Toxic_Commnet_with_distilbert_multilingual.ipynb

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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4zGKw9_k7HI6"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj_7Tz0-pK69",
        "outputId": "38bc6087-01bb-4063-9029-c265151fdf66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/1.6 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U watermark"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jjsbi1u3QFEM",
        "outputId": "c80fddc5-ac4e-492e-ce75-f9850ef003db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m58.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m122.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m90.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qq transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AJqoaFpVpoM8",
        "outputId": "8f202709-d542-476b-e8c4-db7cf4352adb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python implementation: CPython\n",
            "Python version       : 3.10.12\n",
            "IPython version      : 7.34.0\n",
            "\n",
            "numpy       : 1.23.5\n",
            "pandas      : 1.5.3\n",
            "torch       : 2.0.1+cu118\n",
            "transformers: 4.31.0\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%reload_ext watermark\n",
        "%watermark -v -p numpy,pandas,torch,transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w68CZpOwFoly",
        "outputId": "90fa333f-74ce-4bf4-d847-dd01b96035e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#@title Setup & Config\n",
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from collections import defaultdict\n",
        "from textwrap import wrap\n",
        "\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufzPdoTtNikq"
      },
      "source": [
        "## Data Exploration\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "mUKLyKc7I6Qp",
        "outputId": "ecbbad9f-a2e9-4643-a002-f4ddfad5bd2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Com  Class\n",
              "0  বড়রাচোদা আদিবাসী গুলান। অগোর পিছে দিয়া বাঁশ ...      1\n",
              "1  শুয়োরের বাচ্চা গুলা খায় ও শুয়োর স্বভাব ও শু...      1\n",
              "2  আমি বুঝি না এই বানরের বাচ্চাগুলা দেশের প্রশাসন...      1\n",
              "3  থাকতে দিসি দেশে থাকবে গোলামের মত। ওমা এরা দেখি...      1\n",
              "4  শালার মাদারচোদের দেশে থাকি। দেশের মানুষের দাম ...      1\n",
              "5  আদিবাসী মানেই ঝামেলা। দেশের স্বাধীনতা কি এই বা...      1\n",
              "6                    শালাা বানর চোদা খানকি মাগীর দল।      1\n",
              "7  আদিবাসী খানকি মাগি ডি বানর বেইসশার লগে সহবাস ক...      1\n",
              "8  বানর চোদে বানররে আদিবাসী চোদে আদিবাসী রে। কিন্...      1\n",
              "9  চিড়িয়াখানা তে সেদিন দেখি বানর অনেক কম। এই আদিব...      1"
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              "              title=\"Suggest charts.\"\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df = pd.read_excel(\"/content/Hate Comments.xlsx\")\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eF15byEmqGOj"
      },
      "outputs": [],
      "source": [
        "df=df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dB2jE6am7Dpo",
        "outputId": "9e575d58-35ed-40ce-f524-0c0779d82b72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3067, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "_f5FmAIUoRKp",
        "outputId": "f101f5cb-89f8-47a4-8547-bfc5f96fdcd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    Com  Class\n",
              "0     বড়রাচোদা আদিবাসী গুলান। অগোর পিছে দিয়া বাঁশ ...      1\n",
              "1     শুয়োরের বাচ্চা গুলা খায় ও শুয়োর স্বভাব ও শু...      1\n",
              "2     আমি বুঝি না এই বানরের বাচ্চাগুলা দেশের প্রশাসন...      1\n",
              "3     থাকতে দিসি দেশে থাকবে গোলামের মত। ওমা এরা দেখি...      1\n",
              "4     শালার মাদারচোদের দেশে থাকি। দেশের মানুষের দাম ...      1\n",
              "...                                                 ...    ...\n",
              "3063      ইশ আপু,আপনার বাল আজকে আপনার ইজ্জত রক্ষা করছে!      3\n",
              "3064                                ওর মুখে থু থু মারেন      3\n",
              "3065               সিরাজুদ্দৌলার সাথে দেখা কর গিয়া মাগি      3\n",
              "3066                           হিংসা হয় আমার ছবিটা দেখে      3\n",
              "3067  তোর বাবা মা কি ভাবে তোকে জন্ম দিয়েছি তুই কি দ...      3\n",
              "\n",
              "[3067 rows x 2 columns]"
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              "      <th>0</th>\n",
              "      <td>বড়রাচোদা আদিবাসী গুলান। অগোর পিছে দিয়া বাঁশ ...</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>শুয়োরের বাচ্চা গুলা খায় ও শুয়োর স্বভাব ও শু...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>আমি বুঝি না এই বানরের বাচ্চাগুলা দেশের প্রশাসন...</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>থাকতে দিসি দেশে থাকবে গোলামের মত। ওমা এরা দেখি...</td>\n",
              "      <td>1</td>\n",
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              "      <td>শালার মাদারচোদের দেশে থাকি। দেশের মানুষের দাম ...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3063</th>\n",
              "      <td>ইশ আপু,আপনার বাল আজকে আপনার ইজ্জত রক্ষা করছে!</td>\n",
              "      <td>3</td>\n",
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              "      <th>3064</th>\n",
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              "      <th>3065</th>\n",
              "      <td>সিরাজুদ্দৌলার সাথে দেখা কর গিয়া মাগি</td>\n",
              "      <td>3</td>\n",
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              "      <th>3066</th>\n",
              "      <td>হিংসা হয় আমার ছবিটা দেখে</td>\n",
              "      <td>3</td>\n",
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              "      <th>3067</th>\n",
              "      <td>তোর বাবা মা কি ভাবে তোকে জন্ম দিয়েছি তুই কি দ...</td>\n",
              "      <td>3</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df[:]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45xRsQ7teGOC",
        "outputId": "e9fc94f2-8551-481d-caa1-f08cb78edd0b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    1011\n",
              "2     792\n",
              "0     751\n",
              "1     513\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Uzo-0ndZItS8"
      },
      "outputs": [],
      "source": [
        "df=df[df['Class'] != 3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8L3q86vlgQq",
        "outputId": "66b479ee-91ff-473a-a2d8-cadf7ac51827"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    792\n",
              "0    751\n",
              "1    513\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['Class'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWqVNHJbn10l"
      },
      "source": [
        "We have about 16k examples. Let's check for missing values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VA_wGSLQLKCh",
        "outputId": "18322933-8bb4-4268-dd1a-87a9f76ec114"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2056 entries, 0 to 2056\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Com     2056 non-null   object\n",
            " 1   Class   2056 non-null   int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 48.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ogyVhR2cwnmg"
      },
      "outputs": [],
      "source": [
        "#class_names = ['Trans', 'Indigenous','Migrated','Universal Toxic']\n",
        "class_names = ['Trans', 'Indigenous','Migrated']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3cL_1qVn_6h"
      },
      "source": [
        "Great, no missing values in the score and review texts! Do we have class imbalance?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "Wwh_rW4Efhs3",
        "outputId": "3207033b-d042-4542-e1f3-4300d9f5a80d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 868,
              "height": 539
            }
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Increase the figure size\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "Toxic_count = df['Class'].value_counts()\n",
        "#sns.countplot(x='Score', data=df, order=Toxic_count.index)\n",
        "#sns.countplot(x='Score', data=df, order=Toxic_count.index, color='blue')\n",
        "sns.countplot(x='Class', data=df, palette=['#FF2400', 'teal','#A52A2A','Seagreen'])\n",
        "# Rename the x-axis tick labels\n",
        "new_labels = class_names# Replace with your desired labels\n",
        "plt.gca().set_xticklabels(new_labels)\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Mes8WLB9l38v"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "0JhueI6qFu8r"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "from transformers import BertForMaskedLM, BertTokenizer, pipeline\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "E7Mj-0ne--5t"
      },
      "outputs": [],
      "source": [
        "PRE_TRAINED_MODEL_NAME = 'distilbert-base-multilingual-cased'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "H3AfJSZ8NNLF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
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            "fa58ee3470214f6396cf014cb28c07ce",
            "a3f84b0b92f549ed92656ab26230f1f6",
            "6498ae3a909247f7867f6d55344ec137",
            "a18a58a06bc34629a4ef4ce645ff1d8c",
            "1466367b1dba45f08f9daff3d218b2d4",
            "26f079354a1348f489870c769e01f9ec",
            "460acab543d942eca9b341411e78b580",
            "e8fe93e49dc3496d9b5528b588c226bc",
            "a21465e3552042d5be1403e9d5708d04",
            "13b044fc1a5647a18e6fe308c4103bde"
          ]
        },
        "outputId": "98cef15e-3c3d-4a4c-f773-a6341c3e3242"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d61390fa52847b08754c9f97091e1d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e67c82b05da42638ea4da0bf7c91d97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/466 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37160df40a5c46828e5c57a05bf8716e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'DistilBertTokenizer'. \n",
            "The class this function is called from is 'BertTokenizer'.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "HZMitwrqm2eb"
      },
      "outputs": [],
      "source": [
        "sample_txt = 'আমি মুকাফফি '"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDmWTJWqzknY",
        "outputId": "e22f21af-8a4f-4764-c08c-445bb3fb9a8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Sentence: আমি মুকাফফি \n",
            "   Tokens: ['আ', '##মি', 'ম', '##ুক', '##া', '##ফ', '##ফ', '##ি']\n",
            "Token IDs: [938, 37376, 972, 69514, 12079, 54632, 54632, 12235]\n"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.tokenize(sample_txt)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(f' Sentence: {sample_txt}')\n",
        "print(f'   Tokens: {tokens}')\n",
        "print(f'Token IDs: {token_ids}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO2qBTVl_KPs"
      },
      "source": [
        "Some basic operations can convert the text to tokens and tokens to unique integers (ids):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTFhpHpsoWO7",
        "outputId": "15218a67-3536-4c0b-90b5-3b79c6dc551f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Sentence: আমি মুকাফফি \n",
            "   Tokens: ['আ', '##মি', 'ম', '##ুক', '##া', '##ফ', '##ফ', '##ি']\n",
            "Token IDs: [938, 37376, 972, 69514, 12079, 54632, 54632, 12235]\n"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.tokenize(sample_txt)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(f' Sentence: {sample_txt}')\n",
        "print(f'   Tokens: {tokens}')\n",
        "print(f'Token IDs: {token_ids}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzbbKLR8lZbu"
      },
      "source": [
        "### Special Tokens\n",
        "\n",
        "`[SEP]` - marker for ending of a sentence\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXwz47bQvCbc",
        "outputId": "234ff52d-59ed-48be-dd21-6e20606c55ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[SEP]', 102)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tokenizer.sep_token, tokenizer.sep_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mip_eGeXwLFF"
      },
      "source": [
        "`[CLS]` - we must add this token to the start of each sentence, so BERT knows we're doing classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6K4it5HwE6l",
        "outputId": "2143ace5-c8a8-4d45-a76a-174bc6895ef3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[CLS]', 101)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "tokenizer.cls_token, tokenizer.cls_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qi6O-yEY09gl"
      },
      "source": [
        "There is also a special token for padding:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vx7gD5xf1AFK",
        "outputId": "9ed7e9bb-8369-4c90-e6a5-24a3928bc26a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[PAD]', 0)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "tokenizer.pad_token, tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GWCfijM0TWB"
      },
      "source": [
        "BERT understands tokens that were in the training set. Everything else can be encoded using the `[UNK]` (unknown) token:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cmfFsbEKQDT",
        "outputId": "54fd5557-130f-424c-ead5-5346e3e097d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[UNK]', 100)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "tokenizer.unk_token, tokenizer.unk_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9ap7jdL0LYU"
      },
      "source": [
        "All of that work can be done using the [`encode_plus()`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.encode_plus) method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vea9edaaxSPO",
        "outputId": "9d20ffcb-bdf8-47b9-aaa4-d326b9208a02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "  sample_txt,\n",
        "  max_length=32,\n",
        "  add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  truncation=True,\n",
        "  return_tensors='pt',  # Return PyTorch tensors\n",
        "\n",
        ")\n",
        "\n",
        "encoding.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS69c8WvdOED"
      },
      "source": [
        "The token ids are now stored in a Tensor and padded to a length of 32:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzBmcOla0yQR",
        "outputId": "1a9d6e10-541d-435a-a5ea-a7fcc8184839"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([  101,   938, 37376,   972, 69514, 12079, 54632, 54632, 12235,   102,\n",
              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0,     0])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "print(len(encoding['input_ids'][0]))\n",
        "encoding['input_ids'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itAyVPsNdyc1"
      },
      "source": [
        "The attention mask has the same length:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wiv5LLiw03Ox",
        "outputId": "04553341-ed62-48a4-d824-81060b271168"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "print(len(encoding['attention_mask'][0]))\n",
        "encoding['attention_mask']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1RvhC4jNHHy"
      },
      "source": [
        "We can inverse the tokenization to have a look at the special tokens:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IagGoafKLUwW",
        "outputId": "d867f1c9-9f87-4bd2-fb1f-61d22bff3c92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'আ',\n",
              " '##মি',\n",
              " 'ম',\n",
              " '##ুক',\n",
              " '##া',\n",
              " '##ফ',\n",
              " '##ফ',\n",
              " '##ি',\n",
              " '[SEP]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]']"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waKjYxTDuaWt"
      },
      "source": [
        "### Choosing Sequence Length\n",
        "\n",
        "BERT works with fixed-length sequences. We'll use a simple strategy to choose the max length. Let's store the token length of each review:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUnE5CT9hbeZ",
        "outputId": "8b85e867-c0ac-455a-d67f-f1dc1011ddcd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ]
        }
      ],
      "source": [
        "token_lens = []\n",
        "\n",
        "for txt in df.Com:\n",
        "  #txt = normalize(txt) # this normalization step is required before tokenizing the text\n",
        "  tokens = tokenizer.encode(txt, max_length=512)\n",
        "  token_lens.append(len(tokens))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI4goUrHf6da"
      },
      "source": [
        "and plot the distribution:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "SzE1j4jxmUtd",
        "outputId": "bd246669-1c5e-4528-bc08-b2e6d51cddab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 1041,
              "height": 693
            }
          }
        }
      ],
      "source": [
        "sns.distplot(token_lens)\n",
        "plt.xlim([0, 256]);\n",
        "plt.xlabel('Token count');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oW6ajl30t6du"
      },
      "source": [
        "Most of the reviews seem to contain less than 128 tokens, but we'll be on the safe side and choose a maximum length of 160."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "t7xSmJtLuoxW"
      },
      "outputs": [],
      "source": [
        "MAX_LEN = 160"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvvcoU6nurHy"
      },
      "source": [
        "We have all building blocks required to create a PyTorch dataset. Let's do it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "E2BPgRJ7YBK0"
      },
      "outputs": [],
      "source": [
        "class GPReviewDataset(Dataset):\n",
        "\n",
        "  def __init__(self, comments, targets, tokenizer, max_len):\n",
        "    self.comments = comments\n",
        "    self.targets = targets\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_len = max_len\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.comments)\n",
        "  def __getitem__(self, item):\n",
        "    review = str(self.comments[item])\n",
        "    target = self.targets[item]\n",
        "\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      review,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_len,\n",
        "      return_token_type_ids=False,\n",
        "      pad_to_max_length=True,\n",
        "      return_attention_mask=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "\n",
        "    return {\n",
        "      'toxic_comments': review,\n",
        "      'input_ids': encoding['input_ids'].flatten(),\n",
        "      'attention_mask': encoding['attention_mask'].flatten(),\n",
        "      'targets': torch.tensor(target, dtype=torch.long)\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2uwsvCYqDJK"
      },
      "source": [
        "The tokenizer is doing most of the heavy lifting for us. We also return the review texts, so it'll be easier to evaluate the predictions from our model. Let's split the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "B-vWzoo81dvO"
      },
      "outputs": [],
      "source": [
        "df_train, df_test = train_test_split(df, test_size=0.30, shuffle=True)\n",
        "df_val, df_test = train_test_split(df_test, test_size=0.65,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xz3ZOQXVPCwh",
        "outputId": "d042fd5e-2094-4433-ece6-0abff524dca3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1439, 2), (215, 2), (402, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "df_train.shape, df_val.shape, df_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4tQ1x-vqNab"
      },
      "source": [
        "We also need to create a couple of data loaders. Here's a helper function to do it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "KEGqcvkuOuTX"
      },
      "outputs": [],
      "source": [
        "\n",
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "  ds = GPReviewDataset(\n",
        "    comments=df.Com.to_numpy(),\n",
        "    targets=df.Class.to_numpy(),\n",
        "    tokenizer=tokenizer,\n",
        "    max_len=max_len,\n",
        "\n",
        "  )\n",
        "\n",
        "  return DataLoader(\n",
        "    ds,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=4,\n",
        "    shuffle=True\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "vODDxMKsPHqI"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 16\n",
        "\n",
        "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A6dlOptwqlhF"
      },
      "source": [
        "Let's have a look at an example batch from our training data loader:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y93ldSN47FeT",
        "outputId": "63ef5e48-b31f-45aa-d80c-80235b7297b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['toxic_comments', 'input_ids', 'attention_mask', 'targets'])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "data = next(iter(train_data_loader))\n",
        "data.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdU4YVqb7N8M",
        "outputId": "5ae04e2c-7fdf-44f8-a430-701e8b283b96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([16, 160])\n",
            "torch.Size([16, 160])\n",
            "torch.Size([16])\n"
          ]
        }
      ],
      "source": [
        "print(data['input_ids'].shape)\n",
        "print(data['attention_mask'].shape)\n",
        "print(data['targets'].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H63Y-TjyRC7S"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "440Nd31VTHER"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "0P41FayISNRI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26c552d4-4074-4b62-f35e-4db5a8f9dd50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using a model of type distilbert to instantiate a model of type bert. This is not supported for all configurations of models and can yield errors.\n",
            "Some weights of BertModel were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['encoder.layer.0.attention.self.key.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.0.output.dense.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.1.output.dense.bias', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.3.output.dense.weight', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.4.intermediate.dense.bias', 'embeddings.word_embeddings.weight', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.4.output.dense.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.10.attention.self.query.bias', 'pooler.dense.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'embeddings.position_embeddings.weight', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.0.attention.self.query.bias', 'embeddings.token_type_embeddings.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.11.output.LayerNorm.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.2.output.dense.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.2.output.dense.weight', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.11.output.dense.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.10.output.dense.bias', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.11.output.dense.weight', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.2.attention.self.key.weight', 'pooler.dense.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.dense.weight', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.8.output.LayerNorm.bias', 'embeddings.LayerNorm.bias', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.10.attention.output.dense.bias', 'embeddings.LayerNorm.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.0.attention.output.LayerNorm.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) # method name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "ED7_yrjyt3k6"
      },
      "outputs": [],
      "source": [
        "!export LRU_CACHE_CAPACITY=1\n",
        "!export 'PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "QVDHPUI3t5U5"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFE7YSbFdY4t"
      },
      "source": [
        "And try to use it on the encoding of our sample text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "s1aoFxbQSn15"
      },
      "outputs": [],
      "source": [
        "last_hidden_state, pooled_output = bert_model(\n",
        "  input_ids=encoding['input_ids'],\n",
        "  attention_mask=encoding['attention_mask']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "mUJHXNpIbcci",
        "outputId": "a127bac0-19e2-4c85-df69-5106f6697051"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'distilbert-base-multilingual-cased'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "PRE_TRAINED_MODEL_NAME"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4dAot4zbz8k"
      },
      "source": [
        "We have the hidden state for each of our 32 tokens (the length of our example sequence). But why 768? This is the number of hidden units in the feedforward-networks. We can verify that by checking the config:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nsxB7Qy7b5YN",
        "outputId": "075e9ab1-327c-49cd-d148-6d9f3688d62a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "bert_model.config.hidden_size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTKi8-rTd_j4"
      },
      "source": [
        "\n",
        "\n",
        "You can think of the `pooled_output` as a summary of the content, according to BERT. Albeit, you might try and do better. Let's look at the shape of the output:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "2jIAtRhaSz9c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0o_NiS3WgOFf"
      },
      "source": [
        "We can use all of this knowledge to create a classifier that uses the BERT model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "m_mRflxPl32F"
      },
      "outputs": [],
      "source": [
        "class ToxicClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self, n_classes):\n",
        "    super(ToxicClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) #model\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
        "\n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    _, pooled_output = self.bert(\n",
        "         input_ids=input_ids,\n",
        "         attention_mask=attention_mask,\n",
        "         return_dict=False\n",
        "    )\n",
        "    output = self.drop(pooled_output)\n",
        "    return self.out(output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJg8m3NQJahc"
      },
      "source": [
        "Our classifier delegates most of the heavy lifting to the BertModel. We use a dropout layer for some regularization and a fully-connected layer for our output. Note that we're returning the raw output of the last layer since that is required for the cross-entropy loss function in PyTorch to work.\n",
        "\n",
        "This should work like any other PyTorch model. Let's create an instance and move it to the GPU:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "i0yQnuSFsjDp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3efa856-92f3-47b1-8cae-80c7b58b8cc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using a model of type distilbert to instantiate a model of type bert. This is not supported for all configurations of models and can yield errors.\n",
            "Some weights of BertModel were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['encoder.layer.0.attention.self.key.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.0.output.dense.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.1.output.dense.bias', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.3.output.dense.weight', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.4.intermediate.dense.bias', 'embeddings.word_embeddings.weight', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.4.output.dense.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.10.attention.self.query.bias', 'pooler.dense.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'embeddings.position_embeddings.weight', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.0.attention.self.query.bias', 'embeddings.token_type_embeddings.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.11.output.LayerNorm.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.2.output.dense.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.2.output.dense.weight', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.11.output.dense.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.10.output.dense.bias', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.11.output.dense.weight', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.2.attention.self.key.weight', 'pooler.dense.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.dense.weight', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.8.output.LayerNorm.bias', 'embeddings.LayerNorm.bias', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.10.attention.output.dense.bias', 'embeddings.LayerNorm.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.0.attention.output.LayerNorm.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = ToxicClassifier(len(class_names))\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCPCFDLlKIQd"
      },
      "source": [
        "We'll move the example batch of our training data to the GPU:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mz7p__CqdaMO",
        "outputId": "e9658e3b-8334-4e6b-dc76-423d28486481"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([16, 160])\n",
            "torch.Size([16, 160])\n"
          ]
        }
      ],
      "source": [
        "input_ids = data['input_ids'].to(device)\n",
        "attention_mask = data['attention_mask'].to(device)\n",
        "\n",
        "print(input_ids.shape) # batch size x seq length\n",
        "print(attention_mask.shape) # batch size x seq length"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hr1EgkEtKOIB"
      },
      "source": [
        "To get the predicted probabilities from our trained model, we'll apply the softmax function to the outputs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rTCj46Zamry",
        "outputId": "77d3be04-ee93-41bd-9df0-c16cfa0b9c0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.4945, 0.1880, 0.3175],\n",
              "        [0.4146, 0.2401, 0.3453],\n",
              "        [0.4802, 0.2219, 0.2979],\n",
              "        [0.5113, 0.2039, 0.2847],\n",
              "        [0.3777, 0.1877, 0.4346],\n",
              "        [0.2985, 0.2178, 0.4838],\n",
              "        [0.4844, 0.1943, 0.3213],\n",
              "        [0.4182, 0.2362, 0.3456],\n",
              "        [0.4061, 0.1620, 0.4319],\n",
              "        [0.4004, 0.1703, 0.4293],\n",
              "        [0.4657, 0.2263, 0.3080],\n",
              "        [0.4468, 0.2391, 0.3140],\n",
              "        [0.4045, 0.2425, 0.3530],\n",
              "        [0.4465, 0.2468, 0.3067],\n",
              "        [0.4722, 0.2199, 0.3079],\n",
              "        [0.4841, 0.1884, 0.3274]], device='cuda:0', grad_fn=<SoftmaxBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "F.softmax(model(input_ids, attention_mask), dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "PcX-MvlhNa8w"
      },
      "outputs": [],
      "source": [
        "#model.load_state_dict(torch.load('best_model_state.bin'))\n",
        "#model = model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9xikRdtRN1N"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76g7FV85H-T8"
      },
      "source": [
        "To reproduce the training procedure from the BERT paper, we'll use the [AdamW](https://huggingface.co/transformers/main_classes/optimizer_schedules.html#adamw) optimizer provided by Hugging Face. It corrects weight decay, so it's similar to the original paper. We'll also use a linear scheduler with no warmup steps:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "5v-ArJ2fCCcU"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 10\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=True)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=4,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8522g7JIu5J"
      },
      "source": [
        "How do we come up with all hyperparameters? The BERT authors have some recommendations for fine-tuning:\n",
        "\n",
        "- Batch size: 16, 32\n",
        "- Learning rate (Adam): 5e-5, 3e-5, 2e-5\n",
        "- Number of epochs: 2, 3, 4\n",
        "\n",
        "We're going to ignore the number of epochs recommendation but stick with the rest. Note that increasing the batch size reduces the training time significantly, but gives you lower accuracy.\n",
        "\n",
        "Let's continue with writing a helper function for training our model for one epoch:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "bzl9UhuNx1_Q"
      },
      "outputs": [],
      "source": [
        "def train_epoch(\n",
        "  model,\n",
        "  data_loader,\n",
        "  loss_fn,\n",
        "  optimizer,\n",
        "  device,\n",
        "  scheduler,\n",
        "  n_examples\n",
        "):\n",
        "  model = model.train()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  for d in data_loader:\n",
        "    input_ids = d[\"input_ids\"].to(device)\n",
        "    attention_mask = d[\"attention_mask\"].to(device)\n",
        "    targets = d[\"targets\"].to(device)\n",
        "\n",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, targets)\n",
        "\n",
        "    correct_predictions += torch.sum(preds == targets)\n",
        "    losses.append(loss.item())\n",
        "\n",
        "    loss.backward()\n",
        "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4PniYIte0fr"
      },
      "source": [
        "[link text](https://)Training the model should look familiar, except for two things. The scheduler gets called every time a batch is fed to the model. We're avoiding exploding gradients by clipping the gradients of the model using [clip_grad_norm_](https://pytorch.org/docs/stable/nn.html#clip-grad-norm).\n",
        "\n",
        "Let's write another one that helps us evaluate the model on a given data loader:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "CXeRorVGIKre"
      },
      "outputs": [],
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      loss = loss_fn(outputs, targets)\n",
        "\n",
        "      correct_predictions += torch.sum(preds == targets)\n",
        "      losses.append(loss.item())\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "s8OPBkz80x3s"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_rdSDBHhhCh"
      },
      "source": [
        "Using those two, we can write our training loop. We'll also store the training history:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zhHoFNsxufs",
        "outputId": "7a0a0963-5630-4567-b710-668be9f716ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "----------\n",
            "Train loss 1.0663634988996717 accuracy 0.42738012508686585\n",
            "Val   loss 0.9647856184414455 accuracy 0.5813953488372093\n",
            "\n",
            "Epoch 2/10\n",
            "----------\n",
            "Train loss 0.8415934218300714 accuracy 0.606671299513551\n",
            "Val   loss 0.6699234758104596 accuracy 0.7162790697674418\n",
            "\n",
            "Epoch 3/10\n",
            "----------\n",
            "Train loss 0.6609350929657618 accuracy 0.7206393328700487\n",
            "Val   loss 0.7638489944594247 accuracy 0.6744186046511628\n",
            "\n",
            "Epoch 4/10\n",
            "----------\n",
            "Train loss 0.551661037074195 accuracy 0.7748436414176512\n",
            "Val   loss 0.5719957947731018 accuracy 0.7627906976744185\n",
            "\n",
            "Epoch 5/10\n",
            "----------\n",
            "Train loss 0.42766963069637615 accuracy 0.8325225851285615\n",
            "Val   loss 0.6114666419369834 accuracy 0.772093023255814\n",
            "\n",
            "Epoch 6/10\n",
            "----------\n",
            "Train loss 0.37298129896322885 accuracy 0.8617095205003474\n",
            "Val   loss 0.6132709181734494 accuracy 0.7813953488372093\n",
            "\n",
            "Epoch 7/10\n",
            "----------\n",
            "Train loss 0.3274571248640617 accuracy 0.8742182070882557\n",
            "Val   loss 0.6189152012978282 accuracy 0.772093023255814\n",
            "\n",
            "Epoch 8/10\n",
            "----------\n",
            "Train loss 0.2877721581194136 accuracy 0.9027102154273801\n",
            "Val   loss 0.5477875231632164 accuracy 0.7953488372093023\n",
            "\n",
            "Epoch 9/10\n",
            "----------\n",
            "Train loss 0.2542581352715691 accuracy 0.9152189020152883\n",
            "Val   loss 0.6159784985440118 accuracy 0.827906976744186\n",
            "\n",
            "Epoch 10/10\n",
            "----------\n",
            "Train loss 0.2112835359449188 accuracy 0.9339819318971507\n",
            "Val   loss 0.5825120961027486 accuracy 0.8186046511627907\n",
            "\n",
            "CPU times: user 6min 19s, sys: 23.7 s, total: 6min 43s\n",
            "Wall time: 7min 11s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "\n",
        "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "  print('-' * 10)\n",
        "\n",
        "  train_acc, train_loss = train_epoch(\n",
        "    model,\n",
        "    train_data_loader,\n",
        "    loss_fn,\n",
        "    optimizer,\n",
        "    device,\n",
        "    scheduler,\n",
        "    len(df_train)\n",
        "  )\n",
        "\n",
        "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "\n",
        "  val_acc, val_loss = eval_model(\n",
        "    model,\n",
        "    val_data_loader,\n",
        "    loss_fn,\n",
        "    device,\n",
        "    len(df_val)\n",
        "  )\n",
        "\n",
        "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "  print()\n",
        "\n",
        "  history['train_acc'].append(train_acc)\n",
        "  history['train_loss'].append(train_loss)\n",
        "  history['val_acc'].append(val_acc)\n",
        "  history['val_loss'].append(val_loss)\n",
        "\n",
        "  if val_acc > best_accuracy:\n",
        "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "    best_accuracy = val_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "zoGUH8VZ-pPQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c3dabc2-5fb5-4380-8f09-4503d22b9b03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using a model of type distilbert to instantiate a model of type bert. This is not supported for all configurations of models and can yield errors.\n",
            "Some weights of BertModel were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['encoder.layer.0.attention.self.key.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.0.output.dense.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.1.output.dense.bias', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.3.output.dense.weight', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.4.intermediate.dense.bias', 'embeddings.word_embeddings.weight', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.4.output.dense.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.10.attention.self.query.bias', 'pooler.dense.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'embeddings.position_embeddings.weight', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.0.attention.self.query.bias', 'embeddings.token_type_embeddings.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.11.output.LayerNorm.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.2.output.dense.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.2.output.dense.weight', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.11.output.dense.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.10.output.dense.bias', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.11.output.dense.weight', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.2.attention.self.key.weight', 'pooler.dense.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.dense.weight', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.8.output.LayerNorm.bias', 'embeddings.LayerNorm.bias', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.10.attention.output.dense.bias', 'embeddings.LayerNorm.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.0.attention.output.LayerNorm.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model=ToxicClassifier(len(class_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "p0fuqatVwRYb"
      },
      "outputs": [],
      "source": [
        "  model.load_state_dict(torch.load('best_model_state.bin'))\n",
        "  model = model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3HZb3NWFtFf"
      },
      "source": [
        "## Evaluation\n",
        "\n",
        "So how good is our model on predicting sentiment? Let's start by calculating the accuracy on the test data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jS3gJ_qBEljD",
        "outputId": "b020af61-2bf6-43ad-a750-631b1afaca66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7835820895522387"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "test_acc, _ = eval_model(\n",
        "  model,\n",
        "  test_data_loader,\n",
        "  loss_fn,\n",
        "  device,\n",
        "  len(df_test)\n",
        ")\n",
        "\n",
        "test_acc.item()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdQ7-ylCj8Gd"
      },
      "source": [
        "The accuracy is about 1% lower on the test set. Our model seems to generalize well.\n",
        "\n",
        "We'll define a helper function to get the predictions from our model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "EgR6MuNS8jr_"
      },
      "outputs": [],
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "\n",
        "  toxic_comments = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  real_values = []\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "\n",
        "      texts = d[\"toxic_comments\"]\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      probs = F.softmax(outputs, dim=1)\n",
        "\n",
        "      toxic_comments.extend(texts)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(probs)\n",
        "      real_values.extend(targets)\n",
        "\n",
        "  predictions = torch.stack(predictions).cpu()\n",
        "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
        "  real_values = torch.stack(real_values).cpu()\n",
        "  return toxic_comments, predictions, prediction_probs, real_values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkbnBTI7kd_y"
      },
      "source": [
        "This is similar to the evaluation function, except that we're storing the text of the reviews and the predicted probabilities (by applying the softmax on the model outputs):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "zHdPZr60-0c_"
      },
      "outputs": [],
      "source": [
        "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVwoVij2lC7F"
      },
      "source": [
        "Let's have a look at the classification report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8a9_8-ND3Is",
        "outputId": "326a849b-f481-4374-d5d7-beb5c6acbc78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Trans     0.8615    0.7943    0.8266       141\n",
            "  Indigenous     0.6885    0.7119    0.7000       118\n",
            "    Migrated     0.7867    0.8252    0.8055       143\n",
            "\n",
            "    accuracy                         0.7811       402\n",
            "   macro avg     0.7789    0.7771    0.7773       402\n",
            "weighted avg     0.7841    0.7811    0.7819       402\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred, target_names=class_names,digits=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFAekw3mmWUi"
      },
      "source": [
        "Looks like it is really hard to classify neutral (3 stars) reviews. And I can tell you from experience, looking at many reviews, those are hard to classify.\n",
        "\n",
        "We'll continue with the confusion matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "6d1qxsc__DTh",
        "outputId": "699cf9b6-eb5a-49fc-aec0-53666966ea2e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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Pj6uuuio++uijuP3226v8b9APP/ww+vXrF8uWLSu1z5o1a+L999+PTz/9NO6///6UEjs//PDD6Nu3b6lJfbm5ufH444/HF198EY8//vgGxw8ApBdJAgBQhTbXmx3Dhw+P66+/vsSnMyMiVq5cGQ899FBMnjw5Bg8eHDVr1ix1rOeeey6uv/76Midz1pkzZ048/fTTccUVV0gSAH7V5s+fHxdffHHMmzcvsrKy4ogjjojddtst6tWrF7NmzYpXX3018RTfDz/8EDfccEMMGTKkzDHvueeepASBWrVqxeGHHx6dOnWKunXrxvfffx+vv/56zJs3L+6999648MILK/19/fe//42BAwcmJQh07NgxDj300GjevHksWrQoPvnkkxg9enR8/fXXceONN1Zo/DvuuCPp5natWrXi0EMPjU6dOkXjxo1j8eLF8d///jc++eSTiIj46aef4vTTT4+XXnop5dK9N910UyJBoEuXLnHggQdG8+bNIzc3Nz7++OPE2GvWrIlrr7029tlnn9h6661LHW/69Olx5plnJk2477jjjnHUUUdF69atY+nSpTFq1Kj4+OOPo6CgIAoKCuKuu+6KzMzMOPvssyv086kMK1asiD59+iTFu//++0fnzp1j6623jszMzMjNzY0ff/wxJkyYEOPHj0/pOx6AjdO9e/do1KhR/PLLLzF27NiYOXNmbLfddiX2LXo9cOyxx1bJ31bz58+Pc889NylBoHXr1nHMMcfE9ttvHytWrIhx48bFyJEjY968eXHFFVdE+/btK3yeJUuWRN++fWP+/PlRp06d6NatW3Tq1CkaNGgQ8+fPj/fff7/UxPj69evHvvvuG3vssUe0aNEi6tatG4sXL47x48fH+++/n5hUf/3116NFixYlVgbIyspKJMH99NNPiWoJzZo1K7W6XkmJ9CNHjozLL788qdrCfvvtFwcccEBkZ2fHypUrY/z48fH222/HypUrE0mLjz/+eHTu3LnUn8/EiRPj8ssvT0oQ2H333aN79+7RsmXLWLRoUbz33nvx1Vdfxbhx4+KGG24odSwAgKIkCQBAFanumx2lGTNmTDz88MORn58frVq1iiOOOCLatGkTGRkZMXny5Hj55ZcTTz6899578dRTT8VZZ51V4liTJk2Kv/71r4nJg9q1a0e3bt1ijz32iKZNm0ZhYWH88ssvMXXq1Pjyyy9jxowZFfgJAmy5/vOf/0RBQUEccsghcccddxSbwL7kkkvi8ssvT0xWjxw5MiZPnpxU+aWob775Jump/FatWsVDDz1UrH/fvn3jL3/5S4wYMSL+/ve/V+p7WrlyZVx33XWJm9+ZmZlx6623Ro8ePZL6XXDBBfH+++9Hv3794pVXXkl5Pft33303KUFg3333jbvuuitatWqV1O/888+Pzz77LPr27Ru5ubkxZ86cuPnmm+O+++4r9xw///xzPPfcc7HVVlvF/fffX6yE/9lnnx0vvfRSXH311VFYWBjLli2LJ554otTv2YKCghg4cGDShHvfvn3jkksuSXrf5557bowdOzYuvfTSRKWge+65Jw466KDYeeedy427Mr3++uvx008/RcTaykEPP/xwHHDAAaX2nzdvXvznP/+JRo0abaoQAdJSnTp14phjjolnn302CgsL4+WXX44+ffoU61dYWBivvPJKot2zZ88qiWfQoEFJ1ZBOP/30uOqqq4olJEybNi0uueSSmD59evzwww8VPs97770XERG77rprPPDAA8USIy6++OJix+y8885xwQUXxBFHHBH16tUrcdy5c+dG//79E+X5n3zyyTjllFOKfe8eeeSRceSRR0ZE8lIMV155ZUpLJEWsvb4YOHBg4hqpZcuWcd9998V+++1XrO9ll10WF198cfzvf/+L1atXx1/+8pd49dVXS3wfa9asieuuuy5WrFgREWuXqvrLX/4SZ555ZlK/Cy64IF566aW45ppr4tVXX0352gsASG+SBACgilT3zY7SPPjgg1FQUBAXXnhh9OnTp9hNnj/+8Y9xxhlnxKxZsyIi4pFHHonTTjstMjOLXzY8/vjjiadJ27RpE//4xz/KXMd6ypQpMWzYMDctgF+9goKC2HPPPePBBx8s8WmzOnXqxO233x6HH354LF26NCIiXnvttVKTBO67776khKy///3vsdNOOxXrl5WVFXfffXf8/PPPMX78+Ep8R2ufWiy63vFVV11VLEFgnW7dusVtt90W/fr1S+kp9Pz8/LjpppsS7T322CP++c9/lvpk5IEHHhj3339/nHfeeRER8cYbb8Qll1xS7ndhQUFBZGZmxt///vfYa6+9SuzTo0eP+Oyzz+Lll1+OiLWT6qUlCYwcOTJp2Z6zzjqrxAmdiLVPEw4ZMiTOOOOMKCgoiLy8vLjvvvviwQcfLDPmylZ0LePf//73ZSYIRKxdJzvVSkcAbJyTTjopnn322YiIeOmll+LSSy8tllg+atSoxER227Ztq2R5oWnTpsVrr72WaB955JFx3XXXldi3Xbt28dhjj8UJJ5wQy5cv36DzNWvWLB5//PGUqgK1adMmpSTEli1bxiOPPBInn3xyfP/991FYWBj//ve/S30fG+POO++MX375JSIiGjVqFE8//XSpS/Rst9128cQTT8Rxxx0XixcvjpkzZ8bw4cPjtNNOK9b3rbfeiokTJybaF110UbEEgXV69OgRS5Ysidtuu00FIAAgJe7QA0AVWnezo7QykUWtu9lxwgknlJogEPH/bna0bds2IiJxsyNVBQUFceaZZ8af/vSnEidftt1227jlllsS7QULFsR///vfEscqOtHw5z//ucwEgYiI9u3bx8033xxZWVkpxwuwpbruuutKTBBYp0mTJokn1yKi1En9nJyc+OyzzxLt0047rcQEgXVq164df/nLXzYg4rK98MILie22bduWeDO7qGOPPTb23XfflMYeMWJE5OTkRMTap+QGDRpUbunkrl27Rrdu3RLtdZMq5TnllFNKTRBYp1evXontefPmxc8//1xiv6Lfv82aNYvLL7+8zHH322+/pCc+P/jgg8T73lSKLoG0ww47bNJzA1C2jh07xo477hgRETNnzowvvviiWJ8XX3wxsV1VVQSef/75xHbNmjXLrVy33XbblVp9LhWXXnppyssG1a5dO+Wk8/r16yctv7RuSaHKlJOTE2+//XaifcUVV5SaILBOixYt4qKLLkq0S7uGKXrtlZ2dXeKDBkWdfvrpZV4jAgAUJUkAAKrQ5nizo0mTJtG/f/8y+xxwwAFJiQ2lTVwVnWho06ZNyjEA/Nql+mRfx44dE9vTp08vsc+HH36Y9ETYqaeeWu64nTp1qtQy9kuWLIlvvvkm0T7xxBOjZs2a5R6X6uTF66+/ntg+4IADUl7T+MQTT0xsjxo1KqVjUikdvOeeeyZ9J5f02axcuTJGjx6daB933HGlrl1c1B/+8IfE9po1a+Ljjz8u95jKVLdu3cT2uHHjNum5AShf0e/OogkBERHLli2Ld955JyIiatSokfQ9WJk+/fTTxPZvfvOb2Hbbbcs9JtXS/OurWbNmHHfccRt0bCqKVsz54YcfIjc3t1LHf+uttxLLDNSvXz9OOeWUlI4r+tlNnjw5Fi5cmPT6ihUrkq5tjj/++KhTp06ZY9asWXODPwcAIP1IEgCAKrK53uw4+uijkyYISlN0cqu0iaui43z99dcpnR8gHaRa+rdly5aJ7XXLDqxvwoQJie3s7Oxo165dSmMfdNBBKfVLRdFStxER+++/f0rHde7cOaV+RUv2H3zwwSnHVbQiwLRp02LZsmVl9q9Vq1bstttu5Y5bp06daNy4caK9roRwURMnTkxMCkSkHveee+6ZlEBY9PPdFHbdddfE9ksvvRQPP/xwrFy5cpPGAEDpTjzxxESi2ltvvZX0/+i33norUdL/wAMPjOzs7Eo//8qVK2PatGmJdqrf+dtvv/0GxdO2bduk79zK1qxZs8R2YWFhzJkzp1LH//LLLxPbXbp0KXcif52mTZtGq1atEu31rwe+/fbbxNJ+Ealf11Xm9R8A8OsmSQAAqsjmerOjMieuik40DBo0KN55550oLCxMaXyAX7PmzZun1K/o8jKlreM7Y8aMxHZFqgNUZiWBWbNmJbVTTVTYbrvtylxyISJi7ty5SU/PpTp2RPLPuaCgIObNm1dm/8aNG5cbzzr169dPbK9YsaLY6z/88ENSO9XqBxERHTp0SGwX/Xw3hZ49eyb9DO67777o2rVr9O/fP55//vli7wuATWvrrbdOJITn5uYmKgdEbJqlBnJycpKS4CryvVyRvuuksjRfacaPHx933HFHnHnmmXHIIYdEp06dYpdddokOHTok/u2+++5Jx1R2JYHJkycnttctCZiqotcx6/89v/71QarLCLRt2zalak8AAJnVHQAA/Fpt7M2ON998MyZOnBgzZsyIpUuXxooVK8qcgE/1ZkeqE1dFJ0dKm7jq3bt3jBkzJiLWJhL06dMntt122zjssMOiS5cusd9++6W83ALAr0mqT5GlouhT7FtttVXKx1Xm/3/Xf5K+UaNGKR1Xo0aNaNiwYbESukUtXrw4qX3BBRdUOL51Snriv6gN/VxK+v5d/1wV+XkX7VtezJWtdevWccstt8Q111yTmARaunRpvPbaa/Haa69FxNoJqq5du8YJJ5wQXbp02aTxARDRo0ePRMn/F198MY4//viYOXNmjB07NiIisrKy4vDDD6+Sc2/od35F+66TylI965s+fXpcf/31ib9FK2LVqlUVPqYsRa9j/vGPf8Q//vGPDRpn/Z/7+on6FVnGsGHDhsWurwAA1idJAACqyOZ6s6MyJ66OO+64GD16dPznP/9J7Pvpp5/iqaeeiqeeeioyMjJi5513jkMPPTR69OixQU+WAKS7ok+xp7JczDpFqxRsrNWrVye2MzMzE2WQU1Hek/ulVavZEJuybH7RBLrMzMyUKxREpFZBoir16NEjdtxxx7j77rtLvObIycmJ559/Pp5//vnYe++945ZbbqlQpQQANs4RRxwRWVlZkZubG//9739jzpw58eKLLyaS1o455pgKXRNURNHv/Ijyv8c3tO86mZkVuz09derUOP3002PRokXFXqtXr15kZWVFnTp1kq5Vfvzxx8R2ZVe+q6zKBOtfw6x/nVGRn1NV/W4AAL8ukgQAoIps6Tc7UnXzzTfHfvvtF0OGDClWoriwsDCmTJkSU6ZMiUcffTR+97vfxfXXX1+lyzAA/NoUnVCuyCR4SSXyN1RWVlZiOz8/P/Ly8qJ27dopHbts2bIyX18/mSE7O3uDE9oqMxGuPEUr7uTn58fq1atTnhwp+tkUHWdT2nvvvWPYsGExffr0+PDDD2P06NHx5ZdfFnvycNy4cdG7d+8YNmxY7LHHHtUSK0C6qVevXhx99NHx/PPPR0FBQbz00kvx0ksvJV6vqqUGIpK/8yMqlsxW3nf+xiosLIyBAwcm/mbOyMiIE088MY477rjYY489Sqy4VFhYGLvsskuVxVS3bt1EokCTJk02qJpCRPEqDOtfZ+Tn56d8j2FTJk0CAFsuSQIAsBnYHG92VMSJJ54YJ5xwQnz11VfxySefxOeffx7jxo1Lqm5QWFgYr732Wnz77bfx7LPPbvDNE4B0U/T/lyUlkpWmrBL/FbX+99DPP/8cO+ywQ7nH5ebmlvuEXZMmTZLad9xxR2It5s3Z+glvCxcujOzs7JSOLfrZVOb34YZMCuy4446x4447xtlnnx2FhYXx7bffxjvvvBPPP/98zJs3LyLWThBdd911SWthA1C1evbsGc8//3xERDzyyCOJCfgddtgh9t133yo77/rf+Tk5OSkfO2fOnMoOJ8nXX38d48ePT7RvvfXWOPnkk8s8pqqX9WnSpEniWucPf/hDXHHFFZUybsOGDZPaCxcujJYtW5Z7XF5eXqVWaQIAfr1SrxEJAFSZkm523HHHHXHwwQeXuv70pl7DuDwZGRmxzz77xGWXXRbDhg2LsWPHxuOPPx6nnnpqUrnD77//PgYPHlyNkQJsWdq0aZPY/t///pfycRXpW571S81/++23KR333XffldunZcuWSU/LVfUEQ2XZfvvtk9qTJ09O+diifYt+vkVtSAWJBQsWpBxDSTIyMmL33XePK664It5+++3o3Llz4rVvv/02pk2btlHjA5C6/fbbL/FdU/QJ/R49elTpeVu2bJn0N2iq3/l5eXkxderUqgorIiJGjRqV2G7btm25CQIREbNmzarKkGLHHXdMbFfmNcz61wep/my///77WLNmTaXFAQD8ekkSAIDNwOZ4s2Nj1a5dOw466KC45ZZb4rXXXovmzZsnXnv99derMTKALcuee+6Z2J4zZ07KE7WffvpppcXQrl27pPLDI0eOTOm4VPplZmbGPvvsk2iPGTOm4gFWg9133z2p7O8nn3yS0nHffPNNUiWBvfbaq8R+RZ8gnDt3bkpjf/311yn1S0X9+vXjmmuuSdonSQBg0zrxxBOT2hkZGVWeJBCxdkmadd57772Ulrb7+OOPkyrJVYWi34epVtUbPXp0yuMX/V4vKChI6ZiiCXVjx45N+Vzl2W233aJmzZqJdqrXdZV5/QcA/LpJEgCAzUBV3+yobtttt11ccMEFifb8+fOLrXkMQMkOOeSQqFHj//3ptq70cFnGjRtXqZUEatasGcccc0yiPWLEiJg5c2aZxyxcuDBeeOGFlMY/+uijE9tvvvlmpS6VUFXq1q0bv/nNbxLt1157LaW1mP/9738ntmvWrBkHH3xwif2KPkE4bty4cscdN25cTJ8+vdx+FbH+U4z5+fmVOj4AZevZs2fssssuiX8nnnhibLvttlV+3t/97neJ7ZycnHjllVfKPeaxxx6rypAiIpKSFVJJSMjPz49nn3025fGLVjYqb7mkdY488sjEddqPP/4YH374YcrnK0u9evWKXWfk5eWVeUxBQYGlgQCAlEkSAIDNQFXf7NgcrD/RoAQiQGq22WabOPDAAxPtp59+uswnulevXh233XZbpcfxhz/8ITIyMhLnGDBgQKll8PPz82PgwIEpr4l74oknRnZ2dkRELF++PK6++uoKf09U9dOLJendu3die8GCBXH//feX2f+rr75KSpzo1q1b4n2vr+hTnO+8806ZP8vVq1fHzTffnFLMs2fPTqlfRMSUKVOS2q1atUr5WAA2XqtWreLll19O/Lvjjjs2yXmPPPLIaNGiRaI9aNCgmDFjRqn9H3nkkfjyyy+rPK5tttkmsT127Nhyk/MeeOCBMuNeX9EEjFSTLdu0aRNHHHFEon3DDTfE/PnzUz5nROnXMEUrDObk5MQjjzxS5jjPPPNMpSaJAgC/bpIEAGAzUNU3O6rCihUrKvSkZ9GJhnr16iWtcwlA2fr165d4Sm3VqlVxwQUXFJvAjVi7ZvGVV14ZX3/9dVL1gcqw++67R69evRLtr776Knr16hWffPJJYkK/sLAwxo4dG6effnp88MEH0axZs2jSpEm5Y9euXTtuuOGGRBLChx9+GGeffXa5T8avWrUqRo4cGeedd148+uijG/7mNtDhhx8enTp1SrSffPLJGDJkSIklir/44ou45JJLEq/Vrl07rrjiilLHLvoU5+LFi2PgwIElPkG4cOHCuPjii2PChAmJn19ZzjzzzLj00kvj448/LjMRY86cOXHdddcl2i1atEha+gKAX6+6devG1VdfnWgvWrQoevfuHc8991wsX748sX/69OkxcODAuPfee6NGjRqx4447VmlcRZMmlyxZEgMHDixxgj0vLy/uvffeePjhhyt0PVQ0Qe+NN96Izz//PKXjrrrqqsT1zk8//RSnnnpquWX/CwoK4ssvv4xrrrkmLrnkkhL7HH300bH77rsn2kOGDImnn366xL6vvPJKDBo0KCKi0q8BAYBfp8zyuwAAVe3AAw+Me++9NyL+382Ou+66K+rUqZPULy8vLwYPHhyPPPJI1KhRI+V1EqvCggUL4rjjjotTTjklTj755Nh1111L7fvFF18kPfVw6KGHunEBUAF77LFH0kT4rFmz4uSTT44jjjgiOnbsGHXr1o3p06fHq6++GvPmzYuMjIy44IIL4uGHH67UOK666qr47rvvYvz48RERMXny5DjvvPOiTp060bRp01i8eHGsWLEiItaumzxo0KC48cYbE0vMlPX//sMOOyz69OkTDzzwQEREjBkzJo455pjYf//9Y999943s7OyoU6dOLF26NHJycmLSpEnx9ddfJyYrOnbsWKnvNRU1atSIQYMGRe/evWPJkiUREfG3v/0tXn/99TjqqKNi2223jdzc3Bg9enR89NFHSZPy/fv3j5133rnUsXfdddfo1q1bvP/++xGxtprAscceG8cdd1y0bt06VqxYEd9++228/fbbkZubGy1btoxu3bqVW2mosLAwRo4cGSNHjoytttoq9tlnn9htt92iWbNmUbdu3Vi8eHF888038e677yY+y4iIK6+80nc3QBo57rjj4osvvohnnnkmItYmrF177bVxww03RLNmzWLlypWJ776IiPPPPz/mzZuXSPCriu+MPffcM37zm9/EqFGjImLt8kcTJkyIY489Ntq0aRP5+fnx/fffxzvvvBM///xzRET06dMn/va3v6U0/nHHHRcPPPBArF69OpYvXx6nn356NG3aNJo3b570fv7+978nVQJq1apV3HPPPXHJJZfEqlWr4qeffopzzz03dt555zjwwANj++23j/r168eKFStiwYIFMXny5Pj6668TFQc6d+5cYjw1a9aMm2++OU477bRYsWJFFBYWxk033RQvvPBCHH744dGyZctYuHBhfPDBB/HFF19ERMQJJ5wQX3zxRYUqBwEA6UmSAABsBqr6ZkdVWbFiRQwbNiyGDRsW22+/fXTq1Cl22mmn2GqrrSIjIyPmzp0bY8eOjc8++yyxpEKdOnXi8ssvr9a4AbZE/fv3j/nz5yfWms3Ly4vXX389Xn/99aR+GRkZ0b9//9hrr70qPUmgQYMG8dhjj8VVV12VmLyOWPtE/7rvp3X97rjjjvjtb3+b9PR7VlZWmeP36dMnWrZsGTfffHPk5eVFYWFhjBkzJsaMGVNubNU1gb3jjjvGk08+Geeff37iZv+0adPiwQcfLLF/RkZGXHnllXH22WeXO/aNN94YU6dOjZkzZ0ZExMyZM+Ohhx4q1q958+bx4IMPxgcffFCh2BctWhTvvvtuvPvuu6X2ycjIiH79+kWPHj0qNDYAW77rr78+GjVqFI8++mgi0S0/Pz/mzJmT6JORkREXXXRRXHbZZXHllVcm9pf3nb+h7rzzzujdu3fiuuOnn36Kxx57rMS+PXv2jEsuuSTlv5tbtWoV1157bdx0002J97tw4cJiFfRWr15d7NiuXbvG0KFDo0+fPjFv3ryIWLtkQSrl/8u6htl9993j/vvvjz59+iSuqSZOnBgTJ04s1rdjx47x17/+NU444YRyzwkAIEkAADYTVXmzY1P48ccf48cffyyzT4MGDeKBBx6o8jKUAL9GGRkZcfvtt8e+++4b999/f+IGdFHt2rWLAQMGRLdu3WL06NFVEkfjxo3j4Ycfjg8//DBefPHFGD9+fMybNy+ysrJi2223jcMOOyxOOeWUxBN2v/zyS+LYhg0bljt+r169omvXrvHwww/HG2+8EUuXLi2171ZbbRUHHXRQ/O53v4tDDjlk49/cBtp1113jjTfeiAceeCCGDx9e4rJBNWrUiP333z+uvPLK2GuvvVIaNzs7O5555pm47bbb4q233kok3K2TmZkZ3bt3j2uvvTZatmyZUpLA9ddfH2+99VZ89tlnSZM8JcV74IEHRt++faulSgMA1W9dotgxxxwTzz33XHz66acxZ86cqFGjRmy99dax//77R69evWK33XaLiEiqLJDKd/6GyM7OjhdeeCFuvvnmGDFiRInV9XbYYYe4+OKLo2fPnhUe//e//31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\n"
          },
          "metadata": {
            "image/png": {
              "width": 1028,
              "height": 693
            }
          }
        }
      ],
      "source": [
        "def show_confusion_matrix(confusion_matrix):\n",
        "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  plt.ylabel('True Toxic')\n",
        "  plt.xlabel('Predicted Toxic');\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)\n",
        "show_confusion_matrix(df_cm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wx0U7oNsnZ3A"
      },
      "source": [
        "This confirms that our model is having difficulty classifying neutral reviews. It mistakes those for negative and positive at a roughly equal frequency.\n",
        "\n",
        "That's a good overview of the performance of our model. But let's have a look at an example from our test data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iANBiY3sLo-K"
      },
      "outputs": [],
      "source": [
        "idx = 40\n",
        "\n",
        "review_text = y_review_texts[idx]\n",
        "true_label = y_test[idx]\n",
        "pred_df = pd.DataFrame({\n",
        "  'class_names': class_names,\n",
        "  'values': y_pred_probs[idx]\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8D0rb1yfnv4",
        "outputId": "c9108ea1-163e-4e8e-8d43-6fc46bdc3d7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "এই দেশে গরুর কালা ভুনা খাইতে পারে,খুব আরামে আছে,আর কত থাকবা,এবার নিজ\n",
            "দেশে যাওয়ার চেষ্টা কর বাবুরা\n",
            "\n",
            "True Label: Migrated\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n\".join(wrap(review_text)))\n",
        "print()\n",
        "print(f'True Label: {class_names[true_label]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7hj_IZFnn2X"
      },
      "source": [
        "Now we can look at the confidence of each sentiment of our model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "qj4d8lZyMkhf",
        "outputId": "7681854e-ec6f-4a54-b27d-bc9e91aae102"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 1103,
              "height": 693
            }
          }
        }
      ],
      "source": [
        "sns.barplot(x='values', y='class_names', data=pred_df, orient='h')\n",
        "plt.ylabel('sentiment')\n",
        "plt.xlabel('probability')\n",
        "plt.xlim([0, 1]);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WL5pDmvFyaU"
      },
      "source": [
        "### Predicting on Raw Text\n",
        "\n",
        "Let's use our model to predict the sentiment of some raw text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QEPi7zQRsDhH"
      },
      "outputs": [],
      "source": [
        "Toxic_com = \"নুনু আছে?\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaN4RnqMnxYw"
      },
      "source": [
        "We have to use the tokenizer to encode the text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zA5Or4D2sLc9"
      },
      "outputs": [],
      "source": [
        "encoded_review = tokenizer.encode_plus(\n",
        "  Toxic_com,\n",
        "  max_length=MAX_LEN,\n",
        "  add_special_tokens=True,\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "et8xlDrKpH60"
      },
      "source": [
        "Let's get the predictions from our model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qr_t3rUksumr",
        "outputId": "dd74aef7-30f1-408c-83a1-c77cd98e0211"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Toxic text: নুনু আছে?\n",
            "Prediction  : Trans\n"
          ]
        }
      ],
      "source": [
        "input_ids = encoded_review['input_ids'].to(device)\n",
        "attention_mask = encoded_review['attention_mask'].to(device)\n",
        "\n",
        "output = model(input_ids, attention_mask)\n",
        "_, prediction = torch.max(output, dim=1)\n",
        "\n",
        "print(f'Toxic text: {Toxic_com}')\n",
        "print(f'Prediction  : {class_names[prediction]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "237R-qwsfDf5"
      },
      "outputs": [],
      "source": [
        "review_text = \"এগুলো খাওয়া হারাম?\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n_7J3N6SygO4"
      },
      "outputs": [],
      "source": [
        "encoded_review = tokenizer.encode_plus(\n",
        "  review_text,\n",
        "  max_length=MAX_LEN,\n",
        "  add_special_tokens=True,\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxwZKdfeygvY",
        "outputId": "7005e364-7c6f-4fa9-962b-f526d8febf48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Review text: এগুলো খাওয়া হারাম?\n",
            "Sentiment  : Indigenous\n"
          ]
        }
      ],
      "source": [
        "input_ids = encoded_review['input_ids'].to(device)\n",
        "attention_mask = encoded_review['attention_mask'].to(device)\n",
        "\n",
        "output = model(input_ids, attention_mask)\n",
        "_, prediction = torch.max(output, dim=1)\n",
        "\n",
        "print(f'Review text: {review_text}')\n",
        "print(f'Sentiment  : {class_names[prediction]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g7gyWUHph24e"
      },
      "outputs": [],
      "source": []
    }
  ],
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