HopeSpeechEDI_BERT_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": "51b0c80e-fdc7-406e-8d33-5c9e41168311"
      },
      "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 K80\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SokVowWo3cc",
        "outputId": "07fa0242-6102-4325-9df9-adb6352c6ad0"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Feb  5 14:23:19 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 K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P8    31W / 149W |     11MiB / 11441MiB |      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": {
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        },
        "id": "DbDuxKy0a9Zl",
        "outputId": "63759b07-c2c1-4abe-bb53-b1871b958d5b"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
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            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
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            "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",
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            "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=251a804bab06dfe60a53739759bf9852cd604f58be794b0efd63b4b0f1744e3b\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, 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": "3e35e0b0-2f44-4fad-900f-cd2d1e0e192b"
      },
      "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": "5e259e4d-2068-460e-a646-e7e6e615771a"
      },
      "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": "6bada25e-7cf4-45de-ff09-c38e6c39870b"
      },
      "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": "69af6f97-fc9c-4645-ffe6-11437568c0d2"
      },
      "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": "916ec2b5-01d7-4324-f85f-591061b006ae"
      },
      "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": "3b3bcb6a-f966-4877-f250-d0971622c437"
      },
      "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": 14
        }
      ]
    },
    {
      "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": "89548ad9-6edf-4023-e4d1-5a20300a7136"
      },
      "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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AypCmtBcDV6v",
        "outputId": "fba85c53-b340-4b57-8eff-da4c437aee17"
      },
      "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": 16
        }
      ]
    },
    {
      "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": "c52e06e9-4ce0-4415-dd3c-194ae0555682"
      },
      "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": 18
        }
      ]
    },
    {
      "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": "36539a29-b937-49e2-856f-2cc00e182f5a"
      },
      "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",
              "    }\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",
              "      <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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkt73boTL0NO",
        "outputId": "c196f98a-30b8-4397-9c7a-ff490b070485"
      },
      "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": 21
        }
      ]
    },
    {
      "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": "73eafb82-f26a-4ff3-d8b6-1ebda96d5978"
      },
      "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": 23
        }
      ]
    },
    {
      "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": {
        "id": "WL4tP_YkI1mY"
      },
      "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": []
    },
    {
      "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": "xo1_bDriha4q"
      },
      "source": [
        "### 4) Make Smart Batches (On Test)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hC-c82iuRlv2"
      },
      "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": "RfXvAn47Rlv5"
      },
      "source": [
        "### 5) Function for calculating accuracy per class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3usMdEvRlv7"
      },
      "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": "aF5EckowRlv9"
      },
      "source": [
        "### 6) Function for plotting training history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVPwVOGxRlv-"
      },
      "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": "EWrNYpQ_Rlv_"
      },
      "source": [
        "### 7) Function to check accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IudqmF2tRlwA"
      },
      "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": "z4NcJu-eRlwB"
      },
      "source": [
        "### 8) Function to evaluate model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znBBqCPlRlwC"
      },
      "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": "kiX8z2qwRlwD"
      },
      "source": [
        "### 9) Function for making predictions on our test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15KDyc3xRlwE"
      },
      "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": "aJEMLZiZRlwF"
      },
      "source": [
        "### 10) Function for making predictions on test dataset(without labels)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSmN7uOaRlwG"
      },
      "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": "BG6CAheNRlwK"
      },
      "source": [
        "### 11) Confusion Matrix Helper Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDFOg-jKRlwL"
      },
      "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": [
            "d07a75e6232944818520e1139cb88117",
            "387649749b8f47a18827e5b9057daf3d",
            "2914ab76e9324cff883761cbcb010226",
            "da1de4468627490ba87ba480609c8913",
            "9702dda1b1204e64a32ee6663f4c19b6",
            "af2990aa1fbf4b91a879980376f26a37",
            "85c5123c01bc4e3e9bb470dadc1096cc",
            "7a22ace44967461692369ca9b21b8c60",
            "48f179865d674226aea3077035e19350",
            "c91f3840d9e248898bb2ea78e61d4423",
            "479b5f1c7df14d7f931f9e2d4e10ee21",
            "27d9d618105c4dc1bbdee8f8d755289d",
            "90955b182a7a4fe9b8885a9efbf9d42b",
            "758748c225e548f893acd89445bb7eea",
            "29e43e6062e24617812bea727350465e",
            "4cb64efed0c0408d9b377a93ec3be0bb",
            "9d474d44954f44e7bd88ccf07519f602",
            "509cdb6b4e224f4cbfa839cad978efba",
            "8d6aad90048e4120a0c41e2abfa77292",
            "12987af6ef2746cb8ee2968b6c1adae5",
            "14775cd89465470386275c918925a15c",
            "474a0f68e44d426eb0b61a9e1264324e",
            "1c5206ffcd1b49078b70fd559fe80810",
            "ecf269cb98a9482fbe3f5560bfed4f98"
          ]
        },
        "id": "fIlwEvcMb4pb",
        "outputId": "8dbbb528-c272-469b-a2ac-77f4da8c4137"
      },
      "source": [
        "model_name = 'bert-base-cased'\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 bert-base-cased tokenizer...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d07a75e6232944818520e1139cb88117",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, 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": "48f179865d674226aea3077035e19350",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d474d44954f44e7bd88ccf07519f602",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435797.0, style=ProgressStyle(descripti…"
            ]
          },
          "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": "a445fded-60d4-4d69-b456-cf23756bb5ba"
      },
      "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": "7650dbc8-eae0-43c2-a4b4-1d0e790bd722"
      },
      "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": "c473575b-97b5-4dae-fe41-a95c26faea4e"
      },
      "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": "fd39062f-f473-451c-d51d-89a70151e752"
      },
      "source": [
        "train_samples[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[([101, 4514, 1128, 102], 1),\n",
              " ([101, 2508, 1315, 102], 1),\n",
              " ([101, 8835, 3669, 102], 1),\n",
              " ([101, 8835, 3669, 102], 1),\n",
              " ([101, 1422, 7670, 102], 0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBxJhEieetFh",
        "outputId": "f8908af7-1157-4072-c9e1-e7c6c4e41535"
      },
      "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: 400\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": "04b1ac22-eacd-4352-c437-830a68cb85fa"
      },
      "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": "00e8a474-2b4d-438f-e354-1742a07c7b20"
      },
      "source": [
        "train_samples[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([101, 4514, 1128, 102], 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": "ba35bde2-32c4-41dd-e939-188c900ac540"
      },
      "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": "04b1d0b4-4214-44f4-b8ab-7c547b5b916d"
      },
      "source": [
        "print(batch_ordered_sentences[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[101, 1731, 1164, 1178, 2131, 2491, 2187, 119, 183, 1942, 11220, 112, 188, 1184, 146, 1341, 1133, 146, 1274, 1204, 1301, 1213, 2157, 1122, 1272, 1122, 1156, 1660, 7682, 170, 2213, 1271, 119, 3766, 3349, 2490, 1106, 7697, 1140, 119, 146, 1209, 1309, 2437, 1725, 2256, 2010, 112, 189, 119, 102], [101, 146, 22751, 24597, 1942, 142, 17145, 2069, 14731, 2069, 3663, 119, 146, 139, 27073, 2249, 15531, 3663, 15740, 7462, 10722, 9272, 123, 141, 1592, 3663, 1708, 119, 19141, 8784, 2249, 145, 26390, 2036, 23096, 3663, 138, 11840, 23354, 119, 9686, 22751, 1708, 7462, 145, 12426, 17145, 3663, 12342, 20174, 102], [101, 1188, 1108, 1304, 2418, 1111, 1155, 2117, 139, 20376, 2316, 1107, 1103, 1244, 2325, 9708, 106, 146, 3683, 2170, 4038, 1180, 1202, 1142, 1106, 24026, 1104, 1393, 16752, 6732, 14604, 1566, 2803, 1105, 18848, 185, 11014, 11761, 2316, 1150, 4415, 1106, 1646, 9401, 1105, 1103, 3608, 23425, 5820, 106, 102], [101, 1681, 7949, 8917, 1103, 3062, 1272, 1119, 5302, 1122, 1148, 9332, 11236, 119, 1124, 3520, 1115, 1602, 7983, 4583, 2463, 1110, 1103, 173, 6834, 26420, 1348, 2754, 1115, 7462, 3663, 1687, 1105, 1679, 12924, 20043, 119, 1409, 1251, 1104, 1343, 23609, 15074, 8673, 1541, 2475, 1115, 1602, 2491, 14361, 102], [101, 137, 3627, 138, 13830, 3161, 1874, 1185, 1187, 1107, 1103, 16516, 2165, 1674, 1122, 1474, 170, 1423, 1159, 1195, 1132, 1136, 1106, 3942, 119, 1122, 1867, 1195, 1132, 1136, 1106, 2496, 1113, 1115, 21261, 1226, 1121, 20850, 1103, 1301, 20080, 1513, 1105, 16252, 1111, 1103, 11850, 9268, 3960, 119, 102], [101, 1272, 1122, 1108, 1189, 1167, 1190, 1620, 1201, 2403, 1106, 8294, 1117, 6157, 1105, 1103, 2860, 1119, 1814, 1106, 1103, 1583, 119, 23425, 1616, 1120, 1115, 1159, 1108, 2732, 1105, 1167, 16244, 1111, 2201, 119, 6682, 2422, 1115, 9401, 1108, 16440, 1234, 119, 1636, 11739, 11484, 1366, 1103, 1763, 102], [101, 146, 112, 182, 1489, 1105, 1107, 1139, 1278, 146, 112, 182, 1107, 170, 1957, 1705, 1111, 23676, 15577, 1651, 119, 1284, 1138, 1659, 1651, 1107, 1103, 1705, 1105, 127, 1104, 1366, 1132, 2636, 119, 1262, 4208, 1103, 3287, 1202, 1341, 1115, 1152, 1132, 1177, 1277, 1618, 1190, 1366, 119, 102], [101, 1398, 1142, 1202, 1110, 18699, 1602, 1105, 1653, 1234, 1256, 1167, 119, 1284, 1138, 1106, 1440, 2894, 2241, 2942, 119, 14903, 2217, 1103, 7070, 1206, 1234, 119, 1332, 1195, 1202, 1115, 1175, 1209, 1129, 1185, 1602, 1661, 1137, 1653, 1661, 119, 1135, 1209, 1129, 1234, 787, 188, 1661, 119, 102], [101, 1109, 2255, 1725, 1122, 112, 188, 6241, 1106, 1474, 1155, 2491, 2187, 1110, 1103, 1842, 11507, 1104, 1103, 1583, 1132, 7963, 1106, 4252, 2083, 17379, 1103, 1653, 1416, 119, 1337, 1110, 1103, 1842, 2255, 1111, 1103, 17606, 2702, 2530, 119, 1249, 1111, 1103, 10116, 1329, 1104, 2049, 1118, 2021, 102], [101, 1185, 1155, 2491, 1138, 1579, 14361, 118, 1122, 1108, 1198, 1887, 2305, 119, 171, 13505, 6616, 1152, 1132, 1217, 1841, 1167, 119, 1457, 9971, 1137, 9193, 1274, 1204, 1619, 1115, 1120, 1155, 119, 1247, 1116, 1199, 1168, 15812, 23992, 1165, 1128, 1838, 1106, 1440, 1146, 1103, 188, 19756, 1116, 102], [101, 141, 17444, 23313, 9741, 15779, 9741, 15678, 22680, 4514, 1128, 1177, 1277, 1111, 1115, 1869, 106, 106, 106, 106, 2353, 7310, 146, 787, 182, 1612, 146, 1180, 1309, 8658, 1122, 1133, 1122, 787, 188, 1541, 170, 1632, 22055, 119, 1153, 2736, 1176, 170, 2712, 3504, 1229, 3351, 1122, 119, 102], [101, 1332, 1225, 1602, 2491, 1225, 1136, 2187, 136, 1177, 1155, 1686, 2187, 119, 6682, 1217, 1741, 1172, 1912, 1104, 1543, 1176, 1152, 1920, 1111, 1602, 2491, 2187, 1165, 1152, 1238, 112, 189, 1256, 1920, 1164, 141, 8101, 1592, 1177, 4268, 1831, 1114, 1103, 177, 1183, 5674, 1665, 4889, 1183, 102], [101, 137, 18810, 1795, 4203, 1274, 112, 189, 4877, 1103, 2689, 9468, 19878, 14788, 3681, 1251, 1104, 1240, 9692, 4251, 25808, 1592, 2308, 25808, 1592, 3880, 106, 9471, 1104, 1172, 1112, 1103, 1959, 1104, 7462, 160, 2240, 5301, 12426, 2137, 1107, 1103, 160, 2240, 5301, 12426, 2137, 11345, 152, 5301, 102], [101, 137, 1122, 112, 188, 2107, 1162, 1109, 3048, 1200, 6633, 1128, 1148, 119, 3414, 1587, 4246, 5703, 12118, 1119, 2993, 3046, 1653, 1648, 3584, 13280, 1612, 1119, 1209, 5113, 1105, 17088, 1128, 1632, 1141, 1105, 1229, 1128, 112, 1231, 1175, 1301, 7591, 1205, 1105, 1117, 11739, 1105, 26723, 119, 102], [101, 139, 22074, 2993, 1412, 1653, 1661, 1106, 5195, 119, 19506, 1106, 3779, 3239, 1106, 4202, 1468, 1155, 3558, 1690, 2975, 119, 1790, 1204, 1243, 1122, 6061, 119, 12998, 190, 183, 8329, 8057, 1328, 1106, 1686, 1334, 1118, 1334, 1301, 3075, 181, 1918, 1186, 119, 138, 2246, 1377, 5984, 119, 102], [101, 1650, 2157, 1602, 2491, 2187, 1105, 1136, 1155, 2491, 1268, 1175, 1128, 1132, 7492, 1155, 1168, 3117, 119, 119, 119, 119, 119, 2490, 112, 188, 1297, 2187, 146, 1274, 1204, 1920, 1184, 2942, 1137, 2241, 1110, 1137, 1191, 1128, 5340, 1137, 23423, 1114, 1139, 4893, 2490, 1116, 1297, 2187, 102]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmkQK_gVgiTu",
        "outputId": "4a720fd9-364e-4fd2-ed46-09a946448339"
      },
      "source": [
        "batch_ordered_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 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": "ff85f188-9287-4abc-fe70-aad949db92df"
      },
      "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: 559,158  (93.9% 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": "b63112fe-3d7a-4860-9bab-fc7f59ca3fdc"
      },
      "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.bert.configuration_bert.BertConfig'> \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "9db79451409641d39577c427227b39a9",
            "a2f2b8036814483e9ae549796e90eb82",
            "b18c28d699de411bb1f7720dc150da16",
            "a816eea046114e71b246c7c219ac6be4",
            "e0b5fda4da6344668708a100494638a6",
            "fd7dd2a7f7bc46968564e11d8fb31600",
            "b12f02ecf9b740f7abae9e1a559d085c",
            "febb312b46bc40268fe5a7efb03947f3"
          ]
        },
        "id": "bWvc9Ikv4HtD",
        "outputId": "05e85674-f5b8-4b47-b391-a72f1b874816"
      },
      "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": "9db79451409641d39577c427227b39a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.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": "d72c3626-4a83-4c7d-c6a0-ecd408128248"
      },
      "source": [
        "model.cuda()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(28996, 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",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "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/"
        },
        "id": "n8J4r6KyhveR",
        "outputId": "2de247e1-f6c2-4e1b-fe34-92edcd98efe5"
      },
      "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": "stream",
          "text": [
            "\n",
            "Model type: <class 'transformers.models.bert.modeling_bert.BertModel'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": {
        "id": "jvOs0G5sh02S"
      },
      "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": []
    },
    {
      "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": "a2d040a6-69d5-4e73-ba0d-a83733a7fc22"
      },
      "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: 0:00:23.  Remaining: 0:05:08\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:48.  Remaining: 0:04:53\n",
            "  Batch     300  of    1,423.    Elapsed: 0:01:12.  Remaining: 0:04:30\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:34.  Remaining: 0:04:01\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:58.  Remaining: 0:03:38\n",
            "  Batch     600  of    1,423.    Elapsed: 0:02:22.  Remaining: 0:03:14\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:45.  Remaining: 0:02:51\n",
            "  Batch     800  of    1,423.    Elapsed: 0:03:09.  Remaining: 0:02:27\n",
            "  Batch     900  of    1,423.    Elapsed: 0:03:32.  Remaining: 0:02:03\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:55.  Remaining: 0:01:39\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:04:18.  Remaining: 0:01:16\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:04:41.  Remaining: 0:00:52\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:05:05.  Remaining: 0:00:29\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:05:29.  Remaining: 0:00:05\n",
            "\n",
            "  Average training loss: 0.23\n",
            "  Training Accuracy: 0.92\n",
            "  Training epoch took: 0:05:38\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: 0:00:09\n",
            "  Batch      40  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:09\n",
            "  Batch      60  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:07\n",
            "  Batch      80  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:06\n",
            "  Batch     100  of      178.    Elapsed: 0:00:07.  Remaining: 0:00:05\n",
            "  Batch     120  of      178.    Elapsed: 0:00:08.  Remaining: 0:00:04\n",
            "  Batch     140  of      178.    Elapsed: 0:00:09.  Remaining: 0:00:02\n",
            "  Batch     160  of      178.    Elapsed: 0:00:10.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.20\n",
            "  Validation Accuracy: 0.93\n",
            "  Validation epoch took: 0:00:11\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: 0:00:24.  Remaining: 0:05:21\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:48.  Remaining: 0:04:54\n",
            "  Batch     300  of    1,423.    Elapsed: 0:01:11.  Remaining: 0:04:25\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:35.  Remaining: 0:04:03\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:58.  Remaining: 0:03:37\n",
            "  Batch     600  of    1,423.    Elapsed: 0:02:22.  Remaining: 0:03:14\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:44.  Remaining: 0:02:49\n",
            "  Batch     800  of    1,423.    Elapsed: 0:03:08.  Remaining: 0:02:27\n",
            "  Batch     900  of    1,423.    Elapsed: 0:03:33.  Remaining: 0:02:04\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:57.  Remaining: 0:01:40\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:04:19.  Remaining: 0:01:16\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:04:43.  Remaining: 0:00:53\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:05:07.  Remaining: 0:00:29\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:05:31.  Remaining: 0:00:05\n",
            "\n",
            "  Average training loss: 0.16\n",
            "  Training Accuracy: 0.95\n",
            "  Training epoch took: 0:05:38\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: 0:00:09\n",
            "  Batch      40  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:08\n",
            "  Batch      60  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:07\n",
            "  Batch      80  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:06\n",
            "  Batch     100  of      178.    Elapsed: 0:00:06.  Remaining: 0:00:05\n",
            "  Batch     120  of      178.    Elapsed: 0:00:07.  Remaining: 0:00:04\n",
            "  Batch     140  of      178.    Elapsed: 0:00:09.  Remaining: 0:00:02\n",
            "  Batch     160  of      178.    Elapsed: 0:00:10.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.24\n",
            "  Validation Accuracy: 0.92\n",
            "  Validation epoch took: 0:00:12\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: 0:00:23.  Remaining: 0:05:11\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:48.  Remaining: 0:04:51\n",
            "  Batch     300  of    1,423.    Elapsed: 0:01:11.  Remaining: 0:04:24\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:35.  Remaining: 0:04:02\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:58.  Remaining: 0:03:39\n",
            "  Batch     600  of    1,423.    Elapsed: 0:02:21.  Remaining: 0:03:13\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:43.  Remaining: 0:02:48\n",
            "  Batch     800  of    1,423.    Elapsed: 0:03:06.  Remaining: 0:02:25\n",
            "  Batch     900  of    1,423.    Elapsed: 0:03:29.  Remaining: 0:02:01\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:53.  Remaining: 0:01:39\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:04:16.  Remaining: 0:01:15\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:04:39.  Remaining: 0:00:52\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:05:03.  Remaining: 0:00:29\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:05:27.  Remaining: 0:00:05\n",
            "\n",
            "  Average training loss: 0.10\n",
            "  Training Accuracy: 0.97\n",
            "  Training epoch took: 0:05:36\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: 0:00:08\n",
            "  Batch      40  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:07\n",
            "  Batch      60  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:07\n",
            "  Batch      80  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:06\n",
            "  Batch     100  of      178.    Elapsed: 0:00:06.  Remaining: 0:00:05\n",
            "  Batch     120  of      178.    Elapsed: 0:00:07.  Remaining: 0:00:04\n",
            "  Batch     140  of      178.    Elapsed: 0:00:09.  Remaining: 0:00:02\n",
            "  Batch     160  of      178.    Elapsed: 0:00:10.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.28\n",
            "  Validation Accuracy: 0.92\n",
            "  Validation epoch took: 0:00:11\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: 0:00:25.  Remaining: 0:05:34\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:48.  Remaining: 0:04:56\n",
            "  Batch     300  of    1,423.    Elapsed: 0:01:11.  Remaining: 0:04:26\n",
            "  Batch     400  of    1,423.    Elapsed: 0:01:34.  Remaining: 0:04:00\n",
            "  Batch     500  of    1,423.    Elapsed: 0:01:57.  Remaining: 0:03:36\n",
            "  Batch     600  of    1,423.    Elapsed: 0:02:19.  Remaining: 0:03:11\n",
            "  Batch     700  of    1,423.    Elapsed: 0:02:44.  Remaining: 0:02:49\n",
            "  Batch     800  of    1,423.    Elapsed: 0:03:07.  Remaining: 0:02:26\n",
            "  Batch     900  of    1,423.    Elapsed: 0:03:31.  Remaining: 0:02:03\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:03:53.  Remaining: 0:01:38\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:04:17.  Remaining: 0:01:15\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:04:38.  Remaining: 0:00:52\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:05:02.  Remaining: 0:00:29\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:05:26.  Remaining: 0:00:05\n",
            "\n",
            "  Average training loss: 0.05\n",
            "  Training Accuracy: 0.99\n",
            "  Training epoch took: 0:05:34\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: 0:00:11\n",
            "  Batch      40  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:08\n",
            "  Batch      60  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:07\n",
            "  Batch      80  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:06\n",
            "  Batch     100  of      178.    Elapsed: 0:00:06.  Remaining: 0:00:05\n",
            "  Batch     120  of      178.    Elapsed: 0:00:07.  Remaining: 0:00:04\n",
            "  Batch     140  of      178.    Elapsed: 0:00:09.  Remaining: 0:00:02\n",
            "  Batch     160  of      178.    Elapsed: 0:00:10.  Remaining: 0:00:01\n",
            "\n",
            "  Average validation loss: 0.38\n",
            "  Validation Accuracy: 0.92\n",
            "  Validation epoch took: 0:00:11\n",
            "Best val accuracy: 0.9264861062258178\n",
            "\n",
            "Training complete!\n",
            "Total training took 0:23:26 (h:mm:ss)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "GOmua_S1eXNM",
        "outputId": "04e5ca6d-5cd8-4e5e-d5b7-3a7db52b789f"
      },
      "source": [
        "plot_training_history(training_stats)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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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": "5c150504-ebf0-4bd7-bab2-36253de374f3"
      },
      "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: 0:00:06.  Remaining: 0:01:22\n",
            "  Batch     200  of    1,423.    Elapsed: 0:00:13.  Remaining: 0:01:17\n",
            "  Batch     300  of    1,423.    Elapsed: 0:00:19.  Remaining: 0:01:10\n",
            "  Batch     400  of    1,423.    Elapsed: 0:00:25.  Remaining: 0:01:04\n",
            "  Batch     500  of    1,423.    Elapsed: 0:00:31.  Remaining: 0:00:57\n",
            "  Batch     600  of    1,423.    Elapsed: 0:00:37.  Remaining: 0:00:50\n",
            "  Batch     700  of    1,423.    Elapsed: 0:00:43.  Remaining: 0:00:44\n",
            "  Batch     800  of    1,423.    Elapsed: 0:00:49.  Remaining: 0:00:38\n",
            "  Batch     900  of    1,423.    Elapsed: 0:00:55.  Remaining: 0:00:32\n",
            "  Batch   1,000  of    1,423.    Elapsed: 0:01:02.  Remaining: 0:00:26\n",
            "  Batch   1,100  of    1,423.    Elapsed: 0:01:07.  Remaining: 0:00:20\n",
            "  Batch   1,200  of    1,423.    Elapsed: 0:01:13.  Remaining: 0:00:14\n",
            "  Batch   1,300  of    1,423.    Elapsed: 0:01:19.  Remaining: 0:00:07\n",
            "  Batch   1,400  of    1,423.    Elapsed: 0:01:25.  Remaining: 0:00:01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXsVn0wngZhv",
        "outputId": "6304f385-822a-40fe-aba4-0c72a9797d50"
      },
      "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       0.74      0.56      0.63      1962\n",
            "Non_hope_speech       0.96      0.98      0.97     20778\n",
            "    not-English       0.00      0.00      0.00        22\n",
            "\n",
            "       accuracy                           0.94     22762\n",
            "      macro avg       0.57      0.51      0.53     22762\n",
            "   weighted avg       0.94      0.94      0.94     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": "4e80b732-dd90-46ee-db04-79e1d5937ec8"
      },
      "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": "9bcee496-f348-4960-bbe2-0a6b7de8b2c0"
      },
      "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: 0:00:12\n",
            "  Batch      40  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:09\n",
            "  Batch      60  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:07\n",
            "  Batch      80  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:06\n",
            "  Batch     100  of      178.    Elapsed: 0:00:06.  Remaining: 0:00:05\n",
            "  Batch     120  of      178.    Elapsed: 0:00:07.  Remaining: 0:00:04\n",
            "  Batch     140  of      178.    Elapsed: 0:00:09.  Remaining: 0:00:02\n",
            "  Batch     160  of      178.    Elapsed: 0:00:10.  Remaining: 0:00:01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAjlATBPgh7c",
        "outputId": "ca5bb95a-8f29-44cf-dff3-de2955f71390"
      },
      "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       0.67      0.47      0.55       272\n",
            "Non_hope_speech       0.94      0.98      0.96      2569\n",
            "    not-English       0.00      0.00      0.00         2\n",
            "\n",
            "       accuracy                           0.93      2843\n",
            "      macro avg       0.54      0.48      0.50      2843\n",
            "   weighted avg       0.92      0.93      0.92      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": "989ecea3-c279-4dd9-ddab-f5ffecc5b2c5"
      },
      "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": "c5617f3b-2e13-4573-e851-ef96d4f6522d"
      },
      "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: 0:00:11\n",
            "  Batch      40  of      178.    Elapsed: 0:00:02.  Remaining: 0:00:08\n",
            "  Batch      60  of      178.    Elapsed: 0:00:04.  Remaining: 0:00:07\n",
            "  Batch      80  of      178.    Elapsed: 0:00:05.  Remaining: 0:00:06\n",
            "  Batch     100  of      178.    Elapsed: 0:00:06.  Remaining: 0:00:05\n",
            "  Batch     120  of      178.    Elapsed: 0:00:07.  Remaining: 0:00:04\n",
            "  Batch     140  of      178.    Elapsed: 0:00:08.  Remaining: 0:00:02\n",
            "  Batch     160  of      178.    Elapsed: 0:00:10.  Remaining: 0:00:01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6EJfj0TNT6R",
        "outputId": "07e92448-05ca-4a4e-e5d9-6510775804b5"
      },
      "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       0.67      0.50      0.57       250\n",
            "Non_hope_speech       0.95      0.98      0.96      2593\n",
            "    not-English       0.00      0.00      0.00         3\n",
            "\n",
            "       accuracy                           0.93      2846\n",
            "      macro avg       0.54      0.49      0.51      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": "8dc45b78-2f22-4784-9cd2-2b99bb456975"
      },
      "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": "eV1nPYflSeo_"
      },
      "source": [
        "## Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsm1xnwtSepE"
      },
      "source": [
        "torch.save(\r\n",
        "    model,\r\n",
        "    \"./drive/MyDrive/Codalab/HopeSpeech Detection For Equality,DiversityAnd Inclusion/Models/BERT_cased_English\"\r\n",
        "    )"
      ],
      "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(\r\n",
        "    './drive/MyDrive/HopeSpeech Detection For Equality,DiversityAnd Inclusion/Models/BERTForSequenceClassification_English_v2',\r\n",
        "                   map_location=device\r\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAaHpZdIzsnT",
        "outputId": "a9a59bc5-72ef-4547-ee59-067485be1162"
      },
      "source": [
        "model.cuda()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 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",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAwCfk8gSepL"
      },
      "source": [
        "## Making Predictions on Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHoA-D_xSepL"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "bqdeFPaqSepM",
        "outputId": "5639bd58-46e2-4e98-ad07-373258a8eae4"
      },
      "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": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jLPpljYQcsQ"
      },
      "source": [
        "## Loading `test_sentences`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEVoAhgIQcsR"
      },
      "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": "slovGuCRThW1",
        "outputId": "208ed450-338d-4d79-ea99-6628c89c5036"
      },
      "source": [
        "test_ids"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   0,    1,    2, ..., 2843, 2844, 2845])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gL7aCG2HTjv6",
        "outputId": "d8b382d3-30c8-4816-db1c-f474091a25cd"
      },
      "source": [
        "test_sentences"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['What do you mean by the word sniped?',\n",
              "       'I love this video!! I’m bisexual and it’s just really nice to hear that someone has gone through similar things like me and that I’m not alone. My story is nothing like his and my heart goes out to him to slowly heal from that',\n",
              "       \"ya the irony but then i don't want to come off as a bigot myself\",\n",
              "       ..., 'Abandorn Hope Situation',\n",
              "       'Sheriff Clarke you are a person of such strong character and wisdom.nI want to see more of you as a public figure and much less of the celebs who I admire for their acting but not their biased views.',\n",
              "       \"Sanders has no room to talk. If there's one person who has white privilege....\"],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9dgO1m3QcsU",
        "outputId": "cedd5dd6-e148-49a9-f4da-0e0077df751c"
      },
      "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": "825f5220-bdb1-49ec-b910-efc755c087a3"
      },
      "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: 0:00:05\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: 0:00:02\n",
            "  Batch     100  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:02\n",
            "  Batch     120  of      178.    Elapsed: 0:00:03.  Remaining: 0:00:01\n",
            "  Batch     140  of      178.    Elapsed: 0:00:03.  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": "806f7b50-3dbc-4f3e-b49d-7e6c144bd076"
      },
      "source": [
        "print(y_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 1 ... 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWWWjz7eUPc7",
        "outputId": "949286ed-3899-4493-d679-31404dce5793"
      },
      "source": [
        "print(y_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1390 1396 1397 ... 2648   42  105]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2ybbsAoQcsY",
        "outputId": "8074a4df-f0df-4749-8302-f94b1308c935"
      },
      "source": [
        "le.inverse_transform(y_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Non_hope_speech', 'Hope_speech', 'Non_hope_speech', ...,\n",
              "       'Non_hope_speech', 'Non_hope_speech', 'Non_hope_speech'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kSMb6sCLQcsZ",
        "outputId": "2148c64f-4ef4-420f-8757-a8ddd4ec6b30"
      },
      "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 0x7f5a8b124080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KXOXwH9U2MT",
        "outputId": "6e085a59-0f0f-42f9-f873-c5702ace8755"
      },
      "source": [
        "len(y_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2846"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-fkBgOxU4bX",
        "outputId": "665adfd7-c523-42c4-cb90-276d91ae7bf5"
      },
      "source": [
        "len(le.inverse_transform(y_preds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2846"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "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": "df0b6f5a-00ca-4be2-f9b6-7eac4b5e3ba9"
      },
      "source": [
        "df_new"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</table>\n",
              "<p>2846 rows × 2 columns</p>\n",
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              "        id            label\n",
              "0     1390  Non_hope_speech\n",
              "1     1396      Hope_speech\n",
              "2     1397  Non_hope_speech\n",
              "3     1424      Hope_speech\n",
              "4     1447  Non_hope_speech\n",
              "...    ...              ...\n",
              "2841  1068  Non_hope_speech\n",
              "2842  1684  Non_hope_speech\n",
              "2843  2648  Non_hope_speech\n",
              "2844    42  Non_hope_speech\n",
              "2845   105  Non_hope_speech\n",
              "\n",
              "[2846 rows x 2 columns]"
            ]
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          "metadata": {
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      "source": [
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              "    </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",
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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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              "  </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",
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          "height": 402
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        "id": "vxReWWmxn7Hj",
        "outputId": "2d99b39c-45f5-4dfa-aa88-c12aa1c464d5"
      },
      "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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              "      <td>Non_hope_speech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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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              "    <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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              "<p>2846 rows × 3 columns</p>\n",
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            ],
            "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": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "1kuASymYn9aV",
        "outputId": "1af23bd2-b5a9-49e1-97d5-0404dde68b8a"
      },
      "source": [
        "df_f = df_f[['id','text','label']]\r\n",
        "df_f"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2841</th>\n",
              "      <td>2841</td>\n",
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              "    <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": 72
        }
      ]
    },
    {
      "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,'bert_base_uncased_english.csv'),index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jW9g_qgMv7mq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}