapsed):\r\n",
        "    '''\r\n",
        "    Takes a time in seconds and returns a string hh:mm:ss\r\n",
        "    '''\r\n",
        "    # Round to the nearest second.\r\n",
        "    elapsed_rounded = int(round((elapsed)))\r\n",
        "    \r\n",
        "    # Format as hh:mm:ss\r\n",
        "    return str(datetime.timedelta(seconds=elapsed_rounded))\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBAo04ENWw3n"
      },
      "source": [
        "#### 3) Make Smart Batches function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nof-AUeoiZc2"
      },
      "source": [
        "def make_smart_batches(text_samples, labels, batch_size,tokenizer,max_input_length):\r\n",
        "    '''\r\n",
        "    This function combines all of the required steps to prepare batches.\r\n",
        "    '''\r\n",
        "\r\n",
        "    print('Creating Smart Batches from {:,} examples with batch size {:,}...\\n'.format(len(text_samples), batch_size))\r\n",
        "\r\n",
        "    # =========================\r\n",
        "    #   Tokenize & Truncate\r\n",
        "    # =========================\r\n",
        "\r\n",
        "    full_input_ids = []\r\n",
        "\r\n",
        "    # Tokenize all training examples\r\n",
        "    print('Tokenizing {:,} samples...'.format(len(labels)))\r\n",
        "\r\n",
        "    # Choose an interval on which to print progress updates.\r\n",
        "    update_interval = good_update_interval(total_iters=len(labels), num_desired_updates=10)\r\n",
        "\r\n",
        "    # For each training example...\r\n",
        "    for text in text_samples:\r\n",
        "        \r\n",
        "        # Report progress.\r\n",
        "        if ((len(full_input_ids) % update_interval) == 0):\r\n",
        "            print('  Tokenized {:,} samples.'.format(len(full_input_ids)))\r\n",
        "\r\n",
        "        # Tokenize the sample.\r\n",
        "        input_ids = tokenizer.encode(text=text,              # Text to encode.\r\n",
        "                                    add_special_tokens=True, # Do add specials.\r\n",
        "                                    max_length=max_input_length,      \r\n",
        "                                    truncation=True,         # Do Truncate!\r\n",
        "                                    padding=False)           # DO NOT pad.\r\n",
        "                                    \r\n",
        "        # Add the tokenized result to our list.\r\n",
        "        full_input_ids.append(input_ids)\r\n",
        "        \r\n",
        "    print('DONE.')\r\n",
        "    print('{:>10,} samples\\n'.format(len(full_input_ids)))\r\n",
        "\r\n",
        "    # =========================\r\n",
        "    #      Select Batches\r\n",
        "    # =========================    \r\n",
        "\r\n",
        "    # Sort the two lists together by the length of the input sequence.\r\n",
        "    samples = sorted(zip(full_input_ids, labels), key=lambda x: len(x[0]))\r\n",
        "\r\n",
        "    print('{:>10,} samples after sorting\\n'.format(len(samples)))\r\n",
        "\r\n",
        "    import random\r\n",
        "\r\n",
        "    # List of batches that we'll construct.\r\n",
        "    batch_ordered_sentences = []\r\n",
        "    batch_ordered_labels = []\r\n",
        "\r\n",
        "    print('Creating batches of size {:}...'.format(batch_size))\r\n",
        "\r\n",
        "    # Choose an interval on which to print progress updates.\r\n",
        "    update_interval = good_update_interval(total_iters=len(samples), num_desired_updates=10)\r\n",
        "    \r\n",
        "    # Loop over all of the input samples...    \r\n",
        "    while len(samples) > 0:\r\n",
        "        \r\n",
        "        # Report progress.\r\n",
        "        if ((len(batch_ordered_sentences) % update_interval) == 0 \\\r\n",
        "            and not len(batch_ordered_sentences) == 0):\r\n",
        "            print('  Selected {:,} batches.'.format(len(batch_ordered_sentences)))\r\n",
        "\r\n",
        "        # `to_take` is our actual batch size. It will be `batch_size` until \r\n",
        "        # we get to the last batch, which may be smaller. \r\n",
        "        to_take = min(batch_size, len(samples))\r\n",
        "\r\n",
        "        # Pick a random index in the list of remaining samples to start\r\n",
        "        # our batch at.\r\n",
        "        select = random.randint(0, len(samples) - to_take)\r\n",
        "\r\n",
        "        # Select a contiguous batch of samples starting at `select`.\r\n",
        "        #print(\"Selecting batch from {:} to {:}\".format(select, select+to_take))\r\n",
        "        batch = samples[select:(select + to_take)]\r\n",
        "\r\n",
        "        #print(\"Batch length:\", len(batch))\r\n",
        "\r\n",
        "        # Each sample is a tuple--split them apart to create a separate list of \r\n",
        "        # sequences and a list of labels for this batch.\r\n",
        "        batch_ordered_sentences.append([s[0] for s in batch])\r\n",
        "        batch_ordered_labels.append([s[1] for s in batch])\r\n",
        "\r\n",
        "        # Remove these samples from the list.\r\n",
        "        del samples[select:select + to_take]\r\n",
        "\r\n",
        "    print('\\n  DONE - Selected {:,} batches.\\n'.format(len(batch_ordered_sentences)))\r\n",
        "\r\n",
        "    # =========================\r\n",
        "    #        Add Padding\r\n",
        "    # =========================    \r\n",
        "\r\n",
        "    print('Padding out sequences within each batch...')\r\n",
        "\r\n",
        "    py_inputs = []\r\n",
        "    py_attn_masks = []\r\n",
        "    py_labels = []\r\n",
        "\r\n",
        "    # For each batch...\r\n",
        "    for (batch_inputs, batch_labels) in zip(batch_ordered_sentences, batch_ordered_labels):\r\n",
        "\r\n",
        "        # New version of the batch, this time with padded sequences and now with\r\n",
        "        # attention masks defined.\r\n",
        "        batch_padded_inputs = []\r\n",
        "        batch_attn_masks = []\r\n",
        "        \r\n",
        "        # First, find the longest sample in the batch. \r\n",
        "        # Note that the sequences do currently include the special tokens!\r\n",
        "        max_size = max([len(sen) for sen in batch_inputs])\r\n",
        "\r\n",
        "        # For each input in this batch...\r\n",
        "        for sen in batch_inputs:\r\n",
        "            \r\n",
        "            # How many pad tokens do we need to add?\r\n",
        "            num_pads = max_size - len(sen)\r\n",
        "\r\n",
        "            # Add `num_pads` padding tokens to the end of the sequence.\r\n",
        "            padded_input = sen + [tokenizer.pad_token_id]*num_pads\r\n",
        "\r\n",
        "            # Define the attention mask--it's just a `1` for every real token\r\n",
        "            # and a `0` for every padding token.\r\n",
        "            attn_mask = [1] * len(sen) + [0] * num_pads\r\n",
        "\r\n",
        "            # Add the padded results to the batch.\r\n",
        "            batch_padded_inputs.append(padded_input)\r\n",
        "            batch_attn_masks.append(attn_mask)\r\n",
        "\r\n",
        "        # Our batch has been padded, so we need to save this updated batch.\r\n",
        "        # We also need the inputs to be PyTorch tensors, so we'll do that here.\r\n",
        "        # Todo - Michael's code specified \"dtype=torch.long\"\r\n",
        "        py_inputs.append(torch.tensor(batch_padded_inputs))\r\n",
        "        py_attn_masks.append(torch.tensor(batch_attn_masks))\r\n",
        "        py_labels.append(torch.tensor(batch_labels))\r\n",
        "    \r\n",
        "    print('  DONE.')\r\n",
        "\r\n",
        "    # Return the smart-batched dataset!\r\n",
        "    return (py_inputs, py_attn_masks, py_labels)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5816-I6miXJ1"
      },
      "source": [
        "### 4) Make Smart Batches (On Test Set)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggH8AkAEaV9N"
      },
      "source": [
        "def make_smart_batches_on_test(text_samples, text_ids, batch_size,tokenizer,max_input_length):\r\n",
        "    '''\r\n",
        "    This function combines all of the required steps to prepare batches.\r\n",
        "    '''\r\n",
        "\r\n",
        "    print('Creating Smart Batches from {:,} examples with batch size {:,}...\\n'.format(len(text_samples), batch_size))\r\n",
        "\r\n",
        "    # =========================\r\n",
        "    #   Tokenize & Truncate\r\n",
        "    # =========================\r\n",
        "\r\n",
        "    full_input_ids = []\r\n",
        "\r\n",
        "    # Tokenize all training examples\r\n",
        "    print('Tokenizing {:,} samples...'.format(len(text_samples)))\r\n",
        "\r\n",
        "    # Choose an interval on which to print progress updates.\r\n",
        "    update_interval = good_update_interval(total_iters=len(text_samples), num_desired_updates=10)\r\n",
        "\r\n",
        "    # For each training example...\r\n",
        "    for text in text_samples:\r\n",
        "        \r\n",
        "        # Report progress.\r\n",
        "        if ((len(full_input_ids) % update_interval) == 0):\r\n",
        "            print('  Tokenized {:,} samples.'.format(len(full_input_ids)))\r\n",
        "\r\n",
        "        # Tokenize the sample.\r\n",
        "        input_ids = tokenizer.encode(text=text,              # Text to encode.\r\n",
        "                                    add_special_tokens=True, # Do add specials.\r\n",
        "                                    max_length=max_input_length,      \r\n",
        "                                    truncation=True,         # Do Truncate!\r\n",
        "                                    padding=False)           # DO NOT pad.\r\n",
        "                                    \r\n",
        "        # Add the tokenized result to our list.\r\n",
        "        full_input_ids.append(input_ids)\r\n",
        "        \r\n",
        "    print('DONE.')\r\n",
        "    print('{:>10,} samples\\n'.format(len(full_input_ids)))\r\n",
        "\r\n",
        "    # =========================\r\n",
        "    #      Select Batches\r\n",
        "    # =========================    \r\n",
        "\r\n",
        "    # Sort the two lists together by the length of the input sequence.\r\n",
        "    samples = sorted(zip(full_input_ids, text_ids), key=lambda x: len(x[0]))\r\n",
        "\r\n",
        "    print('{:>10,} samples after sorting\\n'.format(len(samples)))\r\n",
        "\r\n",
        "    import random\r\n",
        "\r\n",
        "    # List of batches that we'll construct.\r\n",
        "    batch_ordered_sentences = []\r\n",
        "    batch_ordered_ids = []\r\n",
        "\r\n",
        "    print('Creating batches of size {:}...'.format(batch_size))\r\n",
        "\r\n",
        "    # Choose an interval on which to print progress updates.\r\n",
        "    update_interval = good_update_interval(total_iters=len(samples), num_desired_updates=10)\r\n",
        "    \r\n",
        "    # Loop over all of the input samples...    \r\n",
        "    while len(samples) > 0:\r\n",
        "        \r\n",
        "        # Report progress.\r\n",
        "        if ((len(batch_ordered_sentences) % update_interval) == 0 \\\r\n",
        "            and not len(batch_ordered_sentences) == 0):\r\n",
        "            print('  Selected {:,} batches.'.format(len(batch_ordered_sentences)))\r\n",
        "\r\n",
        "        # `to_take` is our actual batch size. It will be `batch_size` until \r\n",
        "        # we get to the last batch, which may be smaller. \r\n",
        "        to_take = min(batch_size, len(samples))\r\n",
        "\r\n",
        "        # Pick a random index in the list of remaining samples to start\r\n",
        "        # our batch at.\r\n",
        "        select = random.randint(0, len(samples) - to_take)\r\n",
        "\r\n",
        "        # Select a contiguous batch of samples starting at `select`.\r\n",
        "        #print(\"Selecting batch from {:} to {:}\".format(select, select+to_take))\r\n",
        "        batch = samples[select:(select + to_take)]\r\n",
        "\r\n",
        "        #print(\"Batch length:\", len(batch))\r\n",
        "\r\n",
        "        # Each sample is a tuple--split them apart to create a separate list of \r\n",
        "        # sequences and a list of labels for this batch.\r\n",
        "        batch_ordered_sentences.append([s[0] for s in batch])\r\n",
        "        batch_ordered_ids.append([s[1] for s in batch])\r\n",
        "\r\n",
        "        # Remove these samples from the list.\r\n",
        "        del samples[select:select + to_take]\r\n",
        "\r\n",
        "    print('\\n  DONE - Selected {:,} batches.\\n'.format(len(batch_ordered_sentences)))\r\n",
        "\r\n",
        "    # =========================\r\n",
        "    #        Add Padding\r\n",
        "    # =========================    \r\n",
        "\r\n",
        "    print('Padding out sequences within each batch...')\r\n",
        "\r\n",
        "    py_inputs = []\r\n",
        "    py_attn_masks = []\r\n",
        "    py_ids = []\r\n",
        "\r\n",
        "    # For each batch...\r\n",
        "    for (batch_inputs, batch_ids) in zip(batch_ordered_sentences, batch_ordered_ids):\r\n",
        "\r\n",
        "        # New version of the batch, this time with padded sequences and now with\r\n",
        "        # attention masks defined.\r\n",
        "        batch_padded_inputs = []\r\n",
        "        batch_attn_masks = []\r\n",
        "        \r\n",
        "        # First, find the longest sample in the batch. \r\n",
        "        # Note that the sequences do currently include the special tokens!\r\n",
        "        max_size = max([len(sen) for sen in batch_inputs])\r\n",
        "\r\n",
        "        # For each input in this batch...\r\n",
        "        for sen in batch_inputs:\r\n",
        "            \r\n",
        "            # How many pad tokens do we need to add?\r\n",
        "            num_pads = max_size - len(sen)\r\n",
        "\r\n",
        "            # Add `num_pads` padding tokens to the end of the sequence.\r\n",
        "            padded_input = sen + [tokenizer.pad_token_id]*num_pads\r\n",
        "\r\n",
        "            # Define the attention mask--it's just a `1` for every real token\r\n",
        "            # and a `0` for every padding token.\r\n",
        "            attn_mask = [1] * len(sen) + [0] * num_pads\r\n",
        "\r\n",
        "            # Add the padded results to the batch.\r\n",
        "            batch_padded_inputs.append(padded_input)\r\n",
        "            batch_attn_masks.append(attn_mask)\r\n",
        "\r\n",
        "        # Our batch has been padded, so we need to save this updated batch.\r\n",
        "        # We also need the inputs to be PyTorch tensors, so we'll do that here.\r\n",
        "        # Todo - Michael's code specified \"dtype=torch.long\"\r\n",
        "        py_inputs.append(torch.tensor(batch_padded_inputs))\r\n",
        "        py_attn_masks.append(torch.tensor(batch_attn_masks))\r\n",
        "        py_ids.append(torch.tensor(batch_ids))\r\n",
        "    \r\n",
        "    print('  DONE.')\r\n",
        "\r\n",
        "    # Return the smart-batched dataset!\r\n",
        "    return (py_inputs, py_attn_masks, py_ids)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6EHYpUbz8fo"
      },
      "source": [
        "### 5) Function for calculating accuracy per class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Knuw_DOei8RO"
      },
      "source": [
        "def accuracy_per_class(preds, labels , label_dict):\r\n",
        "    \r\n",
        "    preds_flat = np.argmax(preds, axis=1).flatten()\r\n",
        "    labels_flat = labels.flatten()\r\n",
        "\r\n",
        "    for label in np.unique(labels_flat):\r\n",
        "        y_preds = preds_flat[labels_flat==label]\r\n",
        "        y_true = labels_flat[labels_flat==label]\r\n",
        "        print(f'Class: {label_dict[label]}')\r\n",
        "        print(f'Accuracy: {round(100*len(y_preds[y_preds==label])/len(y_true) , 2)}\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOWk_8lpWO17"
      },
      "source": [
        "### 6) Function for plotting training history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7-FAyfnWM65"
      },
      "source": [
        "def plot_training_history(history):\r\n",
        "  fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 6))\r\n",
        "\r\n",
        "  ax1.plot(history['train_loss'], label='train loss')\r\n",
        "  ax1.plot(history['val_loss'], label='validation loss')\r\n",
        "\r\n",
        "  ax1.xaxis.set_major_locator(MaxNLocator(integer=True))\r\n",
        "  ax1.set_ylim([-0.05, 1.05])\r\n",
        "  ax1.legend()\r\n",
        "  ax1.set_ylabel('Loss')\r\n",
        "  ax1.set_xlabel('Epoch')\r\n",
        "\r\n",
        "  ax2.plot(history['train_acc'], label='train accuracy')\r\n",
        "  ax2.plot(history['val_acc'], label='validation accuracy')\r\n",
        "\r\n",
        "  ax2.xaxis.set_major_locator(MaxNLocator(integer=True))\r\n",
        "  ax2.set_ylim([-0.05, 1.05])\r\n",
        "  ax2.legend()\r\n",
        "\r\n",
        "  ax2.set_ylabel('Accuracy')\r\n",
        "  ax2.set_xlabel('Epoch')\r\n",
        "\r\n",
        "  fig.suptitle('Training history')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7QOTORh_JHa"
      },
      "source": [
        "### 7) Function to check accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzRDobQ6MwaR"
      },
      "source": [
        "def check_accuracy(predictions,true_labels):\r\n",
        "  # Combine the results across the batches.\r\n",
        "  predictions = np.concatenate(predictions, axis=0)\r\n",
        "  true_labels = np.concatenate(true_labels, axis=0)\r\n",
        "\r\n",
        "  # Choose the label with the highest score as our prediction.\r\n",
        "  preds = np.argmax(predictions, axis=1).flatten()\r\n",
        "\r\n",
        "  # Calculate simple flat accuracy -- number correct over total number.\r\n",
        "  accuracy = (preds == true_labels).mean()\r\n",
        "\r\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7TtFkV5_Olz"
      },
      "source": [
        "### 8) Function to evaluate model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSBNdYHYMA7Q"
      },
      "source": [
        "def eval_model(model,py_inputs, py_attn_masks, py_labels):\r\n",
        "# Prediction on test set\r\n",
        "  t0 = time.time()\r\n",
        "\r\n",
        "  print('Doing validation on {:,} sentences...'.format(len(py_labels)))\r\n",
        "\r\n",
        "  # Put model in evaluation mode\r\n",
        "  model.eval()\r\n",
        "\r\n",
        "  # Tracking variables \r\n",
        "  predictions , true_labels = [], []\r\n",
        "\r\n",
        "  # Choose an interval on which to print progress updates.\r\n",
        "  update_interval = good_update_interval(total_iters=len(py_inputs), num_desired_updates=10)\r\n",
        "\r\n",
        "  # Measure elapsed time.\r\n",
        "  t0 = time.time()\r\n",
        "  total_val_loss = 0\r\n",
        "\r\n",
        "  # Put model in evaluation mode\r\n",
        "  model.eval()\r\n",
        "\r\n",
        "  # For each batch of training data...\r\n",
        "  for step in range(0, len(py_inputs)):\r\n",
        "\r\n",
        "      # Progress update every 100 batches.\r\n",
        "      if step % update_interval == 0 and not step == 0:\r\n",
        "          # Calculate elapsed time in minutes.\r\n",
        "          elapsed = format_time(time.time() - t0)\r\n",
        "          \r\n",
        "          # Calculate the time remaining based on our progress.\r\n",
        "          steps_per_sec = (time.time() - t0) / step\r\n",
        "          remaining_sec = steps_per_sec * (len(py_inputs) - step)\r\n",
        "          remaining = format_time(remaining_sec)\r\n",
        "\r\n",
        "          # Report progress.\r\n",
        "          print('  Batch {:>7,}  of  {:>7,}.    Elapsed: {:}.  Remaining: {:}'.format(step, len(py_inputs), elapsed, remaining))\r\n",
        "\r\n",
        "      # Copy the batch to the GPU.\r\n",
        "      b_input_ids = py_inputs[step].to(device)\r\n",
        "      b_input_mask = py_attn_masks[step].to(device)\r\n",
        "      b_labels = py_labels[step].to(device)\r\n",
        "    \r\n",
        "      # Telling the model not to compute or store gradients, saving memory and \r\n",
        "      # speeding up prediction\r\n",
        "      with torch.no_grad():\r\n",
        "        output = model(b_input_ids, \r\n",
        "                             token_type_ids=None, \r\n",
        "                             attention_mask=b_input_mask, \r\n",
        "                             labels=b_labels)\r\n",
        "\r\n",
        "\r\n",
        "      logits = output.logits \r\n",
        "      loss = output.loss\r\n",
        "      # Move logits and labels to CPU\r\n",
        "      logits = logits.detach().cpu().numpy()\r\n",
        "      label_ids = b_labels.to('cpu').numpy()\r\n",
        "    \r\n",
        "      # Store predictions and true labels\r\n",
        "      predictions.append(logits)\r\n",
        "      true_labels.append(label_ids)\r\n",
        "\r\n",
        "      total_val_loss += loss.item()\r\n",
        "  \r\n",
        "  val_accuracy = check_accuracy(predictions,true_labels)\r\n",
        "\r\n",
        "  validation_time = format_time(time.time() - t0)\r\n",
        "  \r\n",
        "  # Calculate the average loss over all of the batches.\r\n",
        "  avg_val_loss = total_val_loss / len(py_inputs)     \r\n",
        "    \r\n",
        "\r\n",
        "  # print('Accuracy: {:.3f}'.format(val_accuracy))\r\n",
        "  return (avg_val_loss,val_accuracy,validation_time)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lrt_FyJIbC8j"
      },
      "source": [
        "### 9) Function for making predictions on our test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eylCkH2Zipq5"
      },
      "source": [
        "def get_predictions(py_inputs, py_attn_masks, py_labels):\r\n",
        "\r\n",
        "  print('Predicting labels for {:,} test batches...'.format(len(py_labels)))\r\n",
        "\r\n",
        "  # Put model in evaluation mode\r\n",
        "  model.eval()\r\n",
        "\r\n",
        "  # Tracking variables \r\n",
        "  predictions , true_labels = [], []\r\n",
        "\r\n",
        "  # Choose an interval on which to print progress updates.\r\n",
        "  update_interval = good_update_interval(total_iters=len(py_inputs), num_desired_updates=10)\r\n",
        "\r\n",
        "  # Measure elapsed time.\r\n",
        "  t0 = time.time()\r\n",
        "\r\n",
        "  # Put model in evaluation mode\r\n",
        "  model.eval()\r\n",
        "\r\n",
        "  # For each batch of training data...\r\n",
        "  for step in range(0, len(py_inputs)):\r\n",
        "\r\n",
        "      # Progress update every 100 batches.\r\n",
        "      if step % update_interval == 0 and not step == 0:\r\n",
        "          # Calculate elapsed time in minutes.\r\n",
        "          elapsed = format_time(time.time() - t0)\r\n",
        "          \r\n",
        "          # Calculate the time remaining based on our progress.\r\n",
        "          steps_per_sec = (time.time() - t0) / step\r\n",
        "          remaining_sec = steps_per_sec * (len(py_inputs) - step)\r\n",
        "          remaining = format_time(remaining_sec)\r\n",
        "\r\n",
        "          # Report progress.\r\n",
        "          print('  Batch {:>7,}  of  {:>7,}.    Elapsed: {:}.  Remaining: {:}'.format(step, len(py_inputs), elapsed, remaining))\r\n",
        "\r\n",
        "      # Copy the batch to the GPU.\r\n",
        "      b_input_ids = py_inputs[step].to(device)\r\n",
        "      b_input_mask = py_attn_masks[step].to(device)\r\n",
        "      b_labels = py_labels[step].to(device)\r\n",
        "    \r\n",
        "      # Telling the model not to compute or store gradients, saving memory and \r\n",
        "      # speeding up prediction\r\n",
        "      with torch.no_grad():\r\n",
        "          # Forward pass, calculate logit predictions\r\n",
        "          outputs = model(b_input_ids, b_input_mask)\r\n",
        "\r\n",
        "      logits = outputs.logits \r\n",
        "\r\n",
        "      # Move logits and labels to CPU\r\n",
        "      logits = logits.detach().cpu().numpy()\r\n",
        "      label_ids = b_labels.to('cpu').numpy()\r\n",
        "    \r\n",
        "      # Store predictions and true labels\r\n",
        "      predictions.append(logits)\r\n",
        "      true_labels.append(label_ids)\r\n",
        "      # Combine the results across the batches.\r\n",
        "  predictions = np.concatenate(predictions, axis=0)\r\n",
        "  true_labels = np.concatenate(true_labels, axis=0)\r\n",
        "\r\n",
        "      # Choose the label with the highest score as our prediction.\r\n",
        "  preds = np.argmax(predictions, axis=1).flatten()\r\n",
        "\r\n",
        "  return (preds,true_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiRNCS-EbZmy"
      },
      "source": [
        "### 10) Function for making predictions on test dataset(without labels)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIuwhVSmZvkd"
      },
      "source": [
        "def get_predictions_test(py_inputs, py_attn_masks,py_ids):\r\n",
        "\r\n",
        "  print('Predicting labels for {:,} test batches...'.format(len(py_inputs)))\r\n",
        "\r\n",
        "  # Put model in evaluation mode\r\n",
        "  model.eval()\r\n",
        "\r\n",
        "  # Tracking variables \r\n",
        "  predictions , true_labels ,ids = [], [] , []\r\n",
        "\r\n",
        "  # Choose an interval on which to print progress updates.\r\n",
        "  update_interval = good_update_interval(total_iters=len(py_inputs), num_desired_updates=10)\r\n",
        "\r\n",
        "  # Measure elapsed time.\r\n",
        "  t0 = time.time()\r\n",
        "\r\n",
        "  # Put model in evaluation mode\r\n",
        "  model.eval()\r\n",
        "\r\n",
        "  # For each batch of training data...\r\n",
        "  for step in range(0, len(py_inputs)):\r\n",
        "\r\n",
        "      # Progress update every 100 batches.\r\n",
        "      if step % update_interval == 0 and not step == 0:\r\n",
        "          # Calculate elapsed time in minutes.\r\n",
        "          elapsed = format_time(time.time() - t0)\r\n",
        "          \r\n",
        "          # Calculate the time remaining based on our progress.\r\n",
        "          steps_per_sec = (time.time() - t0) / step\r\n",
        "          remaining_sec = steps_per_sec * (len(py_inputs) - step)\r\n",
        "          remaining = format_time(remaining_sec)\r\n",
        "\r\n",
        "          # Report progress.\r\n",
        "          print('  Batch {:>7,}  of  {:>7,}.    Elapsed: {:}.  Remaining: {:}'.format(step, len(py_inputs), elapsed, remaining))\r\n",
        "\r\n",
        "      # Copy the batch to the GPU.\r\n",
        "      b_input_ids = py_inputs[step].to(device)\r\n",
        "      b_input_mask = py_attn_masks[step].to(device)\r\n",
        "      b_ids = py_ids[step].to(device)\r\n",
        "    \r\n",
        "      # Telling the model not to compute or store gradients, saving memory and \r\n",
        "      # speeding up prediction\r\n",
        "      with torch.no_grad():\r\n",
        "          # Forward pass, calculate logit predictions\r\n",
        "          outputs = model(b_input_ids, b_input_mask)\r\n",
        "\r\n",
        "      logits = outputs.logits \r\n",
        "\r\n",
        "      # Move logits and labels to CPU\r\n",
        "      logits = logits.detach().cpu().numpy()\r\n",
        "      b_ids = b_ids.detach().cpu().numpy()\r\n",
        "      # Store predictions\r\n",
        "      predictions.append(logits)\r\n",
        "      ids.append(b_ids)\r\n",
        "      # Combine the results across the batches.\r\n",
        "  predictions = np.concatenate(predictions, axis=0)\r\n",
        "  ids = np.concatenate(ids,axis=0)\r\n",
        "  # Choose the label with the highest score as our prediction.\r\n",
        "  preds = np.argmax(predictions, axis=1).flatten()\r\n",
        "\r\n",
        "  return ids,preds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IM8r_ddYZZS"
      },
      "source": [
        "### 11) Confusion Matrix Helper Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqNaS8SrBox5"
      },
      "source": [
        "def show_confusion_matrix(confusion_matrix, class_names):\r\n",
        "\r\n",
        "  cm = confusion_matrix.copy()\r\n",
        "\r\n",
        "  cell_counts = cm.flatten()\r\n",
        "\r\n",
        "  cm_row_norm = cm / cm.sum(axis=1)[:, np.newaxis]\r\n",
        "\r\n",
        "  row_percentages = [\"{0:.2f}\".format(value) for value in cm_row_norm.flatten()]\r\n",
        "\r\n",
        "  cell_labels = [f\"{cnt}\\n{per}\" for cnt, per in zip(cell_counts, row_percentages)]\r\n",
        "  cell_labels = np.asarray(cell_labels).reshape(cm.shape[0], cm.shape[1])\r\n",
        "\r\n",
        "  df_cm = pd.DataFrame(cm_row_norm, index=class_names, columns=class_names)\r\n",
        "\r\n",
        "  hmap = sns.heatmap(df_cm, annot=cell_labels, fmt=\"\", cmap=\"Blues\")\r\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\r\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\r\n",
        "  plt.ylabel('True Sign')\r\n",
        "  plt.xlabel('Predicted Sign');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBxHY4rMojd-"
      },
      "source": [
        "## Loading the tokenizer using AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
            "3cbd3124b0cf41249bbb18df85ae9f22",
            "826e671c5b5545d98af7eaa003782f37",
            "92153d10d12f48938e507ba66894732c",
            "e96556375e6f4c47bec16e45614380bc",
            "36174ee9ccc54fed9a515f67cdbf85f9",
            "bb6e75051c364b889e4cb4e057fc2a30",
            "decec5cd36284870afda70b5c9e770aa",
            "475d36693ec143e1b90e65be3c092759",
            "d55bb6a496b2417f8ffb7da3619b41c0",
            "f6d5ea908f6d41bcbdab6bf0eb3ab8b0",
            "efd3cb1e6a954294a89239c216b4e16f",
            "f0fe3911f610492f94a058140c07b41d",
            "09099e9b5dbf4f55a1159dccb566cfbe",
            "f17576b3840c42bdbcb650960d4b7a77",
            "ba22541d93684d709d0187aba8704993",
            "28bd4db2db884092ba1819ef30cd4e63",
            "d961f90b2ebc4983a9e11d85538e8849",
            "5aeea3007d5b45169c0ff35eb53f581c",
            "3ec453f6e6664ebc888fe6becebac701",
            "bb011bd25e7b4d5498c5cf1e2fd374cb",
            "ac0069be86c141fcb6e177aaf7266ef1",
            "51693f7322d14165a26a8feb4d98d34b",
            "0c44e5970886424f86583fa0cbb0bbf3",
            "5a4db6831abd4b54a144f9b34735dcc9"
          ]
        },
        "id": "fIlwEvcMb4pb",
        "outputId": "d3f6e6ea-610a-4fe4-ca56-f9ca901cf47a"
      },
      "source": [
        "model_name = 'xlm-roberta-base'\r\n",
        "# Load the BERT tokenizer.\r\n",
        "print(f'Loading {model_name} tokenizer...')\r\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading xlm-roberta-base tokenizer...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3cbd3124b0cf41249bbb18df85ae9f22",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=512.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d55bb6a496b2417f8ffb7da3619b41c0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=5069051.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d961f90b2ebc4983a9e11d85538e8849",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=9096718.0, style=ProgressStyle(descript…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVs3Jb3Vb89i"
      },
      "source": [
        "## Smart Batching of the training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWNt4eOLofmF"
      },
      "source": [
        "lengths = []\r\n",
        "for text in train_sentences:\r\n",
        "  lengths.append(len(text))       "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "hbdGRA3Bo02O",
        "outputId": "c9e8ef5f-c018-40f1-88d2-597143b1799d"
      },
      "source": [
        "\r\n",
        "# Use plot styling from seaborn.\r\n",
        "sns.set(style='darkgrid')\r\n",
        "\r\n",
        "# Increase the plot size and font size.\r\n",
        "sns.set(font_scale=1.5)\r\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\r\n",
        "\r\n",
        "plt.scatter(range(0, len(lengths)), lengths, marker=\"|\")\r\n",
        "\r\n",
        "plt.xlabel('Sample Number')\r\n",
        "plt.ylabel('Sequence Length')\r\n",
        "plt.title('Samples BEFORE Tokenizing')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rMhLNhPcZjU"
      },
      "source": [
        "### Tokenizing the sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7JsLi3EdEje"
      },
      "source": [
        "max_input_length = 400"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J968o8RJcUEd",
        "outputId": "378cbb78-a25e-43f7-9805-f168cdc31d6a"
      },
      "source": [
        "full_input_ids = []\r\n",
        "labels = []\r\n",
        "\r\n",
        "# Tokenize all training examples\r\n",
        "print('Tokenizing {:,} training samples...'.format(len(train_sentences)))\r\n",
        "\r\n",
        "# Choose an interval on which to print progress updates.\r\n",
        "update_interval = good_update_interval(total_iters=len(train_sentences), num_desired_updates=10)\r\n",
        "\r\n",
        "# For each training example...\r\n",
        "for text in train_sentences:\r\n",
        "    \r\n",
        "    # Report progress.\r\n",
        "    if ((len(full_input_ids) % update_interval) == 0):\r\n",
        "        print('  Tokenized {:,} samples.'.format(len(full_input_ids)))\r\n",
        "\r\n",
        "    # Tokenize the sentence.\r\n",
        "    input_ids = tokenizer.encode(text=text,           \r\n",
        "                                 add_special_tokens=True, \r\n",
        "                                 max_length=max_input_length,  \r\n",
        "                                 truncation=True,     \r\n",
        "                                 padding=False)       \r\n",
        "                                 \r\n",
        "    # Add the tokenized result to our list.\r\n",
        "    full_input_ids.append(input_ids)\r\n",
        "    \r\n",
        "print('DONE.')\r\n",
        "print('{:>10,} samples'.format(len(full_input_ids)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenizing 22,762 training samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 2,000 samples.\n",
            "  Tokenized 4,000 samples.\n",
            "  Tokenized 6,000 samples.\n",
            "  Tokenized 8,000 samples.\n",
            "  Tokenized 10,000 samples.\n",
            "  Tokenized 12,000 samples.\n",
            "  Tokenized 14,000 samples.\n",
            "  Tokenized 16,000 samples.\n",
            "  Tokenized 18,000 samples.\n",
            "  Tokenized 20,000 samples.\n",
            "  Tokenized 22,000 samples.\n",
            "DONE.\n",
            "    22,762 samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZzhO2tZeXur"
      },
      "source": [
        "# Get all of the lengths.\r\n",
        "unsorted_lengths = [len(x) for x in full_input_ids]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "x7c_mYuVed_H",
        "outputId": "ca53c61b-84b3-40a2-a5bf-00d93c567a61"
      },
      "source": [
        "\r\n",
        "# Use plot styling from seaborn.\r\n",
        "sns.set(style='darkgrid')\r\n",
        "\r\n",
        "# Increase the plot size and font size.\r\n",
        "sns.set(font_scale=1.5)\r\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\r\n",
        "\r\n",
        "plt.scatter(range(0, len(unsorted_lengths)), unsorted_lengths, marker=\"|\")\r\n",
        "\r\n",
        "plt.xlabel('Sample Number')\r\n",
        "plt.ylabel('Sequence Length')\r\n",
        "plt.title('Samples BEFORE Sorting')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PaeohUVegmv"
      },
      "source": [
        "# Sort the two lists together by the length of the input sequence.\r\n",
        "train_samples = sorted(zip(full_input_ids, train_labels), key=lambda x: len(x[0]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9nyx-BtepE4",
        "outputId": "64079651-7a47-43b6-8d9c-3037ad588f1b"
      },
      "source": [
        "train_samples[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[([0, 25689, 398, 2], 1),\n",
              " ([0, 1215, 5792, 2], 1),\n",
              " ([0, 73398, 33683, 2], 1),\n",
              " ([0, 73398, 33683, 2], 1),\n",
              " ([0, 2646, 46517, 2], 0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBxJhEieetFh",
        "outputId": "bf79a965-7524-4bdd-ca80-fccb63ae7659"
      },
      "source": [
        "print('Shortest sample:', len(train_samples[0][0]))\r\n",
        "print('Longest sample:', len(train_samples[-1][0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shortest sample: 4\n",
            "Longest sample: 335\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P44bFOI-e7hW"
      },
      "source": [
        "# Get the new list of lengths after sorting.\r\n",
        "sorted_lengths = [len(s[0]) for s in train_samples]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "9P5ZX99le_G1",
        "outputId": "a104aae6-9a3d-453f-d7ac-4ab437bc0dc1"
      },
      "source": [
        "\r\n",
        "plt.plot(range(0, len(sorted_lengths)), sorted_lengths)\r\n",
        "\r\n",
        "plt.xlabel('Sample Number')\r\n",
        "plt.ylabel('Sequence Length')\r\n",
        "plt.title('Samples after Sorting')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eh1z0xugfF2t"
      },
      "source": [
        "### Random Batch Selections"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPrYkFMcgm5H",
        "outputId": "4509a022-73d5-487f-8847-12e7c5153afd"
      },
      "source": [
        "train_samples[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 25689, 398, 2], 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isMxwgezfAvO"
      },
      "source": [
        "batch_size = 16"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0tjD8pIfJ0S",
        "outputId": "ede9a03c-3b0f-4101-c3f9-63dbbac909e3"
      },
      "source": [
        "# List of batches that we'll construct.\r\n",
        "batch_ordered_sentences = []\r\n",
        "batch_ordered_labels = []\r\n",
        "\r\n",
        "print('Creating training batches of size {:}'.format(batch_size))\r\n",
        "\r\n",
        "# Loop over all of the input samples...    \r\n",
        "while len(train_samples) > 0:\r\n",
        "    \r\n",
        "    # Report progress.\r\n",
        "    if ((len(batch_ordered_sentences) % 100) == 0):\r\n",
        "        print('  Selected {:,} batches.'.format(len(batch_ordered_sentences)))\r\n",
        "\r\n",
        "    # `to_take` is our actual batch size. It will be `batch_size` until \r\n",
        "    # we get to the last batch, which may be smaller. \r\n",
        "    to_take = min(batch_size, len(train_samples))\r\n",
        "\r\n",
        "    # Pick a random index in the list of remaining samples to start\r\n",
        "    # our batch at.\r\n",
        "    select = random.randint(0, len(train_samples) - to_take)\r\n",
        "\r\n",
        "    # Select a contiguous batch of samples starting at `select`.\r\n",
        "    batch = train_samples[select:(select + to_take)]\r\n",
        "\r\n",
        "    # Each sample is a tuple--split them apart to create a separate list of \r\n",
        "    # sequences and a list of labels for this batch.\r\n",
        "    batch_ordered_sentences.append([s[0] for s in batch])\r\n",
        "    batch_ordered_labels.append([s[1] for s in batch])\r\n",
        "\r\n",
        "    # Remove these samples from the list.\r\n",
        "    del train_samples[select:select + to_take]\r\n",
        "\r\n",
        "print('\\n  DONE - {:,} batches.'.format(len(batch_ordered_sentences)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating training batches of size 16\n",
            "  Selected 0 batches.\n",
            "  Selected 100 batches.\n",
            "  Selected 200 batches.\n",
            "  Selected 300 batches.\n",
            "  Selected 400 batches.\n",
            "  Selected 500 batches.\n",
            "  Selected 600 batches.\n",
            "  Selected 700 batches.\n",
            "  Selected 800 batches.\n",
            "  Selected 900 batches.\n",
            "  Selected 1,000 batches.\n",
            "  Selected 1,100 batches.\n",
            "  Selected 1,200 batches.\n",
            "  Selected 1,300 batches.\n",
            "  Selected 1,400 batches.\n",
            "\n",
            "  DONE - 1,423 batches.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rf91Dd3LhBaP",
        "outputId": "cfa7f24e-715d-4a38-ee68-d4b4824a9062"
      },
      "source": [
        "print(batch_ordered_sentences[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0, 1374, 104140, 53, 22392, 2174, 398, 13319, 1884, 738, 47, 82167, 3664, 5, 568, 444, 959, 15672, 17, 444, 1295, 14713, 1284, 6221, 2873, 398, 91, 77242, 15072, 23295, 3129, 8337, 398, 15381, 5, 5, 73852, 5, 4263, 398, 13319, 24310, 738, 47, 82167, 136, 74703, 43807, 2439, 7, 2], [0, 87, 2301, 25, 18, 3853, 3714, 2367, 47, 5154, 5, 32255, 621, 4552, 18775, 453, 136, 1836, 621, 7730, 4420, 183707, 5369, 6275, 214, 8966, 1810, 5, 4966, 77104, 7, 1884, 903, 2301, 25, 18, 75553, 56, 35743, 6637, 442, 22027, 25, 18, 11301, 3934, 70, 6107, 26908, 5, 2], [0, 221431, 10176, 66847, 538, 8677, 87, 26, 39, 388, 214, 78574, 38750, 7, 16454, 935, 89914, 87, 3871, 47, 2046, 903, 28271, 13580, 80560, 5, 87, 12301, 759, 60313, 190704, 136, 5161, 7730, 47, 106820, 64, 23562, 927, 10760, 7, 5, 1650, 14602, 959, 24145, 5154, 23, 70, 60313, 2], [0, 200681, 289, 15988, 10669, 717, 108, 42551, 17, 3714, 1632, 111, 604, 56002, 19612, 72004, 23, 70, 117249, 136, 764, 2804, 2412, 2806, 186085, 15504, 1257, 678, 54053, 62690, 3021, 3021, 99, 456, 90431, 2317, 7, 20653, 765, 116148, 425, 17496, 19922, 70, 182304, 12275, 2363, 398, 765, 442, 2], [0, 1374, 150567, 223, 23662, 1529, 192942, 26548, 70, 129058, 58984, 111, 208984, 70, 8999, 1902, 51592, 5, 1529, 168499, 297, 142, 64194, 23295, 26548, 41555, 5, 32635, 764, 509, 208984, 1284, 2174, 442, 58954, 25, 18, 100, 4049, 642, 25, 71, 68746, 25, 18, 186, 923, 272, 18925, 5, 2], [0, 2646, 44126, 83, 47, 5161, 15787, 35166, 6637, 3430, 83, 11954, 221, 450, 25, 7, 15400, 70, 738, 11337, 27117, 47, 186, 93403, 8305, 42219, 90, 136, 58309, 6953, 70, 98320, 106820, 90, 450, 70, 8, 5115, 32497, 23, 70, 9351, 111, 5, 116, 581, 7, 99507, 72004, 116, 2], [0, 88949, 83, 959, 29568, 5, 581, 60742, 111, 69496, 14, 34376, 136, 24793, 6183, 2301, 26, 18, 26866, 16916, 756, 70, 29700, 1631, 38, 32255, 3395, 3542, 552, 56, 37534, 1810, 111, 2363, 46002, 44556, 136, 7464, 38043, 23, 1492, 933, 23, 64879, 7, 1379, 102044, 136, 80097, 38, 2], [0, 78662, 509, 903, 10422, 111, 16000, 3229, 756, 8382, 187, 4806, 56776, 34695, 18782, 25251, 3542, 120332, 33, 214, 23607, 4295, 99, 2363, 180, 108049, 70060, 294, 32, 442, 25, 7, 1884, 113, 25, 5584, 4734, 18822, 70, 14994, 83, 2773, 3229, 442, 11177, 7, 935, 29361, 91, 10923, 2], [0, 169006, 711, 823, 24658, 30885, 83, 1660, 237, 110833, 18925, 237, 764, 509, 5369, 6650, 5, 11954, 450, 6, 42687, 32920, 170190, 38, 3164, 9251, 7, 26866, 136, 2174, 398, 2301, 25, 18, 18822, 450, 7068, 5646, 935, 27583, 104, 110410, 78574, 136, 738, 166, 2594, 10, 107466, 5, 2], [0, 9593, 18, 1884, 87, 25, 39, 100244, 10, 188272, 30154, 297, 1202, 678, 903, 5045, 280, 15145, 80280, 111, 903, 153161, 64, 191, 538, 93425, 24793, 5, 581, 64194, 1202, 83, 105950, 47, 10, 166513, 8035, 29114, 48141, 450, 1836, 15935, 25, 18, 2046, 2367, 15700, 200, 71, 6777, 2], [0, 18621, 38, 44084, 13695, 10, 33908, 13, 111, 3060, 28, 5115, 94859, 101785, 89547, 3493, 2301, 25, 18, 5154, 642, 1615, 6953, 32692, 74, 23, 18507, 642, 1221, 30698, 1672, 2856, 390, 114977, 1284, 2685, 25, 7, 110, 31635, 47, 76104, 10, 33908, 13, 111, 6097, 189026, 3395, 38, 2], [0, 3293, 509, 221, 113138, 136, 87, 77947, 77104, 7, 678, 34376, 5, 3293, 83, 10, 34923, 13580, 450, 398, 621, 20594, 136, 1632, 111, 70, 158036, 77104, 7, 87, 26, 272, 17669, 192509, 5, 159, 80060, 6056, 47, 3060, 111, 70, 27863, 6637, 70, 34376, 3542, 28875, 90638, 5, 2], [0, 54133, 100632, 294, 151157, 52136, 14755, 6706, 58837, 13055, 5, 19, 9713, 6, 5, 42563, 647, 6, 98660, 1723, 8562, 111, 151157, 52136, 14755, 6706, 5, 1301, 3430, 75935, 5, 5, 243238, 2750, 67153, 7, 26249, 237, 621, 67153, 1314, 2843, 27, 2347, 53, 4163, 59714, 23, 2363, 44540, 2], [0, 40469, 83, 28821, 6947, 32, 87, 765, 8306, 49782, 450, 9351, 8108, 5, 66348, 1916, 5, 87, 15935, 25, 18, 28219, 2684, 111, 935, 149357, 5, 4171, 398, 456, 50986, 442, 12960, 26785, 214, 70, 122092, 11, 70760, 121742, 7, 221, 70, 10588, 111, 1821, 28219, 32, 22362, 5, 2], [0, 873, 214, 10, 23040, 83, 959, 1660, 1672, 77233, 10576, 136, 54, 42458, 136, 7622, 478, 41884, 7, 5, 1650, 83, 1286, 3501, 450, 38, 1401, 756, 621, 105950, 5, 22362, 100, 68772, 903, 58867, 116483, 136, 53371, 2780, 38514, 678, 903, 8999, 5, 87, 444, 6183, 9525, 5, 2], [0, 581, 7565, 9146, 111, 70, 10542, 1221, 74918, 6637, 111, 1632, 20531, 31089, 450, 83, 8306, 165249, 5, 3493, 450, 83, 1111, 25, 7, 9251, 7, 110, 51713, 39162, 56, 5, 438, 3036, 7136, 168265, 1221, 1957, 70, 31110, 720, 2320, 111, 65925, 67081, 237, 642, 3714, 442, 5, 2]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmkQK_gVgiTu",
        "outputId": "81232603-68a8-431c-a521-63d5491b8d5f"
      },
      "source": [
        "batch_ordered_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZ0TFA8Mfn6m"
      },
      "source": [
        "### Padding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9rdswcpfTip"
      },
      "source": [
        "py_inputs = []\r\n",
        "py_attn_masks = []\r\n",
        "py_labels = []\r\n",
        "\r\n",
        "# For each batch...\r\n",
        "for (batch_inputs, batch_labels) in zip(batch_ordered_sentences, batch_ordered_labels):\r\n",
        "\r\n",
        "    # New version of the batch, this time with padded sequences and now with\r\n",
        "    # attention masks defined.\r\n",
        "    batch_padded_inputs = []\r\n",
        "    batch_attn_masks = []\r\n",
        "    \r\n",
        "    # First, find the longest sample in the batch. \r\n",
        "    # Note that the sequences do currently include the special tokens!\r\n",
        "    max_size = max([len(sen) for sen in batch_inputs])\r\n",
        "\r\n",
        "    #print('Max size:', max_size)\r\n",
        "\r\n",
        "    # For each input in this batch...\r\n",
        "    for sen in batch_inputs:\r\n",
        "        \r\n",
        "        # How many pad tokens do we need to add?\r\n",
        "        num_pads = max_size - len(sen)\r\n",
        "\r\n",
        "        # Add `num_pads` padding tokens to the end of the sequence.\r\n",
        "        padded_input = sen + [tokenizer.pad_token_id]*num_pads\r\n",
        "\r\n",
        "        # Define the attention mask--it's just a `1` for every real token\r\n",
        "        # and a `0` for every padding token.\r\n",
        "        attn_mask = [1] * len(sen) + [0] * num_pads\r\n",
        "\r\n",
        "        # Add the padded results to the batch.\r\n",
        "        batch_padded_inputs.append(padded_input)\r\n",
        "        batch_attn_masks.append(attn_mask)\r\n",
        "\r\n",
        "    # Our batch has been padded, so we need to save this updated batch.\r\n",
        "    # We also need the inputs to be PyTorch tensors, so we'll do that here.\r\n",
        "    py_inputs.append(torch.tensor(batch_padded_inputs))\r\n",
        "    py_attn_masks.append(torch.tensor(batch_attn_masks))\r\n",
        "    py_labels.append(torch.tensor(batch_labels))\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roS5lLWdX-5J"
      },
      "source": [
        "### Check the number of token reductions because of smart batching"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlh7cteVfroV",
        "outputId": "1fc63ebc-6ae2-4de5-edb8-c408a47737c0"
      },
      "source": [
        "# Get the new list of lengths after sorting.\r\n",
        "\r\n",
        "padded_lengths = []\r\n",
        "\r\n",
        "# For each batch...\r\n",
        "for batch in py_inputs:\r\n",
        "    \r\n",
        "    # For each sample...\r\n",
        "    for s in batch:\r\n",
        "    \r\n",
        "        # Record its length.\r\n",
        "        padded_lengths.append(len(s))\r\n",
        "\r\n",
        "# Sum up the lengths to the get the total number of tokens after smart batching.\r\n",
        "smart_token_count = np.sum(padded_lengths)\r\n",
        "\r\n",
        "# To get the total number of tokens in the dataset using fixed padding, it's\r\n",
        "# as simple as the number of samples times our `max_len` parameter (that we\r\n",
        "# would pad everything to).\r\n",
        "fixed_token_count = len(train_sentences) * max_input_length\r\n",
        "\r\n",
        "# Calculate the percentage reduction.\r\n",
        "prcnt_reduced = (fixed_token_count - smart_token_count) / float(fixed_token_count) \r\n",
        "\r\n",
        "print('Total tokens:')\r\n",
        "print('   Fixed Padding: {:,}'.format(fixed_token_count))\r\n",
        "print('  Smart Batching: {:,}  ({:.1%} less)'.format(smart_token_count, prcnt_reduced))\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total tokens:\n",
            "   Fixed Padding: 9,104,800\n",
            "  Smart Batching: 565,648  (93.8% less)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wgdhsI-YIAL"
      },
      "source": [
        "## Load the model configuration from the transformers library using AutoConfig"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9er7EHvdfudf",
        "outputId": "4d0f8311-0431-4b31-a385-a3b81626b6d7"
      },
      "source": [
        "# Load the Config object, with an output configured for classification.\r\n",
        "config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_name,\r\n",
        "                                    num_labels=len(class_names))\r\n",
        "\r\n",
        "print('Config type:', str(type(config)), '\\n')\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Config type: <class 'transformers.models.xlm_roberta.configuration_xlm_roberta.XLMRobertaConfig'> \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "b147bf10d5c74977bc747f359ea46dd4",
            "248fcbb6bb27458dadafae8ec57f45ac",
            "8d463d58f9e540f2a3c67d5df0683b28",
            "edfd2532c80349d0b3aa6d7cce3e8844",
            "6d165fb74f1e42eeb067a1b735dcf227",
            "76c128713cc64b28a5a653aa4bdf264d",
            "d15fa34472cf4fe8b5b10841241e9f23",
            "678edf39d1d445ac82b766f2f0dd90b2"
          ]
        },
        "id": "bWvc9Ikv4HtD",
        "outputId": "d97cc774-39a1-4562-fe18-70ca097a21e3"
      },
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name,config = config)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b147bf10d5c74977bc747f359ea46dd4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1115590446.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']\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 xlm-roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xouf5M2B55ei",
        "outputId": "3178ee0c-ebe8-4b63-ad12-8e9aed8928c1"
      },
      "source": [
        "model.cuda()"
      ],
      "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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXJHBVlxYR-l"
      },
      "source": [
        "## Load the model from the transformers library using AutoModel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "n8J4r6KyhveR",
        "outputId": "ca669f1d-ace2-443b-fea4-fbcaf39ea50c"
      },
      "source": [
        "# # Load the pre-trained model for classification, passing in the `config` from\r\n",
        "# # above.\r\n",
        "# bert = AutoModel.from_pretrained(\r\n",
        "#     pretrained_model_name_or_path=model_name,\r\n",
        "#     config=config)\r\n",
        "\r\n",
        "# print('\\nModel type:', str(type(bert)))\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-53-03249bee5589>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m bert = AutoModel.from_pretrained(\n\u001b[1;32m      4\u001b[0m     \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     config=config)\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\nModel type:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/auto/modeling_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    692\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mMODEL_MAPPING\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m             return MODEL_MAPPING[type(config)].from_pretrained(\n\u001b[0;32m--> 694\u001b[0;31m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    695\u001b[0m             )\n\u001b[1;32m    696\u001b[0m         raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   1029\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m         \u001b[0;31m# Instantiate model.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1031\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1032\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1033\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstate_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfrom_tf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/roberta/modeling_roberta.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, config, add_pooling_layer)\u001b[0m\n\u001b[1;32m    613\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpooler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRobertaPooler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0madd_pooling_layer\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 615\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_input_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36minit_weights\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    745\u001b[0m         \"\"\"\n\u001b[1;32m    746\u001b[0m         \u001b[0;31m# Initialize weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 747\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m         \u001b[0;31m# Prune heads if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \"\"\"\n\u001b[1;32m    444\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \"\"\"\n\u001b[1;32m    444\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    444\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/roberta/modeling_roberta.py\u001b[0m in \u001b[0;36m_init_weights\u001b[0;34m(self, module)\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0;31m# Slightly different from the TF version which uses truncated_normal for initialization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m             \u001b[0;31m# cf https://github.com/pytorch/pytorch/pull/5617\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializer_range\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLayerNorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcFMgf5EuPAF"
      },
      "source": [
        "# # freeze all the parameters\r\n",
        "# for param in bert.parameters():\r\n",
        "#     param.requires_grad = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NS_-0rBDYeY5"
      },
      "source": [
        "## Creating a custom BERT model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmGtZwykJ39i"
      },
      "source": [
        "\r\n",
        "# class BERT_Model(nn.Module):\r\n",
        "#     ## defining the constructor for the class\r\n",
        "#     def __init__(self, bert,num_labels):\r\n",
        "#       ## calling the super class constructor\r\n",
        "#       super(BERT_Model, self).__init__()\r\n",
        "#       ## adding the bert layer to the model\r\n",
        "#       self.bert = bert        \r\n",
        "#       # relu activation function\r\n",
        "#       self.relu =  nn.ReLU()\r\n",
        "#       # adding a dense layer to our custom model\r\n",
        "#       self.fc1 = nn.Linear(768,512)\r\n",
        "#       # adding another dense layer to our custom model ,i.e., the Output layer\r\n",
        "#       self.fc2 = nn.Linear(512,num_labels)\r\n",
        "#       # adding a softmax  activation function for our custom model's output layer\r\n",
        "#       self.softmax = nn.LogSoftmax(dim=1)\r\n",
        "\r\n",
        "#     #defining the forward pass\r\n",
        "#     def forward(self, input_id, mask):\r\n",
        "#       #pass the inputs to the model  \r\n",
        "#       outputs = self.bert(input_id,mask)\r\n",
        "  \r\n",
        "#       last_hidden_state = outputs.last_hidden_state       ## last hidden state from the model\r\n",
        "#       pooler_output = outputs.pooler_output               ## pooler output from the model\r\n",
        "#       ## adding a fully connected layer to the BERT model\r\n",
        "#       x = self.fc1(pooler_output)\r\n",
        "#       ## applying relu activation function\r\n",
        "#       x = self.relu(x)\r\n",
        "#       # the final output layer\r\n",
        "#       x = self.fc2(x)\r\n",
        "#       # apply softmax activation to our output layer\r\n",
        "#       x = self.softmax(x)\r\n",
        "\r\n",
        "#       return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvOs0G5sh02S",
        "outputId": "8e30ece2-fbdf-45e3-b46c-070660ff925c"
      },
      "source": [
        "\r\n",
        "\r\n",
        "# print('\\nLoading model ...')\r\n",
        "# # pass the pre-trained BERT to our define architecture\r\n",
        "# model = BERT_Model(bert,num_labels=3)\r\n",
        "\r\n",
        "# model.cuda()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loading model ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BERT_Model(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(105879, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (relu): ReLU()\n",
              "  (fc1): Linear(in_features=768, out_features=512, bias=True)\n",
              "  (fc2): Linear(in_features=512, out_features=3, bias=True)\n",
              "  (softmax): LogSoftmax(dim=1)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjQSUqCWJTbI"
      },
      "source": [
        "## Custom Loss function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOHVl1RRJSUB"
      },
      "source": [
        "# # convert class weights to tensor\r\n",
        "# weights= torch.tensor(class_wts,dtype=torch.float)\r\n",
        "# weights = weights.to(device)\r\n",
        "\r\n",
        "# # loss function\r\n",
        "# cross_entropy  = nn.NLLLoss(weight=weights) \r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evYV0yrQ2u_b"
      },
      "source": [
        "## Loading Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tAijXBEh7t8"
      },
      "source": [
        "# Note: AdamW is a class from the huggingface library (as opposed to pytorch) \r\n",
        "# I believe the 'W' stands for 'Weight Decay fix\"\r\n",
        "optimizer = AdamW(model.parameters(),\r\n",
        "                  lr = 2e-5, # This is the value Michael used.\r\n",
        "                  eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\r\n",
        "                )\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9n3_5jI2ywo"
      },
      "source": [
        "## Loading lr scheduler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agidP6D6iBFI"
      },
      "source": [
        "# Number of training epochs. I chose to train for 1 simply because the training\r\n",
        "# time is long. More epochs may improve the model's accuracy.\r\n",
        "epochs = 4\r\n",
        "\r\n",
        "# Total number of training steps is [number of batches] x [number of epochs]. \r\n",
        "# Note that it's the number of *batches*, not *samples*!\r\n",
        "total_steps = len(py_inputs) * epochs\r\n",
        "\r\n",
        "# Create the learning rate scheduler.\r\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, \r\n",
        "                                            num_warmup_steps = 0, # Default value in run_glue.py\r\n",
        "                                            num_training_steps = total_steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDDp05Cm245e"
      },
      "source": [
        "## Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4ERR8fBiEJn",
        "outputId": "3f80cf22-d51d-455f-f5d4-337faa3e1402"
      },
      "source": [
        "# We'll store a number of quantities such as training and validation loss, \r\n",
        "# validation accuracy, and timings.\r\n",
        "training_stats = {\r\n",
        "    'epoch':[],\r\n",
        "    'train_loss':[],\r\n",
        "    'Training Time':[],\r\n",
        "    'val_loss':[],\r\n",
        "    'Validation Time':[],\r\n",
        "    'train_acc':[],\r\n",
        "    'val_acc':[]\r\n",
        "}\r\n",
        "\r\n",
        "# Update every `update_interval` batches.\r\n",
        "update_interval = good_update_interval(total_iters=len(py_inputs), num_desired_updates=10)\r\n",
        "\r\n",
        "# Measure the total training time for the whole run.\r\n",
        "total_t0 = time.time()\r\n",
        "best_accuracy = 0\r\n",
        "# For each epoch...\r\n",
        "for epoch_i in range(0, epochs):\r\n",
        "\r\n",
        "    predictions = []\r\n",
        "    true_labels = []\r\n",
        "    \r\n",
        "    # ========================================\r\n",
        "    #               Training\r\n",
        "    # ========================================\r\n",
        "    \r\n",
        "    # Perform one full pass over the training set.\r\n",
        "\r\n",
        "    print(\"\")\r\n",
        "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\r\n",
        "    \r\n",
        "    # At the start of each epoch (except for the first) we need to re-randomize\r\n",
        "    # our training data.\r\n",
        "    if epoch_i > 0:\r\n",
        "        # Use our `make_smart_batches` function (from 6.1.) to re-shuffle the \r\n",
        "        # dataset into new batches.\r\n",
        "        (py_inputs, py_attn_masks, py_labels) = make_smart_batches(train_sentences, train_labels, batch_size,tokenizer,max_input_length)\r\n",
        "    \r\n",
        "    print('Training on {:,} batches...'.format(len(py_inputs)))\r\n",
        "\r\n",
        "    # Measure how long the training epoch takes.\r\n",
        "    t0 = time.time()\r\n",
        "\r\n",
        "    # Reset the total loss for this epoch.\r\n",
        "    total_train_loss = 0\r\n",
        "\r\n",
        "    # Put the model into training mode. Don't be mislead--the call to \r\n",
        "    # `train` just changes the *mode*, it doesn't *perform* the training.\r\n",
        "    # `dropout` and `batchnorm` layers behave differently during training\r\n",
        "    # vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)\r\n",
        "    model.train()\r\n",
        "\r\n",
        "    # For each batch of training data...\r\n",
        "    for step in range(0, len(py_inputs)):\r\n",
        "\r\n",
        "        # Progress update every, e.g., 100 batches.\r\n",
        "        if step % update_interval == 0 and not step == 0:\r\n",
        "            # Calculate elapsed time in minutes.\r\n",
        "            elapsed = format_time(time.time() - t0)\r\n",
        "            \r\n",
        "            # Calculate the time remaining based on our progress.\r\n",
        "            steps_per_sec = (time.time() - t0) / step\r\n",
        "            remaining_sec = steps_per_sec * (len(py_inputs) - step)\r\n",
        "            remaining = format_time(remaining_sec)\r\n",
        "\r\n",
        "            # Report progress.\r\n",
        "            print('  Batch {:>7,}  of  {:>7,}.    Elapsed: {:}.  Remaining: {:}'.format(step, len(py_inputs), elapsed, remaining))\r\n",
        "\r\n",
        "        # Copy the current training batch to the GPU using the `to` method.\r\n",
        "        b_input_ids = py_inputs[step].to(device)\r\n",
        "        b_input_mask = py_attn_masks[step].to(device)\r\n",
        "        b_labels = py_labels[step].to(device)\r\n",
        "\r\n",
        "        # Always clear any previously calculated gradients before performing a\r\n",
        "        # backward pass.\r\n",
        "        model.zero_grad()        \r\n",
        "\r\n",
        "        # Perform a forward pass (evaluate the model on this training batch).\r\n",
        "        # The call returns the loss (because we provided labels) and the \r\n",
        "        # \"logits\"--the model outputs prior to activation.\r\n",
        "        output = model(b_input_ids, \r\n",
        "                             token_type_ids=None, \r\n",
        "                             attention_mask=b_input_mask, \r\n",
        "                             labels=b_labels)\r\n",
        "\r\n",
        "        # Accumulate the training loss over all of the batches so that we can\r\n",
        "        # calculate the average loss at the end. `loss` is a Tensor containing a\r\n",
        "        # single value; the `.item()` function just returns the Python value \r\n",
        "        # from the tensor.\r\n",
        "        loss = output.loss \r\n",
        "        logits = output.logits\r\n",
        "\r\n",
        "        # Move logits and labels to CPU\r\n",
        "        logits = logits.detach().cpu().numpy()\r\n",
        "        label_ids = b_labels.to('cpu').numpy()\r\n",
        "      \r\n",
        "        # Store predictions and true labels\r\n",
        "        predictions.append(logits)\r\n",
        "        true_labels.append(label_ids)\r\n",
        "        \r\n",
        "        # Accumulate the training loss over all of the batches so that we can\r\n",
        "        # calculate the average loss at the end. `loss` is a Tensor containing a\r\n",
        "        # single value; the `.item()` function just returns the Python value \r\n",
        "        # from the tensor.\r\n",
        "        total_train_loss += loss.item()\r\n",
        "\r\n",
        "        # Perform a backward pass to calculate the gradients.\r\n",
        "        loss.backward()\r\n",
        "\r\n",
        "        # Clip the norm of the gradients to 1.0.\r\n",
        "        # This is to help prevent the \"exploding gradients\" problem.\r\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\r\n",
        "\r\n",
        "        # Update parameters and take a step using the computed gradient.\r\n",
        "        # The optimizer dictates the \"update rule\"--how the parameters are\r\n",
        "        # modified based on their gradients, the learning rate, etc.\r\n",
        "        optimizer.step()\r\n",
        "\r\n",
        "        # Update the learning rate.\r\n",
        "        scheduler.step()\r\n",
        "\r\n",
        "    # Calculate the average loss over all of the batches.\r\n",
        "    avg_train_loss = total_train_loss / len(py_inputs)     \r\n",
        "       \r\n",
        "    training_accuracy = check_accuracy(predictions,true_labels)       \r\n",
        "    \r\n",
        "    # Measure how long this epoch took.\r\n",
        "    training_time = format_time(time.time() - t0)\r\n",
        "\r\n",
        "    print(\"\")\r\n",
        "    print(\"  Average training loss: {0:.2f}\".format(avg_train_loss))\r\n",
        "    print(\"  Training Accuracy: {0:.2f}\".format(training_accuracy))\r\n",
        "    print(\"  Training epoch took: {:}\".format(training_time))\r\n",
        "        \r\n",
        "    (py_inputs, py_attn_masks, py_labels) = make_smart_batches(val_sentences, val_labels, batch_size ,tokenizer,max_input_length)\r\n",
        "    val_loss,val_accuracy,validation_time = eval_model(model,py_inputs, py_attn_masks, py_labels)  \r\n",
        "\r\n",
        "    if val_accuracy > best_accuracy:\r\n",
        "        torch.save(model.state_dict(), 'best_model_state.bin')\r\n",
        "        best_accuracy = val_accuracy\r\n",
        "    # Record all statistics from this epoch.\r\n",
        "\r\n",
        "    print(\"\")\r\n",
        "    print(\"  Average validation loss: {0:.2f}\".format(val_loss))\r\n",
        "    print(\"  Validation Accuracy: {0:.2f}\".format(val_accuracy))\r\n",
        "    print(\"  Validation epoch took: {:}\".format(validation_time))\r\n",
        "    \r\n",
        "    \r\n",
        "    training_stats['epoch'].append(epoch_i + 1)\r\n",
        "    training_stats['train_loss'].append(avg_train_loss)\r\n",
        "    training_stats['Training Time'].append(training_time)\r\n",
        "    training_stats['val_loss'].append(val_loss)\r\n",
        "    training_stats['Validation Time'].append(validation_time)\r\n",
        "    training_stats['train_acc'].append(training_accuracy)\r\n",
        "    training_stats['val_acc'].append(val_accuracy)\r\n",
        "\r\n",
        "print(f'Best val accuracy: {best_accuracy}')\r\n",
        "  \r\n",
        "model.load_state_dict(torch.load('best_model_state.bin'))\r\n",
        "\r\n",
        "print(\"\")\r\n",
        "print(\"Training complete!\")\r\n",
        "\r\n",
        "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "======== Epoch 1 / 4 ========\n",
            "Training on 1,423 batches...\n",
            "  Batch     100  of    1,423.    Elapsed: 0:00:18.  Remaining: 0:03:59\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:37.  Remaining: 0:03:45\n",
            "  Batch     300  of    1,423.    Elapsed: 0:00:55.  Remaining: 0:03:28\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:13.  Remaining: 0:03:06\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:31.  Remaining: 0:02:48\n",
            "  Batch     600  of    1,423.    Elapsed: 0:01:49.  Remaining: 0:02:30\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:08.  Remaining: 0:02:12\n",
            "  Batch     800  of    1,423.    Elapsed: 0:02:26.  Remaining: 0:01:54\n",
            "  Batch     900  of    1,423.    Elapsed: 0:02:45.  Remaining: 0:01:36\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:03.  Remaining: 0:01:17\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:03:20.  Remaining: 0:00:59\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:03:39.  Remaining: 0:00:41\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:03:57.  Remaining: 0:00:22\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:04:16.  Remaining: 0:00:04\n",
            "\n",
            "  Average training loss: 0.26\n",
            "  Training Accuracy: 0.91\n",
            "  Training epoch took: 0:04:23\n",
            "Creating Smart Batches from 2,843 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,843 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,843 samples\n",
            "\n",
            "     2,843 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Doing validation on 178 sentences...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      60  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.21\n",
            "  Validation Accuracy: 0.92\n",
            "  Validation epoch took: 0:00:05\n",
            "\n",
            "======== Epoch 2 / 4 ========\n",
            "Creating Smart Batches from 22,762 examples with batch size 16...\n",
            "\n",
            "Tokenizing 22,762 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 2,000 samples.\n",
            "  Tokenized 4,000 samples.\n",
            "  Tokenized 6,000 samples.\n",
            "  Tokenized 8,000 samples.\n",
            "  Tokenized 10,000 samples.\n",
            "  Tokenized 12,000 samples.\n",
            "  Tokenized 14,000 samples.\n",
            "  Tokenized 16,000 samples.\n",
            "  Tokenized 18,000 samples.\n",
            "  Tokenized 20,000 samples.\n",
            "  Tokenized 22,000 samples.\n",
            "DONE.\n",
            "    22,762 samples\n",
            "\n",
            "    22,762 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 1,423 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Training on 1,423 batches...\n",
            "  Batch     100  of    1,423.    Elapsed: 0:00:19.  Remaining: 0:04:10\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:37.  Remaining: 0:03:49\n",
            "  Batch     300  of    1,423.    Elapsed: 0:00:56.  Remaining: 0:03:28\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:14.  Remaining: 0:03:10\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:33.  Remaining: 0:02:51\n",
            "  Batch     600  of    1,423.    Elapsed: 0:01:51.  Remaining: 0:02:33\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:09.  Remaining: 0:02:13\n",
            "  Batch     800  of    1,423.    Elapsed: 0:02:28.  Remaining: 0:01:55\n",
            "  Batch     900  of    1,423.    Elapsed: 0:02:47.  Remaining: 0:01:37\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:06.  Remaining: 0:01:19\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:03:24.  Remaining: 0:01:00\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:03:44.  Remaining: 0:00:42\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:04:02.  Remaining: 0:00:23\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:04:21.  Remaining: 0:00:04\n",
            "\n",
            "  Average training loss: 0.20\n",
            "  Training Accuracy: 0.93\n",
            "  Training epoch took: 0:04:27\n",
            "Creating Smart Batches from 2,843 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,843 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,843 samples\n",
            "\n",
            "     2,843 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Doing validation on 178 sentences...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      60  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.23\n",
            "  Validation Accuracy: 0.93\n",
            "  Validation epoch took: 0:00:05\n",
            "\n",
            "======== Epoch 3 / 4 ========\n",
            "Creating Smart Batches from 22,762 examples with batch size 16...\n",
            "\n",
            "Tokenizing 22,762 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 2,000 samples.\n",
            "  Tokenized 4,000 samples.\n",
            "  Tokenized 6,000 samples.\n",
            "  Tokenized 8,000 samples.\n",
            "  Tokenized 10,000 samples.\n",
            "  Tokenized 12,000 samples.\n",
            "  Tokenized 14,000 samples.\n",
            "  Tokenized 16,000 samples.\n",
            "  Tokenized 18,000 samples.\n",
            "  Tokenized 20,000 samples.\n",
            "  Tokenized 22,000 samples.\n",
            "DONE.\n",
            "    22,762 samples\n",
            "\n",
            "    22,762 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 1,423 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Training on 1,423 batches...\n",
            "  Batch     100  of    1,423.    Elapsed: 0:00:18.  Remaining: 0:04:04\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:38.  Remaining: 0:03:50\n",
            "  Batch     300  of    1,423.    Elapsed: 0:00:56.  Remaining: 0:03:30\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:15.  Remaining: 0:03:12\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:34.  Remaining: 0:02:53\n",
            "  Batch     600  of    1,423.    Elapsed: 0:01:52.  Remaining: 0:02:34\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:10.  Remaining: 0:02:14\n",
            "  Batch     800  of    1,423.    Elapsed: 0:02:28.  Remaining: 0:01:55\n",
            "  Batch     900  of    1,423.    Elapsed: 0:02:47.  Remaining: 0:01:37\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:06.  Remaining: 0:01:19\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:03:24.  Remaining: 0:01:00\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:03:42.  Remaining: 0:00:41\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:04:01.  Remaining: 0:00:23\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:04:20.  Remaining: 0:00:04\n",
            "\n",
            "  Average training loss: 0.17\n",
            "  Training Accuracy: 0.95\n",
            "  Training epoch took: 0:04:27\n",
            "Creating Smart Batches from 2,843 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,843 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,843 samples\n",
            "\n",
            "     2,843 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Doing validation on 178 sentences...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:00.  Remaining: 0:00:03\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:03\n",
            "  Batch      60  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.25\n",
            "  Validation Accuracy: 0.93\n",
            "  Validation epoch took: 0:00:05\n",
            "\n",
            "======== Epoch 4 / 4 ========\n",
            "Creating Smart Batches from 22,762 examples with batch size 16...\n",
            "\n",
            "Tokenizing 22,762 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 2,000 samples.\n",
            "  Tokenized 4,000 samples.\n",
            "  Tokenized 6,000 samples.\n",
            "  Tokenized 8,000 samples.\n",
            "  Tokenized 10,000 samples.\n",
            "  Tokenized 12,000 samples.\n",
            "  Tokenized 14,000 samples.\n",
            "  Tokenized 16,000 samples.\n",
            "  Tokenized 18,000 samples.\n",
            "  Tokenized 20,000 samples.\n",
            "  Tokenized 22,000 samples.\n",
            "DONE.\n",
            "    22,762 samples\n",
            "\n",
            "    22,762 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 1,423 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Training on 1,423 batches...\n",
            "  Batch     100  of    1,423.    Elapsed: 0:00:20.  Remaining: 0:04:20\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:38.  Remaining: 0:03:53\n",
            "  Batch     300  of    1,423.    Elapsed: 0:00:56.  Remaining: 0:03:31\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:14.  Remaining: 0:03:10\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:33.  Remaining: 0:02:51\n",
            "  Batch     600  of    1,423.    Elapsed: 0:01:51.  Remaining: 0:02:32\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:10.  Remaining: 0:02:14\n",
            "  Batch     800  of    1,423.    Elapsed: 0:02:28.  Remaining: 0:01:56\n",
            "  Batch     900  of    1,423.    Elapsed: 0:02:47.  Remaining: 0:01:37\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:05.  Remaining: 0:01:18\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:03:23.  Remaining: 0:01:00\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:03:41.  Remaining: 0:00:41\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:04:00.  Remaining: 0:00:23\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:04:19.  Remaining: 0:00:04\n",
            "\n",
            "  Average training loss: 0.14\n",
            "  Training Accuracy: 0.96\n",
            "  Training epoch took: 0:04:25\n",
            "Creating Smart Batches from 2,843 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,843 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,843 samples\n",
            "\n",
            "     2,843 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Doing validation on 178 sentences...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:05\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      60  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.32\n",
            "  Validation Accuracy: 0.92\n",
            "  Validation epoch took: 0:00:05\n",
            "Best val accuracy: 0.9328174463594794\n",
            "\n",
            "Training complete!\n",
            "Total training took 0:18:26 (h:mm:ss)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "GOmua_S1eXNM",
        "outputId": "3145d5b1-b61d-48eb-d058-5af195b53484"
      },
      "source": [
        "plot_training_history(training_stats)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3c-6bOIxVyrM"
      },
      "source": [
        "## Evaluating Performance Over Training Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QmKPNAeWHkZ",
        "outputId": "cac50df5-0afa-41e5-d3cf-0b95fec49a23"
      },
      "source": [
        "(py_inputs, py_attn_masks, py_labels) = make_smart_batches(train_sentences, train_labels, batch_size ,tokenizer,max_input_length)\r\n",
        "\r\n",
        "y_pred , y_true = get_predictions(py_inputs, py_attn_masks, py_labels)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating Smart Batches from 22,762 examples with batch size 16...\n",
            "\n",
            "Tokenizing 22,762 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 2,000 samples.\n",
            "  Tokenized 4,000 samples.\n",
            "  Tokenized 6,000 samples.\n",
            "  Tokenized 8,000 samples.\n",
            "  Tokenized 10,000 samples.\n",
            "  Tokenized 12,000 samples.\n",
            "  Tokenized 14,000 samples.\n",
            "  Tokenized 16,000 samples.\n",
            "  Tokenized 18,000 samples.\n",
            "  Tokenized 20,000 samples.\n",
            "  Tokenized 22,000 samples.\n",
            "DONE.\n",
            "    22,762 samples\n",
            "\n",
            "    22,762 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 1,423 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Predicting labels for 1,423 test batches...\n",
            "  Batch     100  of    1,423.    Elapsed: 0:00:03.  Remaining: 0:00:34\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:05.  Remaining: 0:00:32\n",
            "  Batch     300  of    1,423.    Elapsed: 0:00:08.  Remaining: 0:00:29\n",
            "  Batch     400  of    1,423.    Elapsed: 0:00:11.  Remaining: 0:00:27\n",
            "  Batch     500  of    1,423.    Elapsed: 0:00:13.  Remaining: 0:00:24\n",
            "  Batch     600  of    1,423.    Elapsed: 0:00:15.  Remaining: 0:00:21\n",
            "  Batch     700  of    1,423.    Elapsed: 0:00:18.  Remaining: 0:00:19\n",
            "  Batch     800  of    1,423.    Elapsed: 0:00:20.  Remaining: 0:00:16\n",
            "  Batch     900  of    1,423.    Elapsed: 0:00:23.  Remaining: 0:00:13\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:00:26.  Remaining: 0:00:11\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:00:28.  Remaining: 0:00:08\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:00:31.  Remaining: 0:00:06\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:00:33.  Remaining: 0:00:03\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:00:36.  Remaining: 0:00:01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXsVn0wngZhv",
        "outputId": "54b51bce-712a-4fa0-c70f-97d14fbb84e9"
      },
      "source": [
        "print(classification_report(y_true, y_pred, target_names=class_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Hope_speech       0.84      0.78      0.81      1962\n",
            "Non_hope_speech       0.98      0.99      0.98     20778\n",
            "    not-English       0.00      0.00      0.00        22\n",
            "\n",
            "       accuracy                           0.97     22762\n",
            "      macro avg       0.61      0.59      0.60     22762\n",
            "   weighted avg       0.97      0.97      0.97     22762\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "guClUdRhYPql",
        "outputId": "06823a10-39ea-4809-f792-d970ba5b2893"
      },
      "source": [
        "cm = confusion_matrix(y_true, y_pred)\r\n",
        "show_confusion_matrix(cm, class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VM2N-X1tV3il"
      },
      "source": [
        "## Evaluating Performance Over Validation Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnadNvtxWIYw",
        "outputId": "7f9e26cd-82f1-4db2-f568-8e00ddb19aa4"
      },
      "source": [
        "(py_inputs, py_attn_masks, py_labels) = make_smart_batches(val_sentences, val_labels, batch_size ,tokenizer,max_input_length)\r\n",
        "\r\n",
        "y_pred , y_true = get_predictions(py_inputs, py_attn_masks, py_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating Smart Batches from 2,843 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,843 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,843 samples\n",
            "\n",
            "     2,843 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Predicting labels for 178 test batches...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:05\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      60  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAjlATBPgh7c",
        "outputId": "d8de735d-1bad-40f6-c0d7-4433a0db698e"
      },
      "source": [
        "print(classification_report(y_true, y_pred, target_names=class_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Hope_speech       0.68      0.57      0.62       272\n",
            "Non_hope_speech       0.95      0.97      0.96      2569\n",
            "    not-English       0.00      0.00      0.00         2\n",
            "\n",
            "       accuracy                           0.93      2843\n",
            "      macro avg       0.55      0.52      0.53      2843\n",
            "   weighted avg       0.93      0.93      0.93      2843\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "7nELY57NYshD",
        "outputId": "0596c2e6-8af1-4a37-9f36-cf9f19fe9b38"
      },
      "source": [
        "cm = confusion_matrix(y_true, y_pred)\r\n",
        "show_confusion_matrix(cm, class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_Ev8kTSNT6L"
      },
      "source": [
        "## Evaluating Performance Over Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knzcVqvONT6N",
        "outputId": "a2c3712d-fbd4-4a60-91f3-76d2f3b62744"
      },
      "source": [
        "(py_inputs, py_attn_masks, py_labels) = make_smart_batches(test_sentences, test_labels, batch_size ,tokenizer,max_input_length)\r\n",
        "\r\n",
        "y_pred , y_true = get_predictions(py_inputs, py_attn_masks, py_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating Smart Batches from 2,846 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,846 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,846 samples\n",
            "\n",
            "     2,846 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n",
            "Predicting labels for 178 test batches...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      60  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6EJfj0TNT6R",
        "outputId": "fd1915fc-8ee3-4097-c7fa-40916fef8247"
      },
      "source": [
        "print(classification_report(y_true, y_pred, target_names=class_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Hope_speech       0.65      0.53      0.59       250\n",
            "Non_hope_speech       0.95      0.97      0.96      2593\n",
            "    not-English       0.00      0.00      0.00         3\n",
            "\n",
            "       accuracy                           0.93      2846\n",
            "      macro avg       0.54      0.50      0.52      2846\n",
            "   weighted avg       0.93      0.93      0.93      2846\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "ZE3EG9RTNT6S",
        "outputId": "3bf8a4c6-b02d-4826-9a97-54672f7d2116"
      },
      "source": [
        "cm = confusion_matrix(y_true, y_pred)\r\n",
        "show_confusion_matrix(cm, class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZMtja2mL79u3Q0CjfznbChAnw9PTEqlWr0KNHD4XGf+rUKbRt2xZr1qypsa6dnZ20nre3NzQ1NbFt2zZMnToVLVu2RFJSEtavX4/58+djypQp0nZ9+vTB+PHjsXv3bkyYMEGajfL29sYXX3whrTdkyBB4eHggKCgI8+bNQ2lpKf7zn/+gefPm2L17N5o1ayat++LeEk+ePMHKlSvh4eEBABg9ejTc3Nywc+dO9O3bV6H3goiIiOh1pUwZlLpiBqYGX331FbZs2SL3sLGxkdaJiYmBhoYGJk2aJC0TCATw9fWFWCyWy5i4u7tLgxcA6NKlC+zt7RETEwOgPNtw/vx5DB48GDk5OdKsT3Z2Nnr16oVr166hoKAAitDX18fjx49x5cqVGutOnDhR5rm3tzckEgnOnDkDADh27BgkEgnc3d1lslE2NjYwMzPDhQvl5yf88ccfyMnJwZAhQ2Tq6erqwtHRUVrv+vXrSE5Ohp+fn0zwUvH+Pa9Zs2YYNmyY9LlIJEL79u3x8OFDhd4HIiIioteZKk0hYwamBh06dKh0G2UdHR3pn5OTk2FhYSFTBgCtW7eWXn+era2tXH+2tra4fPkyACAxMRESiQQrV67EypUrKx1XVlYWtLW1axz/xIkTcfDgQYwdOxbW1tbo2bMnBg8ejLfffluurp2dXaXPK8b/4MEDlJWVYcCAAZW+VkZGhrQeUB4AVaZly5YAyjNRANC2bdsa78PS0lLuP5aBgQFu375dY1siIiKi150yBSB1xQDmFVSxvmb69OmVBhoAZKakVcfExAS//fYbzp49i5iYGMTExGD79u3w8vLCsmXLaj0uNTU1bNq0qdL/JPr6+gD+nf61cuXKSsepqVn7Myx48FbjEABwtzfBO28Yw1RXhJyiElxIfIbd11IgLq35yKgt49tXWl5YXAr/nTdkygY5mKKjVTM0b6YJXZEa8sSleJxdhGN30vF3cnZ93A6pKElZGe7F7MX9c4eQn5EKTT0DWHfshTcHe0NdU6tWfZWIC3F0eQDyM1LQutcwuHp9IHN955zqTzVvN3QSHN3H1foeiIDy37thIcGIityGR8nJMDI2xsBBQzAzYLbcl6b10f73mNPY9MsG3L59CyINEbp1746P586HtXXLhrg9qm+qE78wgKkPVlZWOH/+PPLz82V+IFTsLGZlZSVTv7KduBISEtCiRQsA/2YoNDU1qwxgakMkEqFfv37o168fJBIJvv76a4SFheGDDz6QmQr34MEDWFpayjwHIB2XjY0NSktLYWtrC2tr6ypfr2L8ZmZm6NatW4317t69W209ajwTXC3h7mCKSw+f4dCtp2ihr4UB9qawNdLG9yfjocipt7dT83A6LkOmrLRMvmVrE208zSvG1Uc5yC0qha5IDZ1tDDCrty12XUvBvuup9XRXpGqu/LYZcb/vQ4v2PWD/jieyUx7iXsw+ZCXdR2//ryGoxRciNw6GQZxXdUDdxfuTytsdjkDe08ewbNe11uMnqvD98mUIDw2B2wB3+E6egvv34xARFoJbN29gY2BQjV/u1ab9saNHMG/ObNg7OOKTufORm5uL0JCt8Js0AeE7dsLc3KKhb5dIYfxaux706dMHxcXFMtsmSyQShISEQCQSyS24P3r0KFJT//1wdvHiRdy5cwd9+vQBUJ416dKlCyIiIqTTsp5XWVlVMjMzZZ4LBAI4ODgAAIqKimSuvbjtc1hYGAQCAXr37g2gfO2OUCjEunXr5F6nrKwMWVlZAIBevXpBT08Pv/zyC0pKSqoc/5tvvgkrKysEBQUhJydHps6Li/ip4bXQ10R/exP89fAZ1p5NRMz9TGz75zG2XX4MJws9dLM1UKiftDwxziVkyTwuPHwmV2/DHw8ReD4JB289xe/xmTh0+ymWHYtDYmYBhjiaQoUy4VSPsh8nIO7MfrRw6YEeUz5Fqx6D0GHkNLiMmIq0e1fx8HKMwn1lPryHezF74TR4YpV1bDr3k3uYvtEeeekpMGr5BgxatKqP2yIVdO/eXUSEhaL/gIFY9eNaeI0Zi/kLF2PugkW4eOE8Dh2Irrf2xcXF+HbZ12je3BJBwWEYN8EbU6e/jw2/BCI9PR0/r1vb0LdL9aCx18CIxWJ8//336NWrl/S4D0V3yv3jjz/g4+ODbt26oUuXLhg3bhwOHDig8GszA1MP3Nzc0K1bN6xcuRJJSUlo27YtTp06hZiYGHz00Udy06isra3h7e2N8ePHo6CgAEFBQTAzM4OPj4+0zpIlS+Dt7Q0PDw+MGTMG1tbWSE1NxaVLl1BUVISwsDCFxvb555/j2bNn6N69OywsLPD48WOEhobCyckJbdq0kan74MEDfPjhh3j77bdx6dIlREdHY9y4cdJMia2tLWbPno3Vq1fj4cOH6NevH7S1tfHw4UMcPnwY/v7+GDNmDJo1a4YvvvgCixYtgpeXF4YOHQpDQ0MkJyfjxIkT6N+/P+bMmQM1NTV8+eWXmDlzJkaOHAlPT0+Ympri3r17ePLkCdau5Q/MxtTd1hBCgQBH7sie6XM6LgNjOjRHD1sj/JkgH4hURk0ogLpQgKKSsporP6dMAmQVlMDaUAtqAgFKGMhSLT28HANIJGjbZ4RMeasegxAbvRWJl07BptM7NfYjKSvF3zvWwsLxLVi59MC1PYEKjyHhwjFAUga77gNrO3wiqUPR+yGRSDDJd7JMudfosfhx1UpE79+LoR5VT2GsTftLf11EWmpq+dQyXV1pXUcnJ3Tu0hWHDx3A4s+XSHdFpVdTY6+BWbRoEY4cOQJfX1/Y2tpi9+7dmD59OkJCQuDq6lplu5MnT8Lf3x+urq6YNWsWgPIzC+fMmYO8vDyMGTOmxtdmAFMPhEIhNmzYgNWrV+PgwYOIiopCy5YtpUHIi7y8vFBaWorg4GBkZmbC1dUVS5YsgaGhobSOvb09oqKisGbNGkRGRiI7OxumpqZo165dtVs7v+jdd9/Fjh07EB4ejuzsbJiZmWHw4MGYNWuWXOr5p59+wg8//IAVK1ZAU1MTU6ZMwSefyE6P8Pf3h62tLYKDg7FmzRoIBAK0aNECAwYMkJnuNnLkSFhYWGDjxo3YuHEjSkpK0Lx5c3Tt2lVmN7G+ffsiKCgIa9euRWBg+QcEOzu7KjcAoIbTylgbZWUSxKfL7nBXUiZBYmYBWhnXvGkEAHS21kcPW0OoCQXILizBhcQs7LqWgoLiyoMZXZEahAJAT1MdXVoawNlSD7dS81BSybQzoppkJN4FBEIY2drLlKtpiGDYojUyE+8q1M/d03uQk5qE7u8trtXrSyQSPDh/DGoiLbR8i1u808uLjY2FUCiEc3sXmXJNTU04Ojjieuy1emtf8ecOHeU/dLp06IgL5/9EQsIDvPFGzZvuUNNpzADm6tWriI6OxuLFi+Hn5weg/LOfh4cHVqxYUe0X7WFhYTAzM8PWrVshEokAAGPHjkX//v2xZ88eBjB1MWrUKIwaNarK63v27JF5rquri88++wyfffaZQv1PnToVU6dOrbaOra0tVqxYoVB/VRk0aBAGDRqkUF0TExOFzosZOnQohg4dWmO9Hj16KHReTZcuXbB169Yqr4eEhFRa/u2339bYNynOUFsDOeKSSgOHzIJitDXThZpQUOl6lgpx6fn4K/EZUnLF0NYQwsWyGQbYm8LBXBdLj92vNCPz32H2aKZZ/qOopEyCSw+zEXIpWa4ekSIKszOgqasPNXX5b4q1DIyR/uAmykqKIazkeoW89Ce4cSgcTgPHQ9fYAnkZKQq/ftrdq8jPSIFt1/7Q0Kp5kTVRVdLSUmFoZCT9gPc8cwsL/PPPZRSLxdCo5Hpt21dMa69snUvFsQ+pKSkMYF51jZiAOXToEDQ0NGSCDU1NTYwePRqrVq1CamqqzJEhz8vNzYWBgYHMv02RSAQDAwOFN3piAENEAACRugAlVew0Vvy/cpGaAAXVBDDfHI2Tef7Hgyw8fFaI0S7N4W5vgv030uTarD2TAA01IYy0NdClpQE01ATQUhcip6i0DndDqqpUXASheuW/2tQ0yn9ZlhQXQVRNAPN35HromjRH23dG1vr14/88AgCw6+Ze67ZEzyssLIBIo/LgRPS/D3kFhYVVBjC1aV9YWJ55ryzYqahbWFhYuxug19rNmzfRqlUr6D435RAAXFxcIJFIcPPmzSoDmK5du+KXX37B6tWrpcmCXbt24cGDB1i8WLGsNwMYJVVaWlrjYn4dHR25f1hEVRGXSNBMq/J9PTTUyr/WUWQr5RcdupmGEe3M0aFFs0oDmDtp+dI/n4nPxPs9WuLTAW3w2YE7yK9i2hlRVdREmijJqfyDVmmxGACgrlH1N3yJf51E6p1/0DfgvxCq1e5XpDgvB4+unUMzc2uYtm5Xq7ZEL9LS0kZGfnql18T/24RHW6vqbcFr015Lq3yKsFgsrrKuVjWvRa+G+phClp2djexs+Z0X9fX1pcdlAEBaWhosLOQzdmZmZgAgs1nViz744AMkJibi559/xoYNGwCUf2Zdv349evbsqdA4GcAoqcePH6N///7V1gkICJAujiKqSVZBMVroa0JdKJCbRmakrYGcwpJqp49VpfR/C/P1RIr9uDkbn4nutobo1NIAv9/PrLkB0XO09I2R/eQhSkuK5aaRFT7LgEhXv8rpY6Ulxbi6JxDNnTpBq5kRctMeAQAKnpV/CCwpyENu2iOI9PQh0taTa5946RTKSoph153ZF6o7MzNz3I+7B7FYLJcZSU1JgZGRUZXZl9q2l04TS01B6xc2+JFOL6vkwyq9WuojgNm6dWulmyi9+JmysLCw0k0dKqaAvbjT7fNEIhHs7OwwePBguLu7o7S0FDt27MDHH3+MoKAguLi4VNm2AgOYRmRtbV1vJ8ebmZlhy5Yt1dap2D2sJrNmzWKgQ4jPKICzZTO0MtHG3eeyIupCAWyMtHE7Ne+l+lUXCmCko4H76fk1VwYgUivPAumK1F7q9Ui1Gdu0Rerty8hMuAPTNv9mQUqLxch6dL/azEhpcRGKcp/hyY2/8OTGX3LXEy+dQuKlU2j/7nuw7ye/RvLB+aMQqKnDprNb/dwMqTRnZ2ec++MMYq9dxVudOkvLi4qKcOv2LXR6rqyu7ds5lx9CfOWfy+jeQ/b8uatX/oGenh5sbe3q4a6oIdVHADN58mR4enrKlT+ffQHKM3LFxcVy9SoCl+rWsnz99de4du0aoqKipBtKDRkyBB4eHli2bBm2bdtW4zgZwCip+jrkkqjChcQsDHvTDAPtTXE3LVFa3reNMTTVhfgzIUtaZqYngppAgCc5/37DoitSQ55Yft3KqPYWUBcK8E/yvylpkVr5fvMvLuoXCAC3tiYAyjcEIKot6469cetYJO7G7JEJYOLPHUapuEhmC+Xcp49RVloCfYvyL3vURVroNnmRXJ9Fec/wT9QGWDi+BbtuA2HQwk6uTmbiXTx7FI8WLj2g1cxQ7jpRbQ0aMhSbN/2C0OCtMgHIzqgdKCwokNlC+WFiIkpKitGqdZuXat+pcxeYmZlh984o+Pj6SbdSvn3rFv66eAEjPEdxC2UV8eJUsaqYmZlVOk0sLa18qnhV61/EYjGioqLw/vvvy+yGq6Ghgd69eyMiIgIlJSVQr2ItYwUGMEQEAEh6VoQTd9MxwN4UAT1tcPVxDiz1NTHA3hS3UnNlApgF/VrBVFeE97b9uw3n8HbmaGOig1upuUjPK4aWuhAuLZrByUIPcU/zcezuv3OxLZppYpFba/yV9AxPsouQJy6FkbYGutkawFJfC2fiM2WyQESKMmhhhzY9hyHuzH6c+3UZmr/ZCTkpSbgXsw+mbZxltjb+ff3nyM9MhdeqfQAAoZo6rDvKz7+u2IVM19Sy0usAEH/+KACgFc9+oXrS1t4B4yZ4Y1t4KOZ8FIDevfvi/v04RISFoHOXrhg67N8AZMZUPzx6lIwr12+/VHsNDQ0sWPwZFsydAz9fb3iNHoPc3DyEhgTByMgY/h/Obsxbp5fUmNsoOzo6IiQkBHl5eTLrra9cuSK9XpmsrCyUlJSgtFT+C8+SkhKUlJQodJg5Axgikgq//BhP84rRt40xXFo0Q25RKY7fScfu2BTU9OPkdmourHgYa+EAACAASURBVPQ10dPOCHqaaiiTACk5RYi6+gSHbz2VWVeTmV+Mcw+y0NZMB29Z6UNLQw0FxaVIzCzA3utpMsESUW118JwGHWNzxJ87jCc3LkKkp482vT3Qbog3BMLKN6qoi1JxEZL+joG2oSksHN6q9/5JdS1Y9ClaWFlhZ+R2/H76FAyNjDB+4iR8GDBb7iy3urYfOGgINDW1sOmXDfhhxXfQEInQrVsPfPzJvEoXa9MrqBG3UR48eDB+/fVXREZGSs+BEYvF2LVrF9566y3pv5lHjx6hoKBAeni6iYkJ9PX1cfToUQQEBEgze3l5eTh58iTs7e0VyvYJJIqEOUSvieczBkTKylJfsX3yiV51Swba11yJSAlovQIpASv/3XXuI3mD/PqXqnz00Uc4fvw4Jk+eDBsbG+zevRuxsbHYunUrOnXqBADw8fHBhQsXZNaAVxz+3q5dO7z77rsoKytDVFQU4uLisGrVKoXOGnwF3m4iIiIiIqqLxpxCBgDfffcdVq9ejT179uDZs2dwcHDAxo0bpcFLVfz9/WFtbY3g4GCsW7cOYrEYDg4OWLt2LdzdFdvFkRkYUinMwNDrgBkYel0wA0Ovi1chA2M987c695G0vvYH+DaFV+DtJiIiIiKiumjsDExTYgBDRERERKTsVCd+YQBDRERERKTsmIEhIiIiIiKloUoBTP1viE9ERERERNRAmIEhIiIiIlJyqpSBYQBDRERERKTkGMAQEREREZHyUJ34hWtgiIiIiIhIeTADQ0RERESk5DiFjIiIiIiIlAYDGCIiIiIiUhoqFL8wgCEiIiIiUnaqlIHhIn4iIiIiIlIazMAQERERESk5FUrAMIAhIiIiIlJ2qjSFjAEMEREREZGSU6H4hQEMEREREZGyEwpVJ4LhIn4iIiIiIlIazMAQERERESk5TiEjIiIiIiKlwUX8RERERESkNFQofuEaGCIiIiIiUh7MwBARERERKTlOISMiIiIiIqXBAIaIiIiIiJSGCsUvDGCIiIiIiJSdKmVguIifiIiIiIiUBjMwRERERERKToUSMAxgiIiIiIiUnSpNIWMAQ0RERESk5FQofmEAQ0RERESk7FQpA8NF/EREREREpDSYgSEiIiIiUnIqlIBhAENEREREpOxUaQoZAxhSKT+NbNfUQyCqM/N3Fjf1EIjqxZKBy5t6CESvDRWKX7gGhoiIiIiIlAczMERERERESo5TyIiIiIiISGmoUPzCAIaIiIiISNkxA0NEREREREpDheIXLuInIiIiIiLlwQwMEREREZGS4xQyIiIiIiJSGgxgiIiIiIhIaahQ/MIAhoiIiIhI2alSBoaL+ImIiIiISGkwA0NEREREpORUKAHDAIaIiIiISNmp0hQyBjBEREREREpOheIXroEhIiIiIiLlUasMTHR0NEJCQpCQkICsrCy56wKBADdu3Ki3wRERERERUc2EKpSCUTiA2bx5M1auXAlDQ0N06NABRkZGDTkuIiIiIiJSUGPHL2KxGD/++CP27NmD7OxsODo6Ys6cOejRo4dC7fft24etW7fi3r17EIlEsLe3x4IFC+Di4lJjW4UDmPDwcHTo0AFBQUHQ0tJStBkRERERETWwxl7Ev2jRIhw5cgS+vr6wtbXF7t27MX36dISEhMDV1bXatqtWrcLmzZvx7rvvYty4ccjPz8etW7eQlpam0GsrHMCkpaVh6tSpDF6IiIiIiF4xwkaMX65evYro6GgsXrwYfn5+AICRI0fCw8MDK1asQFhYWJVt//77b/zyyy9Ys2YN3N3dX+r1FV7Eb2tri5ycnJd6ESIiIiIiej0cOnQIGhoaGDNmjLRMU1MTo0ePxqVLl5Camlpl2+DgYLRv3x7u7u4oKytDXl5erV9f4QDmvffeQ1RU1Eu9CBERERERNRyBQFDnR3Z2NpKSkuQe2dnZMq918+ZNtGrVCrq6ujLlLi4ukEgkuHnzZpXjPHfuHNq3b48ffvgBnTp1wltvvQU3Nzfs3btX4XtVeAqZmpoaTExMMGTIEHh5ecHa2hpqampy9UaOHKnwixMRERERUd3VxxKYrVu3Yu3atXLlAQEBmDVrlvR5WloaLCws5OqZmZkBQJUZmGfPniErKwvR0dFQU1PDvHnzYGhoiLCwMMyfPx/a2toKTStTOIBZtGiR9M8bNmyotI5AIGAAQ0RERETUyASoewQzefJkeHp6ypXr6+vLPC8sLISGhoZcPU1NTQBAUVFRpf3n5+cDALKysrBjxw506NABAODu7g53d3esW7eufgOY4OBgRasSEREREZGS0dfXlwtWKqOlpYXi4mK58orApSKQeVFFubW1tTR4AQCRSIRBgwYhODgYeXl5clPTXqRwANO1a1dFqxIRERERUSNqzF3IzMzMKp0mVrENsrm5eaXtDA0NIRKJYGpqKnfN1NQUEokEubm5NQYwCi/iJyIiIiKiV1N9LOJXlKOjI+Lj4+U297py5Yr0emWEQiGcnJyQkpIid+3JkydQU1ODgYFBja+vcAamsgU9zxMIBNDS0oKlpSW6desGExMTRbsmIiIiIqI6aMxzLAcPHoxff/0VkZGR0nNgxGIxdu3ahbfeeku6wP/Ro0coKChAmzZtZNouX74cZ8+eRc+ePQEAubm5OHjwIFxdXRU6c7JWAUxFZCaRSGSuvViurq6OqVOnYs6cOYp2T0REREREL0nYiBFMhw4dMHjwYKxYsQJpaWmwsbHB7t278ejRI/z3v/+V1lu4cCEuXLiA27dvS8smTJiAyMhIzJo1C35+ftDX18fOnTuRk5ODTz75RKHXVziA2b9/PxYuXAiRSARfX1+0atUKAHD//n0EBwejtLQUX3zxBR4/fowtW7Zg48aNsLS0xPjx4xV9CSIiIiIiUgLfffcdVq9ejT179uDZs2dwcHDAxo0b0alTp2rbaWtrIzg4GN999x1CQ0NRWFiIdu3aYcuWLTW2rSCQvJhOqcKyZcsQGxuLkJAQufNfSkpK4OPjAxcXFyxevBglJSXw8vKCUCjE7t27FRoIUWPIKSxr6iEQ1Zn5O4ubeghE9SLzzPKmHgJRvdBSOCXQcLx+vVTnPnZOUSyAaGoKL+I/cOAAhgwZUunhlerq6hgyZAiio6NlnsfHx9ffSImIiIiIqFKNuYi/qSkcL+bk5CAnJ0fh60ZGRkr1RhARERERKStV+titcAbG0dER4eHhSE5OlruWlJSEiIgIODk5Scvi4+NhZmZWP6MkIiIiIqIqCQWCOj+UhcIZmLlz52Lq1KkYOnQoBgwYADs7OwDlgcrx48dRVlaGlStXAijfRm3fvn145513GmLMRERERESkohQOYLp27YotW7bg22+/la51qeDs7IyFCxeiS5cuAACRSISTJ09CXf0VWNFERERERPSaU578Sd3VKsLo3LkzoqKikJ6ejqSkJACAlZUVTE1N5eqKRKL6GSEREREREVVLldaev1SKxMTEBCYmJvU9FiJ6xf2yYS02/byuyutq6uo4f+kaACD22lUcjN6Hmzev4+7t2ygoyMeX/7cMw0d4NtZwSYW80dIUEwa7YkA3e7SyMoaWSAP3k9Ox68RVrN12BvmFxdK6n00bgM+nuVfaz+KforE6POal+gWA8YNdMbSnE95ytIKlmT7Ss/Jx5e4jfBd0AhevP2yYmyd6QVlZGcJCghEVuQ2PkpNhZGyMgYOGYGbAbOjo6DT18KiBCFUnfnm5AIaIVJNbf3e0bGkjV3737m2EBP2KPn3ekZadPRODyO3hsLNrhbYODrj6z+VGHCmpmsnDO+N9r7cRfeYGth2+jOKSUvTt1AZffTAYXv1d0HfaOhQWlci0mb9qH54+y5Mpu3xLdqOa2vSrKVLHlv+Mxz+3kxF57AoePMpEc9NmmO7ZHac2zcTU/9uBbYf4/4Aa3vfLlyE8NARuA9zhO3kK7t+PQ0RYCG7dvIGNgUEQChXew4nolVRlAOPo6AihUIh//vkHIpEIjo6ONaamBAIBbty4Ue+DJKJXQ1t7B7S1d5ArX/p/XwIARnh6SctGjx0P38lToK2jg2NHDzOAoQa1+8Q1fL/1FLLzCqVlm3efx72HT7Hovf7wG94FP0edk2mzN+Y6Eh9n1lu/JaVlcPf/GWcuy56BtmXPBVwK/wTfzh6G7Yf/gYLnRxO9lHv37iIiLBT9BwzEDz+ukZZbWVtj+bJvcOhANIZ6DG/CEVJD4RQyACNHjoRAIJAeXFnxnIjoeQX5+Thy+AAsLJqjR8/e0nITE/m1cUQN5e9b8lv8A0DUsatY9F5/vNm6eaXXm+loIr+oGKWlZXXut7S0TC54AYDUjFycuXwfI/u1h7mRLlIycmu6HaKXdih6PyQSCSb5TpYp9xo9Fj+uWono/XsZwLymVOljepUBzLffflvtcyIiADh29DDycnMxfsIk6RceRK8KK3MDAOVBxIsuhn4MfV0tlJSU4q8bSfjvluM4cu52nfutqn6RuARZuYU1Vyaqg9jYWAiFQji3d5Ep19TUhKODI67HXmuikVFDU6VEA9fAEFGd7Nm9EwKBAO+O9Kq5MlEjEgoFWPxefxSXlGL7kX+nMD7LKcTm3efx57UHyMopgL2NGQLG9cLulX54f2kUQqMvvVS/VRnUwwFd2tkg7MAlFIlLaqxPVBdpaakwNDKqdDdYcwsL/PPPZRSLxdDgbrGvHS7iV8DDhw8RHR2NlJQUvPHGG/Dy8oKWllZ9jo2IXnEPHsTjn8uX0LVbd1hZWzf1cIhkrJgzHN1dbPHF+oO4m/hUWr52+xmZetG4ia37/sJf4XPw3Uce2H3iGvIKxLXutzJtWpog8D/jkJz6DIt+iq62LlF9KCwsgEij8uBEpKkJACgoLGQAQ0qt2m0oIiMj8e677yI9PV2m/OzZs3j33Xfx448/IiIiAt988w3GjBmDvLy8KnoiotfRnt1RAIARnqObeCREspbMGAj/MT2xefd5rAg+VWP9jOx8bN59Hkb6Ouje3rZe+rW1NMLBNTMgkQAj5vyKp1n8HUkNT0tLG+LiygNwcVERAECbXzi/lgQCQZ0fyqLaAObUqVPQ1dWVOfNFIpFgyZIlKCwsxIwZM7BhwwZ4enri7t27CAoKaujxEtEroqSkBAf27YWBoSH69a/8TA2ipvDZtAFYPKU/tu67iFnLdyncLuFxBgDA1FC3zv3aWBrh8PoZ0NUWwWP2ZlyPe6L4DRDVgZmZObIyMyEWywcxqSkpMDIyYvblNSWoh4eyqDaAuXXrFjp16iRT9vfffyM5ORkjRozAnDlz0K9fPyxbtgzdunXD8ePHG3SwRPTq+P30SaSnP8XQYcMrnWtN1BQqDqkMif4L/st21qrtGy3Ld85LycipU782lkY4sn4G9HW14DF7M67ceVSrcRDVhbOzM8rKyhB77apMeVFREW7dvoU32zk30ciooQkFgjo/lEW1AUxGRgZatmwpU/b3339DIBBgyJAhMuV9+/ZFQkJC/Y+QiF5Je3aXf4jj9DF6VSye0h+fT3NH2IFLeP+bqErPW1FTE0JfV376jLW5AaaP6o6nWXn485rs7zJF+q1g09wQh9fNgIGeNoZ/FIjLtyvfhpmooQwaMhQCgQChwVtlyndG7UBhQQG3UH6NCQR1fyiLahfxq6uro7i4WKbs2rXy7fc6duwoU25oaFhpupKIXj9pqak498cZtHN2wRtt7Sut8/hRMqL37wUA3I+7BwCIOX0SKSnlU2mGebwLyxZWjTNgeu2979UDS2YMROLjTJy4eA/jBsn+jkrNyMWJC3ehpy3CzV0LsS/mBm4/SEXm/3Yh83u3C/S0RZi8JAKFRSW17hcA9HREOLRuBuxaGGP9jrNoa2uGtrZmMvVPXLir8NbLRC+jrb0Dxk3wxrbwUMz5KAC9e/fF/ftxiAgLQecuXTF0GAMYUn7VBjBWVla4fPkyJk2aBAAoLS3FpUuXYGtrCwMDA5m6WVlZMDIyariRNoHz58/D19cX69atw4ABA5p6OCpr165dWLx4MX777Tc4OTk19XAIwL69u1FaWoqRo6reOjk5ORk/r/tJpuzk8aM4efwoAKCjaycGMFRvOr1ZvguejaURAr8cJ3c95u84nLhwFwVFxfjtVCy6vNkSw/u8CT0dTTzNysPJi/fwQ+gp/HUj6aX6BQBjA120sipfMzpzbM9Kxzlw5i8MYKjBLVj0KVpYWWFn5Hb8fvoUDI2MMH7iJHwYMBtCYbWTb0iJKdMi/LqqNoAZOHAg1q9fD1dXV3Tv3h07d+5ERkYGvLzkP7RcvXoV1rXcRrXig6mWlhaOHTsGMzPZb6pGjBgBfX19hISE1KpfImpYU6a9jynT3q+2TucuXfHXlZuNNCJSdTO+jsSMryNrrCcuLsXMWqyNUbRfAEh8nAnt7gsV7puooaipqWGy3xRM9pvS1EOhRqRC8Uv1a2B8fX1hZWWFpUuXYvjw4diyZQuaN2+O9957T6ZeTk4OTp8+jW7dur3UIAoLCxEYGPhSbYmIiIiIVJ0qLeKvNgOjp6eHnTt3YseOHUhISICNjQ3GjBkDfX19mXpxcXEYNWoUhg0b9lKDcHJywrZt2zB9+nSZLZuJiIiIiIieV+NESD09PUyZMgVfffUVpk6dKhe8AOUL+r/44gu88cYbLzWI999/H2KxuMYsTF5eHpYtW4bevXvD2dkZQ4cORXh4uEydpKQkODg4ICgoCBERERgwYACcnZ3h5eWFq1evVtFz9crKyrBu3Tr07t0b7du3x+TJkyvdce3AgQMYOXIk2rdvjx49euDTTz9FRkaGTB0fHx+MGDECV69exbhx4+Di4gJ3d3fs2iV/pkBhYSFWrVqF/v37w9nZGf369cOPP/6IkpISubrVefDgAWbNmoWePXuiffv26NOnD+bMmYOcnPKtQp9/zwIDA9G3b1906NABfn5+iI+Pl+vv7t27CAgIQNeuXeHi4oKxY8fi7NmzcvUeP36MBQsWoEePHnB2dsbw4cOxf/9+uXpZWVn45ptv0K9fP+l9fvrpp8jNlZ0nXlRUhKVLl6J79+7o2LEjPvzwQ7n3l4iIiEgVcReyRmZnZ4dhw4YhIiIC06ZNg7GxsVwdiUQCf39/XLhwAWPHjoW9vT1OnjyJr776Cs+ePYO/v79M/T179iA/Px/jxo2DQCDA5s2bMWvWLBw7dgwaGhq1Gt+GDRsgFAoxbdo0ZGdnIzAwEPPmzUNk5L/zoivW83Ts2BHz58/H48ePERoaimvXriEqKgqamprSullZWXj//ffh4eGBYcOGITo6WroWaOjQoQDKg6YPPvgAV65cwfjx42FnZ4fr16/j559/xpMnT/Df//5XobGLxWJMnToVampq8PPzg6GhIZ48eYKTJ08iOzsbzZo1k9bduXMnCgsL4ePjg4KCAmzduhW+vr7Yv3+/dNOG27dvY+LEiWjRogVmzJgBTU1N7Nu3D9OnT0dgYCB69OgBAEhNTcXYsWOhoaEBX19fGBgY4Pjx45g7dy7EYjFGjRoFAMjNzYW3tzcePHiA0aNHw8nJCU+fPsWRI0eQlZUFPT096fi++uorGBoaYtasWUhKSsLWrVvxf//3f1i9enWt/j6JiIiIXjdcxN8E/P39sX//fmzZsgVz586Vu378+HGcP38ec+fOxYwZMwAA3t7emDFjBtavX49x48bJBD5PnjzBkSNHpB/QW7VqhZkzZ+LMmTPo169frcZWUlKCXbt2SQMfAwMDLF26FHfu3IG9vT2Ki4uxYsUKODo6IiQkRHqon7OzMz755BPs2LEDPj4+MmP7/PPPpWXjxo2Dp6cnVq5ciSFDhkAgEGDv3r24cOECIiIi0KFDB2lba2trrFy5EtOmTUObNm1qHHtcXBySkpIQGRkJFxcXafmsWbPk6iYlJeHw4cMwNzcHAHTv3h2TJk1CSEgIAgICAADLli2Dra0ttm/fLn0/JkyYAE9PT6xatUoawKxevRpCoRC//fabNGs3ceJETJs2DT/88ANGjhwJoVCIzZs34969e9iwYQPc3NykYwkICJA7a8HY2BibN2+W/gctKytDSEgIcnJyZAIxIiIiIlWjSvvLvTL32rp1awwdOhShoaHIysqSux4TEwMNDQ3pls5AeaTp6+sLsViMc+fOydQfNmyYzIfazp07AwAePnxY67F5eXnJZG1e7Cs2Nhbp6enw9vaWOZF8yJAhsLCwwKlTp2T6E4lEGDNmjPS5pqYmRo8ejaSkJNy/fx8AcPjwYbRt2xYtW7ZERkaG9FERIFy4cEGhsVdkME6ePFnjOT3u7u7S4AUAunTpAnt7e8TExAAozxydP38egwcPRk5OjnRM2dnZ6NWrF65du4aCggJIJBIcPXoUbm5uKCkpkRl/7969kZaWJp2advToUbRr104meKnw4jcJ48ePlynr3LkzSktLkZzMg+KIiIhItQkEgjo/lMUrk4EBgJkzZ+LAgQPYsmUL5syZI3MtOTkZFhYW0NHRkSlv3bq19PrzWrRoIfO8YgpUdnZ2rcdlaWkp87wio1DR16NHjwCUZ3meJxQKYWtrK71ewcLCAlpasidB29raSu+jTZs2SEhIQFxcnDRgeZGiaz9atmyJ9957D+vXr0dQUBC6du2Kfv36wcPDQ2Z61vNjeLHs8uXLAIDExERIJBKsXLkSK1eurPT1srKyIBKJkJ2djfDwcLk1ShUyMzMBlAeBFdPmalLT3wMRERERvf5eqQCmTZs2GDJkCEJDQzFlSt32Lq/qoKYXpyUpQk1Nrd76UlRZWRnefPNNzJ8/v9LrLVu2VLivRYsWYdSoUTh+/DjOnDmDr776Cj///DO2b98OCwuLWo0JAKZPn46333670jrGxsbSgGLUqFEYPrzyE3/btm2r8OtWaIq/B1VTVlaGiLBg7IragcePkmFkZIwBAwfjg5mzoP3Clwd1aV9SXIzvvl2KG9ev4fHjR8jPy4OZmTnaObfH5CnT4ej0ZkPeJr3mBAIBAsb1xNSR3WBraYSnWXnYefwq/m/jEeQXFtfY3txYD59Pc8eQno4wN9ZDSnou9p6OxdebjuJZbqFM3YI/l1fb15c/H8J3QSfrdD+kusrKyhAWEoyoyG14lJwMI2NjDBw0BDMDZst9oVsf7X+POY1Nv2zA7du3INIQoVv37vh47nxYWyv+mYOajlB5Eih19koFMEB5FubgwYPYunWrTLmVlRXOnz+P/Px8mf90FVORrKya7kTvimxPfHw8unTpIi2XSCRISEiQ+7CekpKCwsJCmSxMxa5mFfdhY2ODe/fuVRko1Ja9vT3s7e3h7++PK1euYOzYsYiIiMDHH38sN4bnJSQkSO+vImjS1NSsdlzGxsbQ1dWFRCKpcfw2Nja4c+fOy9wSNYAfvv8W28JD0M9tACb5+iH+/n1siwjF7Vs3sX7jrzWe4Kxo++LiYty8EYsOHd/CUI93oaOji5Qnj7F3z274TRqPNes3oku37o1xy/Qa+v5jD3w4rhf2nIrFjxG/w9HOHDPH9kQH+xYYOmtztV96mBnpIibwQ1ia6iPwt/O4HpeCdm0sMH1Ud/Ts2ApuMzagoOjfIOi9/2yrtJ/Ppw5Am5amOHCGh7nSy/t++TKEh4bAbYA7fCdPwf37cYgIC8GtmzewMTCoxp/JtWl/7OgRzJszG/YOjvhk7nzk5uYiNGQr/CZNQPiOnTA3V/wLT2oaDGCqkZSUhHPnzuHp06cYPnw4rK2tIRaL8fTpU5iamsqsAXkZb7zxBgYNGoTg4GDo6+tLpwn16dMH27dvR3h4OKZNmwagPECoWDRf1VSrxuDs7AwTExNERERg5MiR0vfg0KFDSElJwfTp02Xqi8ViREZGShfxi8ViREVFwcrKSjolbtCgQTh9+jR2794NT09PmfZ5eXlQV1eX2dmsKrm5udDS0oK6+r9/1W3btoW6ujqKiopk6h49ehSpqanSdTAXL17EnTt3pAv4TUxM0KVLF0RERMDb21tut7iMjAwYGxtDTU0N7u7uOHDgAGbMmCG9pxfrAcCAAQOwYcMGnDhxQm4djEQiUar5mMou7t5dbI8IRb/+7vj+h5+k5S2srLFi+VIcOXQAg4d61Et7bR0dhEREyfXhNWYchg3uj5DgXxnA0EtxamUB/zFv47eT1zBhcai0/MGjDPwwdwTGunfA9iP/VNl+wWQ32FoaY/IX4dhx9Iq0/M+rCdj69UTMntgby7eckJZvO3RZrg8rMwPYfWGMSzceIvbek3q6M1I19+7dRURYKPoPGIgfflwjLbeytsbyZd/g0IFoDPWofJZDbdsXFxfj22Vfo3lzSwQFh0FHVxcA0LNXH0wYOwo/r1uLJV993UB3SvVFlT4z1WoR//fff49Bgwbhiy++wE8//SRdxC4WizFs2LAq1zvU1syZM5GbmyuzrsXNzQ3dunXDypUr8Z///AdhYWGYMWMGTp06BX9//0q3Xm4sGhoamDdvHm7cuAFfX1+EhITgu+++w4IFC2Bvb4+xY8fK1LewsMD69euxdOlShISEwMfHB3Fxcfjkk0+k//hGjhyJXr16YfHixZg3bx7CwsIQFBSEzz//HH369MGTJ4r9Uvzzzz/h5uaGpUuXIjw8HKGhofDz84NAIMCgQYNk6lpbW8Pb2xuBgYFYu3Yt/P39YWZmJrOD2pIlS1BcXAwPDw+sWrUKkZGRWLduHaZMmSKzs9ncuXNhbGwMLy8vfPvtt9ixYwd++eUXBAQEYPTo0dJ606ZNQ6tWrTBr1ix8+eWX2LZtG9avXw9PT08uzm9khw9FQyKRYOIkX5lyT68x0NLSxoHovQ3aHgCMjE2gKRIhh+ua6CWNHdgBQqEQa7edkSn/dc8F5BWIMX6wa7Xt+3RqjfxCsUzwAgCRx66ioLAYvsM61zgGH4/OUFMTYsvei7W/AaL/ORS9HxKJBJN8J8uUe40eCy1tbUTvoL2b4QAAIABJREFUr/5nam3aX/rrItJSU+HpNVoavACAo5MTOnfpisOHDqC4uObpl9S0hIK6P5SFwhmYbdu2ITAwED4+PujXr5/MGhU9PT24ubnh5MmT8PPzq/Og7O3tMXDgQBw+fFhaJhQKsWHDBqxevRoHDx5EVFQUWrZsiSVLlsDb27vOr1lXo0aNgqamJjZt2oTly5dDT08Pw4cPx7x58+QyJUZGRvj666/xzTffYNu2bbCwsMDSpUvh4fHvt9tqamrYsGEDfv31V+zduxeHDh2Crq4ubGxsMGPGDIXXrjg4OKBXr144deoUtm/fDm1tbTg4OGDTpk3o2LGjTF0vLy+UlpYiODgYmZmZcHV1xZIlS2BoaCitY29vj6ioKKxZswaRkZHIzs6Gqakp2rVrB1/ffz+4mpubIzIyEmvXrsXBgweRnp4OQ0NDODg44KOPPpLW09PTQ3h4OH766SccO3YMO3fuhJmZGXr27AkjI6Na/R1Q3dyIjYVQKEQ7ZxeZck1NTdg7OuLG9dh6b19aWoqc7GyUlJYg5ckThG79Ffn5+ejZq0/db4hUUienligtLcPFG7I7ThaJS3D17iN0crKutr2mhjoKi+QPC5ZIJCgoKkZraxOYGOgg/Vl+lX34enRCbn4RdlST6SGqSez/fqY6t5f/mero4IjrsdfqrX3Fnzt0lA/wXTp0xIXzfyIh4QHeeKP261eJGoLCAUx4eDjc3d3x2WefSXeQep6DgwMuXqzdt02jRo2SHmj4op9++kmuTFdXF5999hk+++yzKvu0trb+f/buPL6ma/3j+OdkTshIQpCgVIKgxDzWPNU8l9ZUpcbbAR20dVutKtVWdaKDCC5CUKKKqqLVqvzMYp6FJKaESHIy/f7QnEqTkEiI43zfXnndZu21917b3fY+z1nrWYvDhw9nuy2n8pzUq1cv231yOkfHjh3p2LFjro5dvXp1li5desc6dnZ2jBgxghEjRuSuwdnw8fHh/fffz3X9oUOHMnTo0DvWKVu2LDNmzLjrsTw9Pfnvf/9713oeHh5MnjyZyZMnZ7s9p/skp/9/5N7ExETj5uae7TBQL68S7N29i+RkI7a22Q8TvZf9T544Tt+eXUy/F3V2ZvDQ5xk09PkCuCKxRN7FnbkUG48xOTXLtsjoOBpUL4etjTXJKVm3A0ScjMKvnBfVH/dm79ELpvLqj3vj4Xor/9KnpFuOAcyTtStQvnQx5q/ZyfWbSdnWEcmNmJho3NxzeKaWKMHu3btINhqxzWHofl72j46OvlWeTZ5LxrDy6KgoBTAPOQsaQZb7AObUqVP069cvx+3u7u7ZBjYiYh4SExNzfBFmvAATExJzDGDuZf/Spcvw+dffkpyczLkzZ1gbtpobN66TbDRmytsSyS0nBzuMxuyDk0Rj8t91bIm9kX2d2Yu30alpVRa815/xH6/m4IkoKj9Wgun/6YQxOQU7WxucHHLO9RzcuS4AQas1fEzyJzExAbscnrd2f4/sSLjDczcv+ycmJtwqz+ZYGXUTExOzbJOHi5UFRTC5/oRgb29PQkJCjtsjIyNNCfcPO6PRSGxs7B3rODs7Z1mr5WFz7dq1O45Jtba2LtTcIDEvDg4OXM1hfaGMRVAdHHP+N3Ev+zs6OVGv/j8z1XXu1p0BfXow4ew4Pvtybp7aLwJwM9GIp0fRbLc52Nn+XSfn5+Zve07x7JuLmPFSZ1Z+fGuodEpKKt//8BcRJ4vS5ckA4uKz/yDn7uJI52ZVOXQqmt/3nMrfhYjFc3Bw5MrNy9luM/49CY/jHT6n5GV/BwfHW+XZLHidUfdh/0wkD9Hq9A9ArgOY6tWrs2HDhmzXZ0lKSmLVqlXUqlWrQBt3v+zatStTvkZ2pk6dmuPwtofFmDFj2LFjR47bS5cuzaZNm3LcLnI7T08vTp44jtFozPItXHR0FG7u7jn2vhTE/gBOTkVo3rI1Qd9/w7mzZyjj43vvFyQW6cKl61QuXwI7W+ssw8hKebkQc/VGjsPHMoRu2sfKzfsJqFASZyd7jpyJIeZqPFu/HU1ySirHz2b/obBv25o42NsSpOR9KQCenl6cOH4s+2dqVBTu7u459r7kdX/TMLHoKB6rUCFz3YzhZXlYN07kfst1AJORGzF+/Hh69OgBwKVLl9i6dSufffYZUVFROa7O/rDx9/fn+++/v2OdihUr3pdzBwcHF9ixJk6ceMdV6HMzzTLcOW9ILEeVgAD+2P4bB/bvpWatf2ZaSkpK4sihQ9QKvPPsS/nd//b6ALGxsWjtNMmr8IiztK5fiTpVfPjttl4Qezsbqj9eim27T+bqOGlp6ZlyYEp4FKWGXym27jqRaR2Y2w3sVAdjcgoLfwzP1zWIwK0lGrb/vo39+/Zmen4mJSVx6PAhAu/yTM3L/lUDqgGwZ/cu6jfIvH7b3j27KVq0KGXLliuAq5L7yYJGkOU+gGnYsCGTJ0/mvffeY82aNQBMmDABuDWN8LvvvkvNmneenvJh4erqWmALRBamgICAwm6CPELatO3A99/MYdGC+ZkCkBXLQ0hMTMi0Bsy5s2dISUmhXPnH7mn/q1eu4OrmlmURtkuXYti4YR1OTk5UqHB/vkSQR9uyjXuZMLA5o/s2zhTADOlSlyKOdiz+6Z91W8qX9sDWxpojp2PueEyDwcBHL3XB2sqQaQ2Y29XyL02NSqVY+cs+Yq7GF8i1iGVr274D38z9mgXzgzIFIMuXLSUxISHTGjBnz5whJSWZ8o9VuKf9A2vXwdPTkxXLl/HMs4NMUykfPnSInX/toEu37tja2t7Py5UCoByYHPTp04cWLVqwbt06Tpw4QXp6OuXKlaN9+/a5ntZXRB5OFR+vRK8+T7N08ULGvziGRk2acvLECRb/bwG1atfJFIC88PxgLkRGsnNPxD3t/+Pa1fxv4XyebNGK0qXKYGNry5nTpwhbvZK4uDgmvf0uDo6OD/T65dFw4PhFvl6+nRd6NWLxB8+w7vdD+JfzYmTvRmz5v+Ms+emfqY1/nD2Mst4eONafaCor4mjH1u9G88OvBzgVeQXXog70av0EgZXL8NaX69jyfyeyPe/AznUAtPaLFJjHK/nRp19/Fi9awIvjRtOkSTNOnDjO/xYGU7tOXTp0/CcAeX7oICIjz7PnwOF72t/W1pYJr73BhJdfZNCz/enRsxc3bsSzIHge7u4evDBq7IO8dLlHFhS/5C2AAbIsbCgij46XJ7xGqVKlCV2+lG1bf8XNzZ0+ffszYtSYLL0l+dm/Zq3aRBw4wLYtm7kUc4nk5GSKFStG3XoN6Nv/2WzXIhDJrVc+Xs3pC1cZ0qUe7Rr6c/laPF+G/M47c9aTnp5+x32NyansO3qBPm2eoGQxZ24mJhMecY5O475l459Hst3Hwd6G3q2f4OzFa2z4I/s6IvdiwquvU6p0aZaHLGHrr5txc3en79MDGDV6bK6eyXnZv03b9tjbOzD36y+ZOeNDbO3sqFevAf956RV9SW0mzGkhyvwypN/taS7yCLmemFbYTRDJN68nXyvsJogUiKvbphV2E0QKhMNDMPP/5PVH83+MNuax1k+u/7rvNmsX3BonHBQUlK8GiYiIiIhI3igHJhvnzp3LUpaamkpMTAxpaWm4u7vjqDHrIiIiIiIPnAXFL7kPYHJaT8RoNPL9998TGhpaoFMEi4iIiIhI7lhSDky+F+20s7Nj+PDhVK9enQ8++KAg2iQiIiIiInlgKIA/5iLfAUyGwMBAtm3bVlCHExERERERyaLA5kw4d+4cycnZr04sIiIiIiL3jyUNIct1ABMZGZlteWxsLL///jvBwcHUrVu3wBomIiIiIiK5owAmGy1atMCQw/QG6enplC9fnkmTJhVYw0REREREJHdy+pz+KMp1ADNq1Khs/2Lc3NwoV64cDRs2zNWqsCIiIiIiIvcq1wHMmDFj7mc7RERERETkHlnSELJcdZnEx8fTqlUr5s2bd5+bIyIiIiIieWUw5P8nL4xGI9OnT6dx48ZUr16d3r17s3379jy3e9iwYfj5+fHee+/lep9cBTBFihTh2rVrFClSJM+NEhERERGR+8vKYMj3T168+uqrBAUF0blzZ9544w2srKwYNmwYu3btyvUxNm/ezM6dO/N6qblfB6ZGjRrs27cvzycQEREREZH7y8qQ/5/c2rt3L2FhYbzyyitMmDCBPn36EBQUhLe3NzNmzMjVMYxGI1OnTmXo0KF5v9bcVnzllVdYt24dy5cvJz09Pc8nEhERERER87du3TpsbW3p1auXqcze3p6ePXsSHh5OdHT0XY8xf/58EhMT7ymAuWMSf2RkJB4eHjg4ODB16lRcXFyYNGkS06dPx9fXFwcHh0z1DQYDQUFBeW6EiIiIiIjcu4KYRTkuLo64uLgs5S4uLri4uJh+j4iIoHz58lnSS6pXr056ejoRERF4eXnleJ6YmBi++OIL3nrrLRwdHfPczjsGMC1btmT69Ok89dRTnDt3DgBvb28ALl26lOeTiYiIiIhIwbMi/xFMUFAQs2fPzlI+evToTDMSx8TEUKJEiSz1PD09Ae7aAzNz5kzKly9Ply5d7qmddwxg0tPTTcPFNm3adE8nEBERERGR+6sgemAGDhxIt27dspTf3vsCkJiYiK2tbZZ69vb2ACQlJeV4jr1797Jy5UqCg4PvefHNXK8DIyIiIiIiD6eCWAfm30PFcuLg4EBycnKW8ozAJSOQ+bf09HTee+892rRpQ+3ate+5nQpgREREREQk1zw9PbMdJhYTEwOQY/7Lhg0b2Lt3Ly+++KIpPSXDjRs3OHfuHMWLF8+SZ/9vdw1gdu7cSWpq6t2qmXTt2jXXdUVEREREJP/yuo5Lfvj7+xMcHEx8fHymRP49e/aYtmcnMjKStLQ0Bg4cmGVbaGgooaGhzJ07l6ZNm97x/HcNYJYuXcrSpUvvVo309HQMBoMCGBERERGRB+wBxi+0a9eO7777jpCQEAYNGgTcWtclNDSUWrVqmRL8IyMjSUhIoEKFCgC0aNGCMmXKZDneqFGjaN68OT179qRq1ap3Pf9dA5jevXvzxBNP5OWaRERERETkAXqQPTA1atSgXbt2zJgxg5iYGHx9fVmxYgWRkZFMnTrVVG/ixIns2LGDw4cPA+Dr64uvr2+2x/Tx8aFVq1a5Ov9dA5jatWvTqVOnXB1MREREREQefR9++CGffPIJq1atIjY2Fj8/P+bMmUNgYOB9P7eS+EVEREREzNyDHEIGt2YamzhxIhMnTsyxTnBwcK6OldFDk1sKYEREREREzJxVYTfgAVIAIyIiIiJi5u51UUhzdMcA5tChQw+qHSIiIiIico8sJ3yxrN4mERERERExcxpCJiIiIiJi5h7kNMqFTQGMiIiIiIiZs5zwRQGMiIiIiIjZs6AOGOXAiIiIiIiI+VAPjIiIiIiImdM0yiIiIiIiYjYsaViVAhgRERERETOnHhgRERERETEblhO+KIARC2NrY0kdrPLISoov7BaIiIgUGgUwIiIiIiJmTkPIRERERETEbFjSGBMFMCIiIiIiZk49MCIiIiIiYjYsJ3yxrN4mERERERExc+qBERERERExcxY0gkwBjIiIiIiIubOyoEFkCmBERERERMycJfXAKAdGRERERETMhnpgRERERETMnEFDyERERERExFxY0hAyBTAiIiIiImZOSfwiIiIiImI2LKkHRkn8IiIiIiJiNtQDIyIiIiJi5iypB0YBjIiIiIiImdMsZCIiIiIiYjasLCd+UQAjIiIiImLuLKkHRkn8IiIiIiJiNtQDIyIiIiJi5pTELyIiIiIiZsOShpApgBERERERMXOWlMSvHBgRERERETEb6oERERERETFzGkImIiIiIiJmQ0n8IiIiIiJiNiwoflEAIyIiIiJi7qwsqAtGSfwiIiIiImI21AMjIiIiImLmLKf/RQGMiIiIiIj5s6AIRgGMiIiIiIiZ0zTKIiIiIiJiNiwoh19J/CIiIiIiYj7UAyMiIiIiYuYsqANGAYyIiIiIiNmzoAhGAYyIiIiIiJmzpCR+5cCIiIiIiIjZUA+MiORbWloaC4PnsyxkMZHnz+Pu4UGbtu0ZOXosTk5Ohd08kVx5vKwXrz/fnif8ffD2dMXWxpqzF6/w028H+ThoIxcvxRV2E0VyRc9ky2RJs5ApgBGRfJs+7X0WLQimRavWPDtwCCdOHOd/C4M5FHGQOd/Ow8pKnb3y8Ctdwo2SxV344Zc9nI+6RkpqGgGPl2JI90b0ahtIvT5Tibl6o7CbKXJXeiZbpgcdvxiNRj799FNWrVpFXFwc/v7+vPjiizRo0OCO+61fv561a9eyd+9eLl++jLe3N82bN2fkyJE4Ozvn6twKYEQkX44dO8r/Fi6gZas2zPz0M1N56TJlmPb+FNatDaPDU50KsYUiubN5xxE27ziSpXxb+DEWTh/KM53rMzNoYyG0TCT39Ey2YA84gnn11VdZv349zz77LGXLlmXFihUMGzaM4OBgatasmeN+b775Jl5eXnTp0oVSpUpx+PBhgoOD2bp1K8uXL8fe3v6u51YILiL5si5sDenp6Qx4dmCm8h49e+Pg6EjYmh8KqWUiBePMhSsAuLlo6I08/PRMtlyGAviTW3v37iUsLIxXXnmFCRMm0KdPH4KCgvD29mbGjBl33HfWrFmsXr2acePG0atXLyZNmsSUKVM4evQoYWFhuTq/AhgRyZf9+/djZWVFQLXqmcrt7e3x9/PnwP59hdQykXtjb2dDMbcilPZyo2V9fz6b1BeAn7YdKOSWidydnsnyIKxbtw5bW1t69eplKrO3t6dnz56Eh4cTHR2d47716tXLUtaqVSsAjh8/nqvzawiZiORLTEw0bu7u2NnZZdnmVaIEu3fvItloxDab7SIPo8HdGvLxq71Nv586f4nBr8/jt125e7GKFCY9ky1XQSTxx8XFEReXdcISFxcXXFxcTL9HRERQvnx5ihQpkqle9erVSU9PJyIiAi8vr1yf99KlSwC4u7vnqr4CGBHJl8TEBOxss38R2v09jjUhMVEvSzEbq3/Zy+FTURR1tKeGfxk6NqtGMbeihd0skVzRM9lyFUQKTFBQELNnz85SPnr0aMaMGWP6PSYmhhIlSmSp5+npCXDHHpjszJ07F2tra9q0aZOr+gpgRCRfHBwcuXLzcrbbjElJADg6ODzIJonky/noa5yPvgbA6s17WfnzbrYtGI+jox0zvltfyK0TuTM9ky1YAUQwAwcOpFu3blnKb+99AUhMTMTW1jZLvYwE/KS/77XcWL16NcuWLWP48OH4+vrmah8FMCKSL56eXpw4fgyj0ZhlyEJ0VBTu7u76pk/M2v6jkew5dI7hvZoogJGHnp7JlisvSfg5cXFxzhKsZMfBwYHk5OQs5RmBS25mEgPYuXMnb7zxBk8++STjxo3LdTuVxC8i+RIQEEBaWhr79+3NVJ6UlMShw4eoUjWgkFomUnAcHGxxd9UsZPLw0zNZHgRPT89sh4nFxMQA5Cr/5dChQ7zwwgv4+fnx8ccfY21tnevzK4ARkXxp274DBoOBBfODMpUvX7aUxIQErTcgZqNEsewXUGta+3GqVijFjr2nHmyDRO6BnsmWy2DI/09u+fv7c/LkSeLj4zOV79mzx7T9Ts6cOcNzzz2Hh4cHX3/9NU5OefuCSEPI5L75888/efbZZ5k/f75pyrxnnnkGgODg4Fwf59VXX2Xjxo3s3LnzvrRT8ufxSn706defxYsW8OK40TRp0sy06nPtOnXp0FEvSzEPs17vS8niLmz+6whnLlzBwc6WmlV86NU2kOs3E3l1ZmhhN1HkrvRMtlwPch3Ldu3a8d133xESEsKgQYMAMBqNhIaGUqtWLVOCf2RkJAkJCVSoUMG0b0xMDEOGDMFgMPDtt9/i4eGR5/MrgDEzc+bM4bHHHjPNl303n332WbazSWT4v//7vyxT4Ink1YRXX6dU6dIsD1nC1l834+buTt+nBzBq9FisrNTRK+Zh6bqdPP1UPZ7uWJfi7kVJT0/nzIUrfLv8Nz4O2sjZi1cLu4kiuaJnsoV6gBFMjRo1aNeuHTNmzCAmJgZfX19WrFhBZGQkU6dONdWbOHEiO3bs4PDhw6ay5557jrNnz/Lcc88RHh5OeHi4aZuvry81a9a86/kVwJiZOXPm0KpVq1wHMBneeecdHLKZdSS3SVYF5dtvv32g55MHw9ramoGDhjBw0JDCborIPVu+YRfLN+wq7GaI5JueyfIgfPjhh3zyySesWrWK2NhY/Pz8mDNnDoGBgXfc79ChQwB88803WbZ169ZNAYz8o3379rmaVeJ+y25hLRERERHJn4KYhSwv7O3tmThxIhMnTsyxTnYpA7f3xtwr9SPeZ5999hl+fn6cPXuWCRMmEBgYSGBgIK+99hoJCQmmeikpKcyePZuWLVsSEBBAq1at+Pzzz0lNTTXV8fPz4/r166xYsQI/Pz/8/Px49dVXC6Sdf/75J35+fqxfv57PP/+cJk2aUK1aNQYOHMjp06ez1F+4cCEtW7akevXq9OzZk507d/LMM8+Yclxykl2d4OBgOnbsSI0aNahTpw7du3dn9erVWfa9cOECI0aMoGbNmtSvX59p06Zl+vsRERERsVQPMom/sKkH5gEZO3YsPj4+vPzyyxw8eJCQkBA8PDwYP348AJMmTWLFihV07NiRwMBAdu7cyaxZs7hw4QJTpkwBbnXVvf3221StWpXevXsD5HrBn9jYWFJSUjKV2dra4uycedadL7/8EisrK5577jni4uL49ttveeWVVwgJCTHVWbRoEe+88w5169Zl0KBBnD9/nlGjRuHi4kLJkiXz9PeydOlSpkyZQs+ePRk4cCAJCQkcOnSIPXv20KnTP4mGKSkpDBkyhFq1ajFhwgR+//13vvvuO3x8fHj66afzdE4RERGRR40ZxR/5pgDmAalWrRrvvPOO6fdr166xbNkyxo8fz6FDh1ixYgV9+/blv//9LwD9+/fH2dmZJUuWMGDAAPz9/enSpQvvvvsuPj4+dOnSJU/nzy5npmrVqoSGZp5VJyUlhdDQUNPqqq6urrz33nscOXKESpUqYTQa+fTTT3niiSf4/vvvsbG5dQtl9AblNYDZvHkzzZo147333rtjvYSEBLp27crw4cMB6NevH926dWPZsmUKYEREREQsKIJRAPOA9O3bN9PvtWvXZsOGDdy4cYNff/0VgMGDB2eqM2jQIJYsWcKWLVvuOp/23Xz++edZ5tguWrRolno9evQwBS8Z7QQ4e/YslSpVYv/+/Vy7do3evXubgheATp06ZZp1IrdcXFzYsWMHx48fzzTFXnb69OmT6ffAwEB++OGHPJ9TRERERMyXApgHxNvbO9PvGQn1sbGxnD9/HhsbmyzDwcqWLYuNjQ3nz5+/47GNRiOxsbGZyjw8PDKtaFq3bt1cJfHn1M64uDjg1nzeGW27nY2NDaVLl77r8f9t2LBh/P7773To0IEKFSrQuHFjnnrqKapXr56pnpOTE25ubpnKXF1ds1y3iIiIiCV60En8hUkBzANyezBxu/T09Hwfe9euXTz77LOZyn7++WfKlCmT52Pdz3Zmp0KFCqxbt47NmzezdetW1q5dS1BQEGPHjmXUqFF3bZcUrLS0NBYGz2dZyGIiz5/H3cODNm3bM3L02FytkpvX/bdu+ZW5X3/J4cOHsLO1o179+vzn5fGUKeNzPy5PLMQrQ9pQ078MNSv7Ur5McU5HXsa/49t5Ps7TT9VlTP/m+JUrQVx8Imu37Oetz37g0tUbWerWCSjL5NGdqBNQjvT0dP7Yc5I3Z61i75E7fwElcid6JktemFMSfn4pgHkIlC5dmpSUFM6cOUO5cuVM5WfOnCElJSVTz4Yhm7vT39+f77//PlOZp6fnfWlrqVKlADh9+rRpeBncyp05f/48fn5+eT6mk5MTHTp0oEOHDiQnJzN27Fi++OILhg0bpmmXH7Dp095n0YJgWrRqzbMDh5hWbz4UcZA538676wJoedl/44b1vPLiWCr5+fPSy+O5ceMGC4KDGDSgH4uWLsfLq8T9vlx5RL07pjOXr8Wz+9BZXJ0d7+kYY/o358NXerBl51Femb6c0iXcGDugBfWql6fJgOncTDSa6tatVo6f5o4jMvoa734ZBsCIPk3Z+N2LNB80kwPHIgvkusTy6JkseWFB8YsCmIdBs2bNmDlzJkFBQbz99j/fEs6fP9+0PYOjo6NpOFcGV1dXGjZs+EDaGhAQgJubG0uXLqVLly6mPJjVq1ff03Cuq1ev4u7ubvrd1taWxx9/nM2bN2M0GhXAPEDHjh3lfwsX0LJVG2Z++pmpvHSZMkx7fwrr1obR4alOBbJ/cnIyH7z/LiVLejNv/kKcihQBoFHjpvTr3Z2vPp/NW/999z5dqTzqKj/1NqfOXwZgZ8jrFHXK24K9xdyK8Paop9i5/xTth88iLe1WD3T4gdMs/3QEo55+kunfrTfV/2hCT4zJKbQe+gmRMbeeg8vX/x+7QifxwUvd6DTy8wK6MrEkeiZLnllQBKN1YB4C/v7+dOvWjUWLFvHyyy+zcOFCXn75ZRYtWkTPnj0z9WpUrVqV7du38/333xMWFsaePXtydY4ff/yRVatWZfnJa9BhZ2fHmDFj2L17N4MHD2bBggVMmzaNDz74INdTOt9u6NChPP/883z55ZeEhIQwbdo05s2bR7NmzbKdZEDun3Vha0hPT2fAswMzlffo2RsHR0fC1tx5woS87B++8y9ioqPp1qOn6UUJ4F+5MrXr1OWndWtJTk4ugKsSS5QRvNyrTs2rU8TRni8W/2oKXgDWbtnPibMx9OtYx1T2mE9xageUI3TjLlPwAhAZE0voxl20qOdHiWKZp6sXyQ09k0Vyph6Yh8SUKVMoU6YMoaGh/PTTT3h5eTF27FhGjBiRqd6QHoaZAAAgAElEQVTEiRN58803+eSTT0hMTKRbt27UqFHjrsd/6623si1fuXIlrq6ueWrrgAEDSE9P5/vvv2fatGn4+/vz5ZdfMmXKFOzt8/ZNZ58+fVi9ejXz5s3j5s2blCxZkgEDBjBy5Mg8HUfyb//+/VhZWRFQLfMECvb29vj7+XNg/74C2z/jv2s8UTPLcarXeIIdf/7B6dOnqFjx8Xu9HJF7Fljl1iQlf+49mWXbjn2n6N0ukCKOdsQnGKld9Q51955iUNeG1Kzsy7ptB+5vo+WRo2ey5JWS+KXAjBkzhjFjxmQp7969O927dzf9bmNjw+jRoxk9evQdj1euXDmCg4Pzff5/q1evHocPH85SXqZMmWzLn3nmGZ555hnT72lpaZw/f54qVarc8Zj/bnufPn2yTI/8bx988EG25bm9NsmdmJho3Nzdsx2251WiBLt37yLZaMQ2h2F9edk/Ojr6Vnk2Y6q9vLwAiI6K0stSCoW3560vdSKjs/ZQR0Zfw8rKCm9PN46dib5z3ZhrAJTycsuyTeRu9EyWvLKkJH4NIZM8S0pKylK2cuVKrl27Rt26dQuhRVIQEhMTsLPN/kVo93fPWkJiYoHsn5iYcKs8mxdrRt3EO5xL5H5ycrh1XyYZU7JsS/y7zMnh1npZjneqm5S5rkhe6JkseWUogB9zoR4YybPw8HBmzJhBmzZtcHNz4+DBgyxbtoxKlSrRrl27wm6e3CMHB0eu3Mw+d8D4d9Dq6OBQIPs7ONyaGcpoNOZY1+EO5xK5nzJmGLO3syExKfO4fwc7m7/r3CpPuK3uvznYZ64rkhd6JovkTD0wkmc+Pj54eXkRHBzMlClT+Pnnn+natSvz5s3TrGFmzNPTi2tXr2b7AouOisLd3T3HoQp53d80JCE6KmvdjKEMJTRlpxSOC38n45fyypofWMrLjbS0NC78PTzsjnU9bw0di4y+dr+aKo8wPZMlzyyoC0YBjOSZj48PX331Fb/99hv79+/nt99+4/3336dYsWKF3TTJh4CAANLS0ti/b2+m8qSkJA4dPkSVqgEFtn/VgGoA7Nm9K8tx9u7ZTdGiRSlbttw9XolI/oQfPA1Averls2yrW60cR05FE59w60PhzgN3qFu9HGlpaeyKOHMfWyuPKj2TJa8MBfDHXCiAEREA2rbvgMFgYMH8oEzly5ctJTEhIdN6A2fPnOHkieP3vH9g7Tp4enqyYvkybsbHm8oPHzrEzr920LptO2xtlTcg959PSXcqlSuBjc0/r8M1m/dxM8HIC32aYWX1zwu9Q9MAHvPxZPGPf5nKTpy9RPiB03RvVdOU0A+3JgLo3qomm/86QtTl6w/mYuSRomey5JXBkP8fc2E9efLkyYXdCJEHJSWtsFvw8CpWrDhXr15l9aoVHDlymJvx8YStWc2cr76gVmBtXh4/EcPfT7e+Pbsxd85XvDBqzD3tb21tTUlvb1aELmPr1i2kpaXy5x9/8P57/8XR0YkPps/UOkB3MG3u2sJuwkOtX8c6dGxWjca1KtKsTiWcHO2xsbGica2K+Hp7sP9opKluyMfD+eClbgSt3E7sjVuJzDcTjSQZkxnYtQGNa1XEztaGjs2q8cFL3Tlx9hIj/ruQ5JRU0zEOHr/AoK4N6NbyCawMBupWK8dnb/SlaBEHnnn1e6IVwOTo1ec7FHYTHlp6JpsXm4egSyD2Zkq+R5C5OZlHerwhPT09/e7VRB4NiVknCpLbpKamsiA4iOUhS4g8fx43d3fatuvAqNFjMy1u1r51CyIjz7PnwOF72j/Dr5t/Ye7XX3L0yGFs7eyoV68B/3npFXzuYVFUS+Je587TrVu6n+aOo2nt7Kd73bLzKG2HfZqlrl+Htzhz4UqmugM61WPMgOZUKluCuPhEftyynzdnrSLm6o0sx61XvTxvj3yKOtXKkZ6ezh97TvLWZ6vYfehcwV7cI+bqX7MLuwkPNT2TzYfDQ/C5/9Sl/M8UV664eUzWoABGLIoCGHkUKICRR4UCGHlUPBQBzOUCCGCKmUcA8xD8dYuIiIiISH6YUxJ+fimAERERERExc+aUhJ9fCmBERERERMycBcUvmkZZRERERETMh3pgRERERETMnIaQiYiIiIiIGbGcCEYBjIiIiIiImbOkHhjlwIiIiIiIiNlQD4yIiIiIiJmzoA4YBTAiIiIiIubOkoaQKYARERERETFzBgvqg1EAIyIiIiJi7iwnflESv4iIiIiImA/1wIiIiIiImDkL6oBRACMiIiIiYu6UxC8iIiIiImbDkpL4lQMjIiIiIiJmQz0wIiIiIiLmznI6YBTAiIiIiIiYOwuKXxTAiIiIiIiYOyXxi4iIiIiI2VASv4iIiIiIyENIPTAiIiIiImbOkoaQqQdGRERERETMhnpgRERERETMnCX1wCiAERERERExc0riFxEREREReQipB0ZERERExMxpCJmIiIiIiJgNC4pfFMCIiIiIiJg9C4pglAMjIiIiIiJmQz0wIiIiIiJmTrOQiYiIiIiI2TAY8v+TF0ajkenTp9O4cWOqV69O79692b59e672jYqKYty4cdSuXZtatWoxcuRIzp49m+tzK4ARERERETFzhgL4yYtXX32VoKAgOnfuzBtvvIGVlRXDhg1j165dd9wvPj6eZ599lvDwcEaMGMHYsWM5ePAgzz77LLGxsbk6t4aQiYiIiIiYuwc4gmzv3r2EhYXx2muvMWjQIAC6du3KU089xYwZM1i4cGGO+y5atIjTp08TGhpKlSpVAGjSpAmdOnVi3rx5jBs37q7nVw+MiIiIiIjk2rp167C1taVXr16mMnt7e3r27El4eDjR0dE57vvTTz/xxBNPmIIXgAoVKtCgQQN+/PHHXJ1fAYyIiIiIiJkzFMCf3IqIiKB8+fIUKVIkU3n16tVJT08nIiIi2/3S0tI4fPgwAQEBWbZVq1aNU6dOkZCQcNfzawiZiIiIiIiZy2sSfnbi4uKIi4vLUu7i4oKLi4vp95iYGEqUKJGlnqenJ0COPTDXrl3DaDSa6v173/T0dGJiYvD19b1jOxXAiEVx0B0vj4CEXbMLuwkiIvKQKYjPOHODgpg9O+s7ZvTo0YwZM8b0e2JiIra2tlnq2dvbA5CUlJTt8TPK7ezsctw3MTHxru3UxzkREREREWHgwIF069YtS/ntvS8ADg4OJCcnZ6mXEaBkBCP/llFuNBpz3NfBweGu7VQAIyIiIiIiWYaK5cTT0zPbYWIxMTEAeHl5Zbufm5sbdnZ2pnr/3tdgMGQ7vOzflMQvIiIiIiK55u/vz8mTJ4mPj89UvmfPHtP27FhZWVGpUiX279+fZdvevXspW7Ysjo6Odz2/AhgREREREcm1du3akZycTEhIiKnMaDQSGhpKrVq1TAn+kZGRHD9+PNO+bdu2Zffu3Rw8eNBUduLECf744w/atWuXq/Mb0tPT0wvgOkRERERExEKMGzeOn3/+mYEDB+Lr68uKFSvYv38/QUFBBAYGAvDMM8+wY8cODh8+bNrvxo0bdOvWjYSEBAYPHoy1tTXz5s0jPT2dlStX4u7uftdzK4AREREREZE8SUpK4pNPPmH16tXExsbi5+fHSy+9RMOGDU11sgtgAC5evMj777/Pb7/9RlpaGvXq1eONN97Ax8cnV+dWACMiIiIiImZDOTAiIiIiImI2FMCIiIiIiIjZUAAjIiIiIiJmQwGMiIiIiIiYDQUwIiIiIiJiNhTAiIiIiIiI2VAAIyIij5zt27cTGxtr+j01NZW0tLRCbJFI/qSmppr+WytgiKVTACMiJjExMbz55ptEREQAkJKSQkpKSiG3SiRvduzYweDBgwkKCgIgLS0Na2trrKysiImJISEhwVRXHwTlYbZw4ULeeustAKysrExBjMFgMNXRPSyWSAGMiJj8/vvvhISE8P333wNgY2ODjY0NqampbNiwgfDw8EzfAoo8jMqVK0fnzp1ZunQpJ0+exMrKips3b/Laa6/RtWtXBgwYwCeffAJk/iAo8jC5efMmW7duZenSpezduxeDwYC1tTUAf/75JyEhIZw9e9b0JZMCGbEk1pMnT55c2I0QkcKVnp6OwWDA39+fU6dO8dtvv/HYY49Rvnx5Vq5cydChQ1m5ciUrV64kOjoaX19fPDw8SEtL0wdAeegUKVIEFxcXfvzxR65cuUL9+vWZNGkS+/fvp0mTJly9epU1a9ZgMBioW7duYTdXJIu0tDTs7OwoVqwY//d//8eePXvo3r07MTExjB07llmzZrFlyxY2btxIcnIygYGBehaLRVEAIyIYDAZSU1OxsrLCw8ODzZs3c+rUKVq2bMmsWbOoWbMmgwYNomzZsixfvpzIyEjat2+vF6Y8VNLT003BuLu7OwkJCSxevBhPT0/Cw8MZO3YsQ4YMoW3btiQnJxMUFEStWrUoU6ZMYTddJAuDwYCPjw/R0dGEhYVRpUoVoqKi+O233/jPf/5Djx49OH78OGFhYVSuXJly5crpSyWxGApgRMSUIwBQqlQpIiMj2bZtG9u2bcNoNDJp0iQCAwNp0KABN27c4Oeff8bR0ZFq1arphSkPhZSUFKytrU33op2dHe7u7uzZs4dly5YRGBjIiBEjALC3t6dChQr8/vvv7Nu3jyeffBIHB4fCbL6ISVpaGlZW/4zw9/DwYO/evaxZs4Z9+/bRt29fevbsSfny5fHz8+PIkSP88MMPDBo0SM9isRjKgRGxYBn5LBkvy+vXrwPQp08fSpcuzaFDh2jZsiXe3t4YjUYAevXqhb+/P0FBQcTFxWFlZaWx11JoMmYWs7GxITk5mSVLlrBy5UoAHnvsMXr16oWdnR3p6ekkJiaa7lVvb2+GDRvGn3/+yaZNm3QPS6FLT0839YQD3LhxAwB/f386duyIlZUV169fp0mTJqZ9qlSpQq9evYiKiuKrr74CUJ6iWAT1wIhYsIwX5dq1a5k1axb79u2jevXqlCpViuTkZDZt2oSzszPt27fHysoKg8GAq6srycnJbN26ldjYWBo3bgwoGVoKR8Z9FxISwrBhw9i3bx87d+6kYcOGeHp64urqyokTJ4iIiKBjx444Ozub9i1ZsiTHjh1j48aNtGjRAhcXl8K6DLEgV65cMeW4ZPRgZ/S6WFlZcf78eWbOnMlvv/2Gvb09Pj4+lCpVigMHDrBv3z569+6Nh4cHycnJWFtbU6xYMaKioli0aBH9+/fH0dHRNJRS5FGlHhgRC3b+/HkGDx7MG2+8QWpqKunp6aZv/bp3706jRo3YsWOHaQacjF6YNm3aUL9+fdasWcORI0dML2CRwrB06VJmzpxJy5Yt+fDDD5k3bx4VK1YEwMfHh969e3Pp0iWWLFmSaT9nZ2eee+45Ll26xKJFi0hOTi6M5osFCQ8Pp1OnTsycORP450ukjJ7smTNn0rFjR8LDw0lKSjL1ppQsWZIuXbpQpEgRZs+eDdzqdQQoXrw4Xbt2xdnZmalTpwKakUwefeqBEbFgX3zxBdu3b+f111/nmWeeoUOHDri6upKamoq9vT2Ojo5s3bqVixcv0r59e6ytrUlNTcXR0REHBwe2bdvGrl276N69u77tkwcuLS2NxMREJk+ejJubG5MnT6ZSpUq4uLhkSei/fPkyy5cvp3nz5hQrVsx0jGLFinHmzBn+/PNPunTpgr29fSFekTyqMnpLHBwc+OWXX6hQoQJ16tTBYDCYnp3z589n8eLFDBw4kOHDh9O1a1cqVKhgOoaPjw8XLlwgLCyM2rVrU6ZMGYxGI9bW1nh6epKQkMDChQtp0qQJ3t7ehXWpIg+EemBEHnE59YycPXuWhQsX0qFDB7p27YqXl5epfsa3gm3atKFRo0b88ccfrFu3LtPxGjduTIsWLQgMDDR9WBS5H7Ib05+eno6VlRVXr17l0KFD9OjRA29vb9P9aTAYTPexu7s73bp1w8nJia+//jrT4qx2dnZMnDiRsLAwDSGTAnflyhX69u1LWFgYKSkpeHh4MGfOHF566SVsbGxMwUtycjLr1q2jYsWKDB8+HH9/fxwcHEz3c1paGg4ODjz11FOUKVOGGTNmAJiGoTk6OtKiRQs6duxIkSJFCu16RR4UBTAij6i0tLQss9nc7ty5cyQnJ1O1alVTfcCU65LxobF///64ubmxYMECkpKSsLW1JTk5GSsrK8aPH89//vOfTN8iihSUjMA4Y4a8PXv2cODAAa5cuWIKmKOjo3F2dubMmTNA5lysjH8DcCvZ+emnn+ann35iy5Ytmc7j5uYGkCmwESkoRqOR+fPnc/nyZeBWr9/169d5//33+eWXXwC4du0aRYsWxcXFBWtra65cuUJISAjffPMN7777LsuWLQOgfv36tG/fnoMHD5rKMu7bGjVq8NFHH5mGT4o8yjSETOQRlPHttMFg4Pjx48yfP5/jx49z48YNfHx8AEhISOB///sfdevWJSAgwPQhETJPq1yiRAkuXbrEunXrSE9Pp169eqZtGf+rqZTlfsgIjA8cOMDIkSMJCgpi+fLlbNy4kRs3blC7dm2cnJxYtWoV8fHxNGjQABcXF1JSUkz3v8FgICEhAUdHR1xdXfn5558xGo20aNEiyz2bU7Avcq8cHR3x9fVl3rx5uLu788QTT2BlZcW+ffv46KOPuHLlCm3btsXZ2ZnTp0+zceNG5syZw3fffcf+/fsJDw/n6NGjrF27FgcHB2rVqkWxYsU4cOAAS5cuZciQIVmGPep5LJZAAYzII8hgMJCSksL06dN57bXXOHfuHJs2bWL58uXExMRQtWpVfH19+fXXXzl69CitWrXCycnJlDOQkbC/a9cuSpUqRZkyZdi/fz8NGjTA398/2/OJ3A+7d+/mxRdfpGTJkgwaNIjWrVtz9epVFi5cSPHixQkMDOTmzZusWLGCYsWKERgYaApEUlJSCA4O5ocffqB58+a4urrSsmVLevTooXtWHhhPT0+io6NZtWoVzZo1o1ixYpQqVYpr166xfft205paVatWpWbNmlhbW9OtWzc6dOjAa6+9xoABAzhy5Ahbt26lf//+FC9enBs3buDi4kKTJk2ws7PLdD/r3hZLoABG5BG1evVq5s+fz9ChQxk1ahQvvvgiRYoUYfny5URFRdGyZUsMBgNLlizBxcWFihUr4ujoCEBUVBRffvkl27Zto2nTphQvXpxOnTqZhpuJPChz5szh7NmzvPXWWzRt2pTKlStTsWJFwsLCOHjwIO3bt6dZs2Zs2LCBrVu34uTkRLly5UyLsS5ZsgQHBwcaN26Mg4ODabjY7ettiBS02+8vGxsbSpcuzYoVK4iPj6du3brY2dnh6+vL1q1bOXr0KE2aNMHDw4PSpUvTtGlTqlatSrly5bC1tcVoNLJ8+XLs7e1p164d9vb2BAQEmP5bAYtYIgUwImYsp7n+jUYjkydPxtvbmzfffJMSJUpgZ2fHyZMnWbt2LQaDgTZt2lChQgWuXbvGd999R2RkJC4uLvz1118sW7aM9evX06lTJ9M3gtbW1pkSpEUKQlpa2h3XrJg+fTpPPvkkPXr0wMrKioULFzJx4kTc3d0ZPHgwlSpVwtHRkcqVK3P8+HEWLVrE4sWL+fnnn1mxYgVVqlThzTffxMPDI9NxFbzI/ZAxfMvKyoqbN29y4cIFXF1dTesPBQUFUa9ePUqVKoWbmxsJCQls2bKF1NRU6tevn+lYKSkpHDt2jDVr1rB9+3b69u1LnTp1gH/uXwXiYqlsCrsBInJvUlJSTOsA/Nvly5eJiYmhX79+2NnZsWfPHt577z0OHDjAgAED6NGjhylZ9J133iE1NZWNGzeydu1aHB0dcXd356233qJ9+/aZjqsXpRSk1NRUUx5VZGQkN2/exM3NjeLFiwO3EvRtbGxIT09n69atfPjhh5w9e5Z+/frRp08fSpUqhZ2dHQBPPPEEX3zxBWFhYZw7d474+HhatGhBw4YNAe44oYVIfmXcyxn3WFRUFM899xwlSpTgm2++wd7ens6dOxMWFsbcuXOpVKkSHh4e9O7dm61bt7JhwwZatWpFQEAAiYmJLF68mIiICI4ePcqRI0cYOHAgffv2zXLe23MXRSyJIV1zn4qYldu/rU5LS2PJkiVYWVnh5eVF8+bNTfUaNWpEmzZtSEpKIjQ0lEaNGjF8+HACAgJwcnIC4Pr16zg7O5OUlER0dDQxMTHcuHGDpk2bmo6jD35yPyUkJPDuu++yfv16bG1tsbGxoU+fPgwfPhxbW1uee+45tm3bho2NDS1atGDw4MFUqVLFlLgcFRXFrl27aNeuXY7nuD1QErmffvnlF9auXUv58uVZuXIlBoOBl19+mTZt2pCSksK6desYP348U6dO5amnnsLGxob169fzwQcfUK9ePdNClK+88goXL16kdOnSPP/886b1YO7UWyliSTSETMTMZLy81q5dy6BBg/jrr7/49ddfWbFiBSdOnMDHxwdPT0/OnDnD4sWLuXHjBm+//TZDhgyhQoUKpm+s161bx7Zt26hRowZ2dna4urri7e1N2bJlATLN5CRyP1y5coVx48Zx6NAhBgwYQOvWrUlNTWXFihVcvHiR5s2bU7p0aUJDQ3nyySeZNGkSjz/+uKnn8fjx43zwwQds376dTp06mcozPuTdPpxH5H4yGo1Mnz6d999/nzJlynD9+nUMBgMHDx4kNjaWli1b4uDggJeXF8eOHWPTpk00b94cFxcXKlSowKFDhwgPD6dYsWI8/vjj1K9fn1atWtGlSxc8PDxMQy11L4vcon8JImZo3759fPbZZzRs2JBPP/2UkJAQZs+ezZYtW5g1axYJCQm0bNkSLy8v/Pz8aNmyJR4eHhgMBuLj49m4cSMzZswgMTExx3PkNDxNpKD88ccf7N+/nzFjxtC/f3969OjBtGnTqF69OkuXLmXv3r0EBgbSqVMntm/fzvLlywG4ePEi27dvZ9asWRw9epR+/fpha2trOm5G0K0Pe/KgnDlzhtWrV9OlSxcmTZrERx99RHBwMJ07d2bXrl2EhIQA4OrqynPPPcf58+dZuXIlSUlJAPTr14+4uDhWr15NSkoKLi4ueHp6Av/0gut+FvmHPqGIPMT+PVwg4/e1a9cSFxfHyJEjeeyxxwAoUqQIVlZWREREcOTIEQIDA+nduzezZ8/mzTffpEePHsTHxxMREUFoaCilS5emffv2mT74iRS07IZvZdzH4eHhlCxZks6dOwOwc+dOpk2bxokTJ+jfvz/u7u4AvPvuu1y5coXPPvuMBQsWUKxYMS5fvoyjoyNvv/02zZo1e+DXJZYnI2E+u17po0ePcvnyZbp164a3t7cp6JgwYQLHjx/nhx9+oEWLFpQtW5YqVarQq1cvFi1aRMuWLalcuTLVq1dnypQp1K9fP8uXRwpcRLJSACPyEMr40Jdd8JKSksL+/ftp0aIFjz32GNeuXWPGjBmEhoZSq1YtXnjhBSpUqECRIkUYMWIEBoOBpUuXsnr1atzc3EhNTaVnz55MmDBBw8Pkvsm4XzNmrzt//jxeXl6Zpn2NioqiZMmSHDlyhDlz5rBmzRoaN27M119/jb+/P0WLFgXAwcGBjz76iD179vDnn3+SlJTEY489Rv/+/bOcT+R+uH1x37Nnz+Lu7k6RIkVM91xGT8r169eBW0FHWloaxYsXp1u3bkyZMoUlS5YwYcIEnJyc6NevH6GhocyePZsZM2bg6OhIq1atAOVsieSGkvhFHmK///470dHRVK5cmfLly5vyV8aMGcPFixfp3bs306ZNw9XVlREjRtCyZUtcXV2xtrYmMTERBwcH4NYMT5GRkVy/fp2qVavi5eUF6EUpBe/f99SSJUv46quvSEhIoEiRIowZM4aGDRvi5eXF3Llz+eijj3BwcKBUqVKMHDmSRo0amXpe4Fau1r8T9G8PVu40G59Ibv37vs24x24vj4qK4u233+avv/6iePHitGjRgokTJwK3nrGdOnXi6aefZty4cdjY2Jj2jY+P58knn8TJyYlp06ZRv359jEYjq1atwtXVlTZt2hTKNYuYMyXxizwkbp/P/8KFC4wePZovv/ySLVu2EBoayokTJ2jYsCH29vZcvHiRtWvXsmnTJkaMGMGLL75I/fr1TcPIjh07xg8//ICvry9OTk44OztTqlQpypcvT5EiRUhNTVVys9wXGfdUXFwc27dv59NPP6VevXoEBASQlJTEokWLuHr1Ki1btqRMmTL88ssvFC1alA8//JAmTZqYFlO9cuUKX331Ff/73/+oX79+pnVcDAYDGd+9KQCX/Lh9oof09HRCQkKoWrVqljyqy5cv8+677xIVFUWbNm1ITU1l1apVGAwGatSogZOTE6dPnyYsLIwmTZpQrFixTIHP2rVrsbOzIzo6mnbt2mFtbU3VqlVNs4uJSN4ogBF5SFhZWXHjxg1OnjzJ/v372b59O+PHj6dHjx44OzuzbNkyzp07R506dShevDg7d+7E2tqajz/+mOLFi2NlZUVKSgoRERFMmTLF9KLM6IXJkDGTjYbbSEH59/Ct999/nzlz5nDkyBG8vb15/fXXadu2Ld27d+f48eP8+OOPuLq60qBBA5KTk1m7di0uLi7UrVuXtLQ0jh49yooVK1i9ejVt2rShRYsWWXK1DAaD7mG5Z+np6Zlm9QoJCWHIkCH88ssvPPXUU7i6ugJw6NAh+vfvT1JSEuHh4UycOJG+ffvStm1bkpKSmD9/PgEBAVSoUAEPDw/Wr1/P0aNHqVixIl5eXly5coWVK1cSGxtL2bJl2bNnDxUrVsTX17cwL1/E7KnfXaSQ/Ht9lbi4OF5++WW2bt1KQEAAvXr1okuXLgA0btwYHx8f3nvvPSpXrszzzz9Pnz59mDp1KiNHjmTQoEE4Oztz5MgRli9fzqVLl5gwYYLpJXw7feiTgpJdrhZAlSpVmD9/Po6OjgQFBeHh4YHRaMTOzoMFzJ4AACAASURBVI5Ro0Zx/vx5vvnmGzp37syQIUOIiIhg3rx5hISEULJkSVJSUjh79izDhw9n9OjRhXR18qjKePZmTCQxefJkTp06Re/evencuTMlSpQw1XV0dCQ1NZUvvviCnj170qBBAwDs7OwYPXo0v/zyC0FBQVSuXJnAwEBef/11XnrpJQ4cOECdOnVISUlh586dvPXWW1SqVIlu3bpx9OhRGjVqVFiXL/JIUA+MyAOWlpaWKSE0NjYWBwcH7O3tSU5O5tixY5w5c4bBgwdTunRp08u2Ro0abNq0iX379tGsWTPq1KlD2bJl+eGHH1i8eDE///wzmzZtonTp0sycOZPatWsX8pXKoypj+FZGAL5582bCwsKoUKECTk5O+Pn5cfLkSSIiIqhSpQrVqlUzfWB0d3cnLi6OzZs34+XlRbVq1WjWrBmNGjXCYDDg6enJ448/zkcffUTLli2Bf4b5iBQEg8FAXFwc48ePZ8aMGVSuXJk33niDLl26ULZs2UzDEosWLYqHhwcbNmzAx8eHpk2bmnIR7ezsKF68OHPnzqVs2bJUqlQJf39//Pz8SEpK4vjx41y5coUXXniB7t274+HhweLFi/H09KRZs2aaeEIkH9QDI/IA3T5k4eTJk3z11VckJibStWtXmjdvTtOmTdm9ezfLli3j5s2bwK0PiRnfdI8aNYpRo0YRGRmJj48PnTp1okaNGpw5c4bY2FhKlixJYGAg8M+HPr0gpaBl3FMRERG8/fbbnD59Gjs7O4oVK0bfvn0xGAwMHTqUn376id27d9O2bdtMvTBt27blk08+Mc3YZGNjQ2BgIIGBgZk+1GXkhSlXSwpSQkICs2bNYv369YwbN47+/ftTtGjRTPdZYmIiNjY22NjY0LBhQxo1asThw4e5cOECFStWNN2j7dq1o1GjRgQHB/PEE0/g7+9P69atad26NRcuXKBEiRKm4/7444/ExMSYenj0bBa5d3oriDxAGdMgT5s2ja5du3Ls2DFcXFxMLzJPT0/atWuHl5cXCxYsMO2X8QLMeBnu27fPtM3X15fGjRvTsWNHU/Byp/UKRArCX3/9xahRo3B2duadd95h7ty59O3b17S9atWq9O3bl/Xr1/P7778DmL65Tk9PJyUlhfj4eCBzIn7GPZvRS6l7WAqao6MjTz75JL6+vuzatQsXF5dMwcvChQtp3bo1K1euBKB48eIMGTKEc+fOsXbtWhISEoB/eiJfeuklzpw5w8qVK7lx44bpON7e3lhZWXHhwgV+/fVX5s+fT9myZWnduvUDvFqRR5N6YEQesNDQUNasWcPIkSNp3rw55f6/vTsPj+l8Gzj+nRnZI2LIQiREZLMlEjuxhNae2KP2hCCqKF1US7VVxU+btqq2UCWEUBShSBAkte80tNQSS0Ii1Qgmycz7h3dOxda0tSXuz3X1yjVnm+fomZlzn+d+7qdSJeXGDsDf35+OHTsye/Zs4uLilIo1cHf+Ab1ej6Oj40OPfe/cG0I8TRs3bsTExIThw4fj4+Pz0G0GDRrEhg0bmDlzJra2tjRu3Jj09HRiY2NRq9U0a9bskceXXhfxNBi/I+vUqUOHDh2YMWMGO3bsICAggB07djB16lQuXLhAr169aNy4sbKfn58fHTt2ZMmSJQQEBFCrVi2lGl61atVo3bo1R48e5d6ZKbKzs4mKiiIxMZErV65ga2vLp59+iqur6/M4dSGKFZkHRohnSK/X069fPwAWLVqkLM/NzcXExET5cT169CgffvghaWlp9O7dm1dffZVLly4xZ84crly5QlRUFBUrVnxepyEEoaGhmJmZMWvWLOButaa0tDRu3rxJhQoV8Pb2xsTEhOjoaCZOnEjJkiVxcXHB1taW5ORkgoKC+PDDD7G0tHzOZyJeNsbv2ZSUFMaOHcuNGzfw8vIiPj6eV155hfDwcLy8vAo8WIK713hoaChNmjRh7NixlCpVSjnWvfNuGel0OhITE4mLi6NRo0Z069btWZ6mEMWaBDBCPEM5OTmMHz+ey5cvs2DBAq5du8amTZvIzMwkNTUVNzc3hg4dCkB0dDTTp08nOzubOnXqcP78efLz85kwYQLNmzd/zmciXlbGG7Z169bx1ltv0axZM9LT0/njjz+4efMmWVlZWFhYMHjwYAYPHkxubi59+/YlNTWVHj16YGVlReXKlWnatOnzPhUhWLJkCTNmzMBgMPD5559TrVo1bGxslPX3VovMy8tj/vz5TJ8+nSlTptCmTZsHUhzvn1j1/nLNQognQ1LIhHiGLC0tqV27NrNnz1ZmY7a3t8dgMGBqakpcXBw6nY6RI0fSuHFj9u7dy7Zt2/joo4+4evWqVBYTz53xhq19+/Zcu3aNLVu2UKFCBV555RUqV66Mv78/w4cPZ+3atXTo0AEnJyeGDBnCkCFDsLS0pFevXpiamqLX6zEYDJLuKJ6oGzduYGlpSYkSJZSe7YcxBuIBAQHs2rWLnTt34unpqQQv91fag7vFJtq1a0d0dDRHjx6lVatWD1y/9wYvIPMVCfG0SA+MEM+I8UmeTqfjzJkz/PTTT3h4eFC6dGn8/f1Rq9WMGzeOpKQktm/fDtytWvPuu+/SvXt3PvjgA+DBJ3xC/Ff/pZyrsUKe8S9AZGQk33//PRs3blQqLg0dOpTjx4/z+eefSyAunoqUlBRWrFiBn58fbdu2BSA9PR1LS0usra0fuV98fDzvvPMO7du35+OPPy7wHZuXl8eZM2e4dOkSAQEBaDQa0tLSCswVI4R49qRPU4h/yZgaUFjGJ3kmJiZ4eXkxcuRI2rZtS4MGDTA1NSU7O5v09HRKlSrF9evXAWjYsCEdO3YkOjqakydPAsgTa/HE6PV68vPz/9MTYuP1qNFouHHjBsnJySQkJNC+fXvKlCmjbDdy5Ej++OMP1q1bR2Zm5n9uuxD3s7OzY82aNaxYsYKzZ88yYcIEunbtyq5duwpsd//3dp06dQgODiY2NpYTJ05QokQJ9Ho9aWlp/PjjjwwbNowRI0Zw+fJlACV4yc/PfzYnJoR4gExkKcS/kJeXp5R4/aeT7N2/rU6n49dffyUuLo4dO3bw2muvKbM9m5ubY21tzf79+zlw4ACdO3eWdATxRBh7BNVqNVeuXCEhIYGbN29iMBiwsbEpdK+MwWDg4sWLLF26lG3btvHtt99iZWXFiBEjClTLK1OmDJmZmSxevJj69etLEQrxROXn52NlZYW5uTk//PADMTExpKWlERoaSkBAAFZWVsq2KpUKnU7Ht99+i4WFBRUrVsTa2prdu3dz5MgR2rVrR1JSEl988QULFiygYcOGzJ8//4FeFxnXIsTzI3koQvwLxvSC7777jt9//53SpUvTsmVLqlWrhlqtLjDw81F0Oh3Lli0jOTmZixcvcubMGQYMGEDfvn2Bv9J6fHx8ePXVV4mKiuLXX3/F3d39qZ+fKP7UajUGg4H//e9/LF68mJIlS5KZmYmTkxOffPIJdevWVcrEPi6QycnJYfXq1SxatIjSpUvTo0cPhg0b9tBtIyIicHFxISAg4GmdlnhJGa/RmJgY7ty5Q5kyZRg9ejQdOnR4YNsff/yRGTNmcP78edzc3KhZsyY+Pj6EhITw9ddf079/f06cOEGlSpVYtmyZUiZc0neFeHHIGBgh/oX9+/czduxYsrKyKF++PKdPn8bS0pIePXowcuTIQj+9njJlCmfOnMHR0ZGBAwfi7OwM/PV03HicS5cuYWJigp2d3dM+NfESiYyM5Mcff6R79+74+fmRnZ3N4sWLOXv2LGPHji30hHuZmZmcO3cOd3d3ZazBvWNihHjSHhVMJCQkcPjwYaKioujduzdjx44F7n6nwt3y9Z999hndu3enV69euLm5Kcc5efIk77//PhcuXOCdd96hS5cuBfaVHhchXhwSwAjxD+n1et544w0yMjJ488038fb2xszMjEmTJrFs2TLmzZtHo0aN/vYYarWaW7ducefOHWxtbYG7N31qtVrSxMRTZTAYSE9Pp2vXrrRs2ZJRo0ZRsmRJAJYtW8aHH35IaGgoQ4cOVZYXllzD4mm6/+HQ5s2bsbS0xNHRETc3N2V5eHg4J0+eZMKECQQGBirLz507R0ZGBt7e3lhYWDxw/OPHj1OtWjXltfS6CPFikk+lEI/wqDSwAwcOkJCQwIwZM6hXrx4AaWlpnDlzBrj7FO/vAhjjcS0sLJQfUb1eL0+sxRP1sJsv4w3g+fPnuX79Oq+//jolS5bkl19+YdKkSRw8eJDOnTsTHBxcYNzA4xiPKWWRxdNmDF42bNjApEmTuHPnDjk5OVhZWREWFka3bt3QarWMHj2akJAQ1qxZg5+fH7a2thgMBipWrPjY8VfG4MX42ZHgRYgXk3wyhbiPMfXl/uDFeJOWkZGBVqulVq1awN00sEWLFuHp6cmMGTOU5f+UpCeIJ8148/Xzzz9ja2uLq6trgdnCra2t2bp1K7/99hsLFy7E39+fWbNm4evrq6SCPW48l/EzoVKp+PPPP/9xb40Q/0ZiYiKffvoptWvXpn379uTm5rJhwwYiIyPJyclh4MCBeHl50blzZ1avXs2mTZvo3r07KpWKvLw8VCoVGo3msde2BC5CvNikCpkQ/894M2b8Qdu5cyeLFi3i4sWLWFtbU7p0aQBOnz7N2rVruXPnDmPGjOHUqVMMGzaMN998k6pVqxb6qbVRUlIS+fn5yvGF+LeMpb2NT6nj4+MJCwtj2bJlxMbGcujQIUqVKoWrqys5OTkkJCSwbt06rly5wtixYwkPD8fLywszMzPg7mBnCwsLJcXxfsZel++//56IiAgcHBzw8vJ6ZucrijdjOqJRXl4earWa+fPnc+rUKaZOnUrt2rVxd3enTZs2/Prrr6xdu5ZKlSrh6elJ7dq1iY2N5cqVK3h5eaFWq9m4cSMpKSlUrVpV0hyFKMLkka8QFMyrvn37Nh999BHh4eHExcXx0Ucf0aNHD+bOnQtAq1atcHZ2ZuHChTRr1oyYmBh69uyJvb09Go2GzMxMRo8ezYYNGx76PkZ79+6lQ4cOvP3221y5cuXZnKgo1owB+IULF8jMzOSHH37A39+fDz/8kBEjRnD69GlGjRrFgQMHcHd3JyAgAJVKRZcuXejUqRP29vaoVCpu3rzJ+vXrGT9+PPv27SvwHvdew4mJibRv356vv/6anj17/m3qpBCFYRw0b0xHvHr1KnC3VyQ/P5+jR49SpUoVZcxLXl4eAOPGjSM3N5f4+HiuXr2KtbU1w4cP59ixY7zxxhuEh4fz/vvvc+PGjedwVkKIJ0n6SIUAJbXgxx9/xNzcnMOHD/PJJ59Qs2ZNNBoN48ePZ/bs2VhaWtKrVy9CQkKYPHkyHh4e2NvbY2pqil6v5/z580RHR3Pq1Cm6deumHP/eVJtLly7xv//9jw0bNtCsWTM+/PDDf512JsT9EhMTGTFiBBUrViQ3N5cZM2bg6uoKQK1atZgwYQKTJ08mKiqK8PBw9u3bx6JFi/Dy8qJ69eqkp6eze/duVqxYQb169ZRxXkYqlYrTp08zZcoUduzYQatWrZg2bRqenp6SBin+k3vnJgLYuHEjM2fORKfT0bhxYzp37oyXlxfOzs788ssvyn7GwKZs2bKEhISwYcMGZZJJY+pYcnIyGo2GL774Qvk8CCGKLkkhEy+lh5U53rFjB2+++Sapqak4OTkxatQoypYti1arxc/PjwMHDhAfH0+nTp1o0KABBw4cYOXKlaSlpZGXl8fPP//MggULSEpKokePHrRt21Z5gnjvxGmjR48mPz+f999/n4iICCpWrCg3fuIfuz+9xvja2tqaffv2ceHCBRo2bEjXrl2VdY6OjpQqVYrFixfj5ORE/fr1cXZ25uLFi8yZM4dly5axY8cOEhISaNeuHZ9++ilarVZ5j5ycHKZNm8aYMWOwsbFhwoQJDBgwAEdHR0nHEf/ZvdfQunXr+Pjjj3F1dcXS0pLExER+/vlnevbsSXp6OnFxcbi5ueHu7k5ubq7S+3j58mXWr19P+/btsbe3B+4OzA8MDKRNmzaULl2a/Px85YGSEKJokgBGvFQMBsMjB25WqlSJs2fPkpSUhL+/Py1atECv12MwGNBqtRgMBrZu3QpAgwYNCAgIIDs7m3Xr1vHTTz9x9OhRLC0tmTZtWoHgBWDPnj0EBwdz6tQpJY3Bx8cHExOTZ3buongwjnMxXl/p6elYWVkp17SlpSUajYaNGzeiUqno3r27MqeQWq3G1taWgwcPkpKSQteuXXFxcaFdu3a4urri6+uLt7c3H3zwAUFBQWg0mgKB0po1a1i9ejVDhgzh7bffxsvLS6qOiSdGr9czefJkNm/ezPXr1/Hw8ODtt9+mV69e2NnZsXr1avLz82nZsiV79uxh69at9OjRAzMzM+UajYmJ4fLly/Tu3ZtSpUopxzZep8YiLRK8CFG0SQqZKHbuD1CMvS3G5RqNhoyMDI4dO0a5cuWws7NTBtAPGjSIzZs3c+HCBdLS0nBwcFAGjrZp04Z58+Zx/PhxsrKy0Gq1jB8/nkGDBnH79m1u3ryplOC8f+IzMzMz2rVrR2hoKJUrV5YfT/GvGZ8cnzlzhqlTp3L+/HnUajUNGzakT58+ODs7ExQUxNatW4mPj2fLli0EBgaSm5uLqakpDg4OODo6sm/fPuUaL1GixAMzlt87DsH4GQoODqZp06aUKVNGrmHxxOn1eq5evcr69etxcnJi3rx5Si9KYGAgp0+fZvbs2bRp04b+/fszceJEIiIiCA8Pp3z58hw+fJjk5GRatmyJi4vLQ99DAm4higfpgRHFjvHGas+ePTg5OSmvjX8jIyN566232LJlC4sXL2bPnj1UrVoVOzs7ypQpQ1ZWFps3b6Zq1ap4eHigVqvJy8vDwsKCXbt2kZaWRv/+/ZX3s7a2xtbWVvmhfdgTPkdHR7nxE/+ZMZBISEhg8ODBmJub4+PjA9ydF+P48eO4ublRrlw5SpUqxa5du0hJSaFbt24FbtyWLl0KQK9evQr0At4f7N//2VGr1VhaWso1LJ4KtVqNm5sbhw4d4s8//6RHjx5YWlqiVqsxNzdHq9Wyc+dOzpw5w5AhQ3B2dmb58uWsWLGC+Ph41q1bh7e3N++9956U9BaimJMARhQ7+fn5DB06lKioKBo1aoS9vb3SizJt2jRiY2Pp1asX3bt3x83NjaSkJDZv3oybmxsuLi5Uq1aNlStXcv78edzd3XFwcECtVnPu3DmioqJwcnKibdu2jxy38k+XC1FYKpWK3NxcvvjiC3Jzc/n000/p3r07QUFBSorNuXPnCA4OxtnZmdTUVDZv3kx6ejouLi7cunWLhIQEYmNjad++Pc2aNXvg+Pf+FeJZK126NDdu3CAhIQFfX188PDyU7+9SpUphYmLC/PnzqVGjBm3btqV+/frUqlULZ2dnwsLCGDlyJCVLlnzoOEchRPEhAYwoVoyz2efm5rJ7926ys7MJDAxErVaTlpbGxIkT8fPzY+zYsXh5eVGnTh3q1avH8uXLSUtLw9fXF0dHRywsLFi6dCl79uzBwcGBo0ePsn79evbu3cuwYcPw9vZ+3qcqipEDBw5w9epVHBwcgLtBuHHMyv0uXbrEV199RcuWLenatauyvFq1aqSnp7N161a0Wi3VqlWjQoUK7Nq1i127drFmzRqOHDlCbGws1apVY/To0fKUWrxwVCoVlSpVYt++fWzbto3evXsraYwajYYyZcpw+vRpNm7cSLdu3XBycqJq1arUq1ePSpUqAQ8WuBBCFD8SwIgi6XFP11QqFd7e3hw4cIA9e/ZQqVIlXF1dOXPmDAsWLCA0NBQfHx+lzKaDgwNmZmYsXryY6tWr4+npiaenJ4cPH+bYsWOcPXuWkydPcuLECUaPHk3nzp2f5amKYu7q1av07NmTX3/9lSZNmigpM2q1muvXr3Pjxg3MzMyUFLDs7Gzmzp2Ln58fdevWLTDQvly5csTFxZGbm0vz5s2xs7Pj1q1bnDlzBh8fH8aMGUPDhg0ZNWqUBC/ihWVpaYmlpSUrVqzA1NQUf39/Ja3RxsYGExMTli1bhr+/PxUrVlT20+v1BSYjFkIUX/IpF0WSMXiJjY0lLS2twHJjYBIWFoZarSYmJobbt29ja2uLhYUFqampyvbGH7qgoCDKlSvH3r17ATA1NSU8PByAFi1aMHHiRLZu3UpISAjw1wBnIf4rrVbLiBEjOHDgAElJScryyZMn07p1a0JCQhgxYgRZWVkAODk54ePjw7Fjx8jJyQFQghgPDw+8vLy4fPmyco2GhIQo82bodDqaNm1Kfn6+8jkR4kXUokULWrZsycyZM8nIyFCucYCAgADi4+MJCAgosI8ELkK8POTTLoqs/fv3M378eL777jtl2b1pN/7+/ri4uHDo0CHWrl2Lo6Mjnp6erFu3juvXryupZnD3iZ+dnR0nTpxQZnVu0KABr776KrGxscpM0I9L7RHi39BoNLRq1Yrq1avz3XffceHCBWbOnElCQgLBwcH4+vqyZ88exowZw7Vr14C7Aff+/fvZunWrcg0be2iMPTempqYYDAZKlixJSEgId+7cISoqStlWqjGJF5m5uTmhoaGYm5szZcoU4K8HVyVLlqRChQpKSXEhxMtHUshEkVW2bFn++OMPVq1aRePGjbGzs1NKzCYnJzNixAh++eUXTE1NOXfuHO3bt0ej0bB582Zu3bpFQECAchN3+/ZtZs6cSY0aNWjVqpWSoubp6cnChQsxGAz4+vpiZWUFyCBn8WRZWlqi1WpZtGgRFhYWJCUl0aVLFwYPHky7du1wcXFhzpw52NjY4Ovri4ODAydPnmTjxo2UK1cOd3d3cnJySE5OJjo6mm7dutGgQQPlOnZ3dyclJYVdu3ZRvnx5qlSpoqTbCPGisre35/fff2f16tV069btgbRHmYxSiJeXBDCiyNJoNDg6OrJp0yZSU1Np06YNFy9e5P333ycyMpKaNWsyffp0bG1tiY+PR6PR0LdvX06cOMGqVauwtrZGq9Vy7do1lixZwuHDhwkLC8Pd3V35USxdujQ6nY4lS5bg5uaGt7e3/GCKp8Le3p709HSio6PRarV89tlnyuBld3d3UlNTWbt2LXXr1sXNzY3q1auzZs0aVq5cSXJyMsnJycTExFCmTBmGDh1K2bJllZRKtVpN2bJlWbFiBTdv3qRdu3bSiyheeGq1Gh8fH8LCwpQy9UIIAaAySP+rKML0ej1z584lMjKSV199lW3btuHm5kZERAT169fHxsaG/Px8+vbty+XLl5kzZw7W1tZMnjyZn376CSsrK8qUKcOFCxfo378/o0aNKjAvBkBWVhZt27Zl6NCh9O7d+zmdqXgZHDlyhBEjRqBSqfj+++9xdnZWelEuXbpEUFAQrVu3ZtSoUWi1Wk6dOsW6devYv38/t2/fpm7duowePZoSJR4+R/H27dtp1KiRpI+JIsc4v5YQQoAEMKIYSEtLY+zYsSQlJTFy5Eg6d+6sPK0zVq7ZtGkT48aNo1mzZko+9cqVK7l8+TI3b94kODgYT09P4OEVzv7880+p2iSeOp1Ox+zZs5kzZw5ff/01zZs3B/66JmfNmsW3337Ll19+SWBgoLLfrVu30Ov1Sorj/Td7MieGEEKI4kRSyESRZ21tjUajYcuWLTRp0oRGjRoVWK9SqXBzcyMlJYU9e/ag1Wrx8vLC29ubunXr0qhRI8qWLYter3/kAH0zM7NndTriJabRaChfvjzbtm3j7Nmz1K9fH2trayX4qFatGps2bSIlJYU6depQqlQpAExMTDA1NVWu4fufVEvwIoQQojiRJGhRLDRr1ozAwEDmzZtXoEzy/WWVMzMzWbNmDTqdDkCpYGPsqZFxAeJJ+beltitUqMCAAQNISkpix44dSlU8g8GAhYUFAwcO5Pjx49y5c+eBfeUaFkII8TKQFDJRbOzdu5eIiAg6duzIBx988NBtNmzYQMOGDZUn10I8acZg+H7/JI3r+vXrvPnmm1y7do3p06fj6upaYL1Op8PU1PSJtFcIIYQoaiSFTBQb9vb2ZGVlsWzZMho1aoSDg4Oy7t5ysubm5kplJiGeFOM1ZgxStm/fzqxZszh79iz29vb/KGi2sLDA3t6emJgYNBoNfn5+BYpLaDQa8vLy5BoWQgjxUpJfP1FsmJiYEBISgpOTE1999VWBFJv7n3xLNRvxJN3bu6LT6Zg0aRIREREkJSUxbdo0QkND2bdv3z86Zq1atWjSpAl79uxRJqu816MqjQkhhBDFnaSQiWLFYDAwa9YsvvrqK9auXYu7u/vzbpJ4SeTl5bF69WpsbGyYN28e3bp1w9/fn6ysLEaNGkWlSpX44IMPcHNzK3Q6WWZmJlqt9hm0XgghhCg6JIARxU52djY6nU5u/MRT87AAJDExkcGDB1OjRg1cXFz4/PPPlXXbt29n0KBBvPHGG4SHh2NqavqPxsTk5eVJj4sQQgjx/ySFTBQ71tbWaLVapfqYEE+KwWAgPz//oYFH06ZNadeuHUePHsXOzg64O6Bfr9fTpEkTmjZtyvLlyzl8+DDw96WN7322JMGLEEII8RcJYESxJeNcxL9xf/nje0ttq1QqNBoNGRkZJCYmcurUKa5fv65sO3jwYExNTbl06RIZGRmo1WolkJ4wYQJXrlxh7dq1ZGVlFTj2w95PpVJx+vRpfv3116dynkIIIURRJQGMEELcw1jZa8+ePcBfPSXG5ZGRkbzyyiu88847dOnShYiICE6cOAGAh4cHPXr0YMeOHRw8eBC4W1wiLy+PcuXKERYWRlxcHLt37y5wbCgYuGRkZPDee+/Rrl07Dh06pMwFI4QQQggJYIQQooD8/HyGDBnC66+/zvHjxwGUAGLatGksXbqUPn368PHHHxMREUFqairDhw8nKSkJgCFDhmBlZcXy5cu5ePFiG2U2QAAAD61JREFUgWO/88475OTksHDhQjIzMwusM066OnfuXF555RUOHTrEJ598Qrt27SSFTAghhLiHzAMjhHhpGdPC7n2t0WjIzc1l9+7dZGdnExgYiFqtJi0tjYkTJ+Ln58fYsWPx8vKiTp061KtXj+XLl5OWloavry+Ojo6YmZmxcOFC3Nzc8Pb2pkSJEuh0OjQaDVWrVqV58+a4ubkVaEtCQgJDhw5lx44d9OvXj3HjxlG3bt0C878IIYQQQnpghBAvIWO6llqtfmh6VqdOnahVqxaJiYls2bIFgLS0NNLT02natCkWFhbk5+ej1+upVq0ar7/+Otu3b1fSxrp370716tVZvny5MobFGIgEBgbi7++vtCEzM5PXXnuN4cOHU6NGDaKjoxk2bBj29vZP/d9BCCGEKIokgBFCvHSMvS7ffPMNHTp0YOfOndy6dQu1Wq1MGhkaGopKpSImJobbt29ja2uLhYUFqampynGM42KCgoIoV64ce/fuBe4GKyNHjuTYsWNs2LCB3NzcB6qOGV9fv34dFxcXvvnmGyZNmoSXl5dyXCGEEEI8SFLIhBAvpX379jFhwgSuXbvG7t27+e233/D398fa2hqA8uXLc/HiRbZv307ZsmWpXbs2O3bs4NChQwQFBWFpaUlubi4ajQa1Ws3GjRtJS0ujS5cuqNVqXFxcMDc3p1OnTtja2j6yHaVLl6Zp06ZUqVJFKucJIYQQhSCP+YQQL6Xq1avTu3dvANzd3YmLi6N///7ExcUp2/Tv35+yZcsqvTAdO3YkIyODmTNnAn+lheXm5nLp0iUqV66MRqNRSjEPHDiQ8uXLP7RcspFKpcLU1PRpnaYQQghR7EgPjBDipVSiRAm0Wi1JSUm4urryxhtvcOLECRYsWMC1a9fw8PDAycmJvLw8NmzYQIkSJejbty8nTpxg1apVyoSp165dY8mSJRw+fJiwsDDc3d0fKI/8d5NWCiGEEKLwVIbHPRoUQohiTK/XM3fuXCIjI4mKiqJu3bpERkYSGxuLVqvl3XffpWXLlvTp04dLly4xa9YsrK2tmTJlCj/99BNWVlaUKVOGCxcu0L9/f0aNGiVVw4QQQoinTAIYIcRLLT09nYiICAwGAzExMZiYmHDs2DHGjRvHqVOnGDZsGCYmJixduhQ/Pz+mTZsGwMqVK7l8+TI3b94kODgYT09PQHpchBBCiKdNUsiEEC81KysrrK2tiYmJoVSpUtSqVQsHBweaNGlCmTJlmDVrFmlpaWRnZ5OZmYmtrS2enp54e3tTt25dGjVqRNmyZdHr9RgMBqkgJoQQQjxlEsAIIV565cuX5/fffycuLo5XXnmFUqVKYWNjg7+/P5UrVyYtLY3jx49z48YNcnJyaNu2LRqNRult0ev1qNVq6XkRQgghngFJIRNCCGDv3r1ERETQsWNH3n///QLBSF5eHlOnTkWv1zNs2LDHlkUWQgghxNMlAYwQQnC3FPK0adOIiYkhOjqamjVrAneDlxIlSih/AfLz82XOFiGEEOI5kWRtIYTg7pwuISEhODk58dVXX3Hnzh0AJWgx/tXr9RK8CCGEEM+RBDBCCPH/XF1dCQoKIikpifPnzz90GxmkL4QQQjxfkkImhBD3yM7ORqfTodVqn3dThBBCCPEQEsAIIcRDyDgXIYQQ4sUkAYwQQgghhBCiyJBkbiGEEEIIIUSRIQGMEEIIIYQQosiQAEYIIYQQQghRZEgAI4QQQgghhCgyJIARQgghhBBCFBkSwAghhHjhpaam4unpyfTp0x+77EUyZswYPD09n/hxp0+fjqenJ6mpqU/82EIIURSUeN4NEEII8WLavXs3ffv2LbDM0tISV1dXgoOD6d27d5GdKyc1NZVVq1bRsmVLvL29n3dzuHPnDrGxsaxevZrU1FRu3bqFVqvFzc2NevXqMWjQoOfdRCGEeGFIACOEEOKx2rdvT5MmTTAYDKSnp7Nq1SomTZrEb7/9xieffPLc2uXk5MSRI0f+VRB18eJFvvnmG5ycnJ57AJOXl0e/fv04ePAgTZs2pUOHDlhaWpKamsqRI0eYM2dOgQAmIiKCQYMGYWpq+hxbLYQQz48EMEIIIR6ratWqBAcHK6979uxJmzZtWL58OSNGjKBs2bIP3S87Oxtra+un1i6VSoWZmdlTO/6zkpCQwMGDB+nXrx9jx459YP3Vq1cLvC5RogQlSsjPtxDi5SVjYIQQQvwj1tbW1KpVC4PBwIULFwAIDAykT58+nDhxggEDBuDv709QUJCyz9mzZ3n77bdp3Lgx1atXJzAwkClTppCTk/PA8fft20ePHj2oWbMmDRs25OOPP37odo8bA7Nx40b69OlD7dq18fHxoVWrVkycOBGdTsfKlSuV1Lj33nsPT09PPD096dOnj7K/wWBgyZIldO7cGR8fH2rVqkWfPn3YtWvXA+91584dpkyZQuPGjalZsyZdu3Zl586dhf73PHfuHAANGjR46Ho7O7sCrx81BiYlJYWwsDB8fX2pV68e7777LpmZmXh6ejJmzBhlu3v/3bZu3UqXLl2oUaMGjRs3ZsqUKeTl5RW67UII8TzIIxwhhBD/iMFgUG66S5curSy/dOkS/fr1o3Xr1rz66qtK0HHs2DH69euHjY0NISEhODg4kJKSwqJFizh48CCLFi3CxMQEgMOHDxMaGoqVlRXh4eGULFmS9evX8+677xa6fZGRkcyaNYsqVarQv39/7OzsOH/+PJs2bWL48OHUqVOHIUOGMGvWLEJCQvD39wco0JP09ttvExcXR6tWrejcuTM6nY61a9cSFhbG9OnTadGihbLtqFGjiI+Pp3nz5gQEBHD+/HneeOMNKlSoUKj2Ojs7A7BmzRoaNGiAubl5oc/V6OzZs/Tq1Qu9Xk+fPn1wcHAgMTGRgQMHPnKfxMRElixZQo8ePejSpQsJCQnMnz+fUqVKMWTIkH/cBiGEeFYkgBFCCPFYt27dIjMzE4D09HSio6NJSUnB19eXSpUqKdulpqYyceJEunXrVmD/sWPHYmdnx4oVKwqklDVo0IBhw4axdu1aOnfuDMBnn32GwWAgJiYGV1dX4G7KWs+ePQvV1iNHjjBr1izq1avH3LlzC6SYvfXWWwDY2NjQsGFDZs2aha+vb4H0OIDNmzezdu1aPv74Y0JCQpTlffv2pXv37nz66acEBgaiUqnYuXMn8fHxdOrUicmTJyvb1qlTh9dff71QbW7RogXVqlVj/fr17NixA39/f2rUqIGfnx916tRRgrvHiYyMJDs7myVLligBWe/evRk5ciTHjx9/6D6//fYb69atUwKt1157jQ4dOhAdHS0BjBDihSYpZEIIIR5r+vTpNGjQgAYNGhAcHMwPP/xAYGAgM2bMKLCdra2tEogYnTx5kpMnT9K+fXt0Oh2ZmZnKf/7+/lhaWpKUlARARkYGBw8eJDAwUAleAExNTenfv3+h2rpmzRoARo8e/cD4GJVKhUqlKtQxrKysaNmyZYH23rhxg8DAQC5evMjZs2cBiI+PB2DAgAEFjtGyZcsC5/A4pqamLFq0iJEjR1K+fHkSExOZPn06oaGhNG3aVDmnR8nPz2f79u3UrFlTCV6MwsLCHrlfixYtCvQSqVQq6tWrx9WrV7l582ah2i6EEM+D9MAIIYR4rJCQEFq3bo1KpcLCwoJKlSpha2v7wHbOzs4PVAQ7ffo0cDcIetR8LdeuXQNQxtNUrlz5gW2qVKlSqLaeO3cOlUqFl5dXobZ/mNOnT3Pz5k0aNmz4yG0yMjJwdXXlwoULqNXqAj1RRm5ubvz++++Fek8rKysiIiKIiIggOzubI0eOEB8fT2xsLO+++y5OTk4PBCdGmZmZ5OTkPDRgelwQZUxdu5fx/2tWVhZWVlaFarsQQjxrEsAIIYR4rIoVKz72Zt7IwsLikevCwsIICAh46DobG5t/3baHKWxPy6MYDAa0Wi2ff/75I7dxd3f/18f/O9bW1jRs2JCGDRvi5eXFuHHjWLly5SMDmH/rceWnDQbDE30vIYR4kiSAEUII8dRUrFgRALVa/bdBkDGd6cyZMw+s++233wr1fpUqVWL79u2kpKRQs2bNR273uACnYsWKnD17Fh8fn7/thXB2dkav13P27NkHghpj79N/4ePjA0BaWtojt9FqtVhaWj60t6ewPUBCCFGUyBgYIYQQT03VqlXx8PBg6dKlSorYvfLy8sjKygLuVgHz9fVly5YtBW68dTodCxYsKNT7dejQAYAvvvgCnU73wHpjz4KlpSUAf/zxxwPbdOzYEb1ezxdffPHQ9zCmvAFKNbJ58+YV2CY+Pr7QwcMvv/xCenr6Q9cZx9g8LoVOo9EQEBDAkSNH2L9/f4F18+fPL1QbhBCiKJEeGCGEEE+NSqVi6tSp9OvXj6CgILp06UKVKlW4ffs2586dY/PmzYwaNUoZ/D9mzBj69OnDa6+9Rq9evZQyyvn5+YV6v5o1axIeHs7cuXPp3Lkzbdq0wc7OjtTUVDZu3Mjy5cuxsbGhSpUqWFlZsWTJEszNzbGxsUGr1dKgQQNat25N586diY6O5vjx4zRv3pzSpUtz5coVDh06xLlz50hISAAgICCA5s2bs2rVKrKysggICODChQssW7YMDw8PTp069bdtTk5OJjIykkaNGuHn50fZsmX5888/2bNnD1u2bMHOzo7Q0NDHHmPkyJHs3LmTgQMH0rt3bxwdHdm2bZtSPe6/pNQJIcSLRgIYIYQQT5W3tzerVq1i9uzZbNmyhaVLl2JlZYWTkxOdOnUqMIFjrVq1+O677/j888+ZM2cOJUuWpFWrVkqJ38J466238PLyIjo6mqioKAwGA46OjjRp0kSZY8Xc3JzIyEi+/PJLJk2ahE6no27dukpbPvvsM+rVq0dsbCyzZ88mNzcXOzs7qlatyujRowu835dffsmXX37J2rVrSU5OxsPDg+nTp7Nu3bpCBTCtWrVCp9ORnJzMkiVLyMjIoESJEjg5OdG/f38GDBjwwGSW96tcuTKLFy9mypQpLFy4EDMzM5o1a8b48eNp2bLlAxXZhBCiKFMZZKSeEEIIUSwdO3aMLl26MHr0aAYNGvS8myOEEE+EjIERQgghioHbt28XeG0wGIiKigIoVBU5IYQoKiSFTAghhCgGgoODqV+/Ph4eHty6dYutW7eyb98+2rZtS/Xq1Z9384QQ4omRFDIhhBCiGJg6dSpbt27lypUr5OXlUaFCBTp06EB4eDgmJibPu3lCCPHESAAjhBBCCCGEKDJkDIwQQgghhBCiyJAARgghhBBCCFFkSAAjhBBCCCGEKDIkgBFCCCGEEEIUGRLACCGEEEIIIYoMCWCEEEIIIYQQRcb/ASDQm4ifIezZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fOJyH_c3ON2"
      },
      "source": [
        "## Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PXqG-VpurVS"
      },
      "source": [
        "torch.save(model,\"./drive/MyDrive/Codalab/HopeSpeech Detection For Equality,DiversityAnd Inclusion/Models/XLMR_English\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9M_91z1yj0F"
      },
      "source": [
        "## Loading the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42ObCUU1QcsJ"
      },
      "source": [
        "model = torch.load('./drive/MyDrive/HopeSpeech Detection For Equality,DiversityAnd Inclusion/Models/XLMRobertaForSequenceClassification_Mutlti_English_v2',map_location=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAaHpZdIzsnT",
        "outputId": "553647dc-8e70-4141-c6a1-bac1ec4aa372"
      },
      "source": [
        "model.cuda()"
      ],
      "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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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",
              "          )\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": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_crczjW6V-QK"
      },
      "source": [
        "## Making Predictions on Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lETtjTatLo91"
      },
      "source": [
        "test_df_path = join(dataset_dir,'english_hope_test.csv')\r\n",
        "test_df = pd.read_csv(test_df_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_83fOUwqn0w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "7fa22119-6f74-4833-e1fa-cbbd74bc1732"
      },
      "source": [
        "test_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What do you mean by the word sniped?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I love this video!! I’m bisexual and it’s just...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ya the irony but then i don't want to come off...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A PERSON'S CHARACTER MATTERS. PERIOD!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@Blaster of Gasters</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2841</th>\n",
              "      <td>+Ashrenneemakeup I think it's all a deliberate...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>Sheriff David Clarke. This guy is amazing.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2843</th>\n",
              "      <td>Abandorn Hope Situation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2844</th>\n",
              "      <td>Sheriff Clarke you are a person of such strong...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2845</th>\n",
              "      <td>Sanders has no room to talk. If there's one pe...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2846 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text\n",
              "0                  What do you mean by the word sniped?\n",
              "1     I love this video!! I’m bisexual and it’s just...\n",
              "2     ya the irony but then i don't want to come off...\n",
              "3                A PERSON'S CHARACTER MATTERS. PERIOD!!\n",
              "4                                   @Blaster of Gasters\n",
              "...                                                 ...\n",
              "2841  +Ashrenneemakeup I think it's all a deliberate...\n",
              "2842         Sheriff David Clarke. This guy is amazing.\n",
              "2843                            Abandorn Hope Situation\n",
              "2844  Sheriff Clarke you are a person of such strong...\n",
              "2845  Sanders has no room to talk. If there's one pe...\n",
              "\n",
              "[2846 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjAQaP_na7HN"
      },
      "source": [
        "## Loading `test_sentences`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnnscvKOjl6b"
      },
      "source": [
        "test_sentences = test_df['text'].values \r\n",
        "test_ids = test_df.index.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glrG0ZI4imGN",
        "outputId": "1423edcc-92cb-4602-d9eb-2380866632e9"
      },
      "source": [
        "(py_inputs, py_attn_masks,py_ids) = make_smart_batches_on_test(test_sentences,test_ids, 16,tokenizer,max_input_length = 400)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating Smart Batches from 2,846 examples with batch size 16...\n",
            "\n",
            "Tokenizing 2,846 samples...\n",
            "  Tokenized 0 samples.\n",
            "  Tokenized 300 samples.\n",
            "  Tokenized 600 samples.\n",
            "  Tokenized 900 samples.\n",
            "  Tokenized 1,200 samples.\n",
            "  Tokenized 1,500 samples.\n",
            "  Tokenized 1,800 samples.\n",
            "  Tokenized 2,100 samples.\n",
            "  Tokenized 2,400 samples.\n",
            "  Tokenized 2,700 samples.\n",
            "DONE.\n",
            "     2,846 samples\n",
            "\n",
            "     2,846 samples after sorting\n",
            "\n",
            "Creating batches of size 16...\n",
            "\n",
            "  DONE - Selected 178 batches.\n",
            "\n",
            "Padding out sequences within each batch...\n",
            "  DONE.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjsjqQpRQcsV"
      },
      "source": [
        "## Evaluating accuracy over test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwkx-30AQcsV",
        "outputId": "a684d187-2d9a-4c3f-be59-aeddab8f7f83"
      },
      "source": [
        "y_ids,y_preds  = get_predictions_test(py_inputs, py_attn_masks,py_ids)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicting labels for 178 test batches...\n",
            "  Batch      20  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      40  of      178.    Elapsed: 0:00:01.  Remaining: 0:00:04\n",
            "  Batch      60  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch      80  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:03\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     140  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:01\n",
            "  Batch     160  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pDM-cnDHQcsW",
        "outputId": "6d5a787b-efc3-4753-87d8-faba3a7a52d3"
      },
      "source": [
        "print(y_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 ... 0 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWWWjz7eUPc7",
        "outputId": "c301635a-b634-43b7-a3ed-f8bfa815c50f"
      },
      "source": [
        "print(y_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2066 2527 2783 ...  733  738  105]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2ybbsAoQcsY",
        "outputId": "4b45ba10-a0ce-4a45-ebd4-ae777cdd87f1"
      },
      "source": [
        "le.inverse_transform(y_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Non_hope_speech', 'Non_hope_speech', 'Non_hope_speech', ...,\n",
              "       'Hope_speech', 'Non_hope_speech', 'Non_hope_speech'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "kSMb6sCLQcsZ",
        "outputId": "b75b60ee-453d-4201-b666-4d98138867bc"
      },
      "source": [
        "sns.countplot(y =le.inverse_transform(y_preds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faddb4289e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KXOXwH9U2MT",
        "outputId": "487b22b7-d237-4005-e7f1-da1e2fd68541"
      },
      "source": [
        "len(y_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2846"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-fkBgOxU4bX",
        "outputId": "4d5b21c1-76b3-407b-d526-16ccd00ce4c0"
      },
      "source": [
        "len(le.inverse_transform(y_preds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2846"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWPDqogJrjIZ"
      },
      "source": [
        "## Creating Submission DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4Hb7RD1yHol"
      },
      "source": [
        "df_new = pd.DataFrame({\r\n",
        "    'id':y_ids,\r\n",
        "    'label':le.inverse_transform(y_preds)\r\n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twQAkVzcn4Lg"
      },
      "source": [
        "test_df['id'] = test_df.index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "qov36eWmqawv",
        "outputId": "5b2f322d-6703-4ff3-93a7-7b11caac6619"
      },
      "source": [
        "df_new"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2066</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2527</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2783</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2792</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2844</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2841</th>\n",
              "      <td>695</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>719</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2843</th>\n",
              "      <td>733</td>\n",
              "      <td>Hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2844</th>\n",
              "      <td>738</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2845</th>\n",
              "      <td>105</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2846 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id            label\n",
              "0     2066  Non_hope_speech\n",
              "1     2527  Non_hope_speech\n",
              "2     2783  Non_hope_speech\n",
              "3     2792      Hope_speech\n",
              "4     2844  Non_hope_speech\n",
              "...    ...              ...\n",
              "2841   695      Hope_speech\n",
              "2842   719  Non_hope_speech\n",
              "2843   733      Hope_speech\n",
              "2844   738  Non_hope_speech\n",
              "2845   105  Non_hope_speech\n",
              "\n",
              "[2846 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
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        "id": "z0CEApHmqdLr",
        "outputId": "3c8f7d25-4baf-4546-c50a-5db422357759"
      },
      "source": [
        "test_df"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <td>2841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>Sheriff David Clarke. This guy is amazing.</td>\n",
              "      <td>2842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2843</th>\n",
              "      <td>Abandorn Hope Situation</td>\n",
              "      <td>2843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2844</th>\n",
              "      <td>Sheriff Clarke you are a person of such strong...</td>\n",
              "      <td>2844</td>\n",
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              "      <th>2845</th>\n",
              "      <td>Sanders has no room to talk. If there's one pe...</td>\n",
              "      <td>2845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2846 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text    id\n",
              "0                  What do you mean by the word sniped?     0\n",
              "1     I love this video!! I’m bisexual and it’s just...     1\n",
              "2     ya the irony but then i don't want to come off...     2\n",
              "3                A PERSON'S CHARACTER MATTERS. PERIOD!!     3\n",
              "4                                   @Blaster of Gasters     4\n",
              "...                                                 ...   ...\n",
              "2841  +Ashrenneemakeup I think it's all a deliberate...  2841\n",
              "2842         Sheriff David Clarke. This guy is amazing.  2842\n",
              "2843                            Abandorn Hope Situation  2843\n",
              "2844  Sheriff Clarke you are a person of such strong...  2844\n",
              "2845  Sanders has no room to talk. If there's one pe...  2845\n",
              "\n",
              "[2846 rows x 2 columns]"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "vxReWWmxn7Hj",
        "outputId": "2bb5bc11-b025-47f3-ed5f-e8732d1926b6"
      },
      "source": [
        "df_f = pd.merge(test_df,df_new,on = 'id')\r\n",
        "df_f"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
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              "      <th>...</th>\n",
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              "      <th>2842</th>\n",
              "      <td>Sheriff David Clarke. This guy is amazing.</td>\n",
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              "      <td>2843</td>\n",
              "      <td>Non_hope_speech</td>\n",
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              "    <tr>\n",
              "      <th>2844</th>\n",
              "      <td>Sheriff Clarke you are a person of such strong...</td>\n",
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              "      <td>Non_hope_speech</td>\n",
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              "      <td>Non_hope_speech</td>\n",
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              "<p>2846 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text    id            label\n",
              "0                  What do you mean by the word sniped?     0  Non_hope_speech\n",
              "1     I love this video!! I’m bisexual and it’s just...     1      Hope_speech\n",
              "2     ya the irony but then i don't want to come off...     2  Non_hope_speech\n",
              "3                A PERSON'S CHARACTER MATTERS. PERIOD!!     3  Non_hope_speech\n",
              "4                                   @Blaster of Gasters     4  Non_hope_speech\n",
              "...                                                 ...   ...              ...\n",
              "2841  +Ashrenneemakeup I think it's all a deliberate...  2841  Non_hope_speech\n",
              "2842         Sheriff David Clarke. This guy is amazing.  2842  Non_hope_speech\n",
              "2843                            Abandorn Hope Situation  2843  Non_hope_speech\n",
              "2844  Sheriff Clarke you are a person of such strong...  2844  Non_hope_speech\n",
              "2845  Sanders has no room to talk. If there's one pe...  2845  Non_hope_speech\n",
              "\n",
              "[2846 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "1kuASymYn9aV",
        "outputId": "4e89a735-81ef-44cd-cf6e-a5e396fa5ee9"
      },
      "source": [
        "df_f = df_f[['id','text','label']]\r\n",
        "df_f"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2841</th>\n",
              "      <td>2841</td>\n",
              "      <td>+Ashrenneemakeup I think it's all a deliberate...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>2842</td>\n",
              "      <td>Sheriff David Clarke. This guy is amazing.</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2843</th>\n",
              "      <td>2843</td>\n",
              "      <td>Abandorn Hope Situation</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2844</th>\n",
              "      <td>2844</td>\n",
              "      <td>Sheriff Clarke you are a person of such strong...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2845</th>\n",
              "      <td>2845</td>\n",
              "      <td>Sanders has no room to talk. If there's one pe...</td>\n",
              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2846 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                                               text            label\n",
              "0        0               What do you mean by the word sniped?  Non_hope_speech\n",
              "1        1  I love this video!! I’m bisexual and it’s just...      Hope_speech\n",
              "2        2  ya the irony but then i don't want to come off...  Non_hope_speech\n",
              "3        3             A PERSON'S CHARACTER MATTERS. PERIOD!!  Non_hope_speech\n",
              "4        4                                @Blaster of Gasters  Non_hope_speech\n",
              "...    ...                                                ...              ...\n",
              "2841  2841  +Ashrenneemakeup I think it's all a deliberate...  Non_hope_speech\n",
              "2842  2842         Sheriff David Clarke. This guy is amazing.  Non_hope_speech\n",
              "2843  2843                            Abandorn Hope Situation  Non_hope_speech\n",
              "2844  2844  Sheriff Clarke you are a person of such strong...  Non_hope_speech\n",
              "2845  2845  Sanders has no room to talk. If there's one pe...  Non_hope_speech\n",
              "\n",
              "[2846 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3FtBVTVroJi"
      },
      "source": [
        "## Saving Submission DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzoT0p9rqrO-"
      },
      "source": [
        "path = './drive/MyDrive/Codalab/HopeSpeech Detection For Equality,DiversityAnd Inclusion/SubmissionFiles'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZmgZFM4qi7F"
      },
      "source": [
        "df_f.to_csv(join(path,'xlmr_english.csv'),index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jW9g_qgMv7mq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}