{
  "cells": [
    {
      "metadata": {
        "id": "_Ly997V5FRvJ"
      },
      "cell_type": "markdown",
      "source": [
        "# Implementation of text classification with BERT\n",
        "\n",
        "\n",
        "Still Working on it.\n",
        "\n",
        "This notebook is based in this TensorFlow tutorial: [Classify text with BERT](https://www.tensorflow.org/tutorials/text/classify_text_with_bert)\n",
        "\n",
        "BERT [(article link)](https://arxiv.org/abs/1810.04805) and other Transformer encoder architectures have been wildly successful on a variety of tasks in NLP (natural language processing). They compute vector-space representations of natural language that are suitable for use in deep learning models.\n",
        "\n",
        "![](http://www.d2l.ai/_images/nlp-map-pretrain.svg)\n",
        "\n",
        "Source: http://www.d2l.ai/chapter_natural-language-processing-pretraining/index.html\n",
        "\n",
        "BERT models are usually pre-trained on a large corpus of text, then fine-tuned for specific tasks.\n",
        "\n",
        "In this notebook, I am going to use a pretreined BERT to compute vector-space representations of a hate speech dataset to feed two different downsteam Archtectures (CNN and MLP).\n",
        "\n",
        "Sentiment Analysis\n",
        "\n",
        "This notebook trains a sentiment analysis model to classify the [Hate Speech and Offensive Language Dataset]( https://www.kaggle.com/mrmorj/hate-speech-and-offensive-language-dataset) tweets in three classes:\n",
        " \n",
        "* 0 - hate speech \n",
        "* 1 - offensive language \n",
        "* 2 - neither as positive or negative"
      ]
    },
    {
      "metadata": {
        "id": "0E1ATVOAFRvL"
      },
      "cell_type": "markdown",
      "source": [
        "## Installing dependencies and importing packages"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "m_AYtaEKFRvN"
      },
      "cell_type": "code",
      "source": [
        "# A dependency of the preprocessing for BERT inputs\n",
        "!pip install -q tensorflow-text > /dev/null\n",
        "!pip install -q tf-models-official > /dev/null\n",
        "!pip install -q transformers > /dev/null"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# this jupyter was running in collab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEiku7sUB2Bo",
        "outputId": "94116144-847a-4d33-aa61-29ab3597b83e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "mehF1PbZFRvM"
      },
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "pP-P1A0nFRvO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "460653d2-1650-47c5-c756-3a7f5d4c8f05"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import GroupKFold\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import tqdm\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n",
        "import tensorflow.keras.backend as K\n",
        "from scipy.stats import spearmanr\n",
        "from math import floor, ceil\n",
        "from transformers import *\n",
        "\n",
        "np.set_printoptions(suppress=True)\n",
        "print(tf.__version__)\n",
        "\n",
        "import shutil\n",
        "\n",
        "from official.nlp import optimization  # to create AdamW optmizer\n",
        "\n",
        "tf.get_logger().setLevel('ERROR')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.11.0\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "cfU6jpbDFRvO"
      },
      "cell_type": "markdown",
      "source": [
        "## Reading and preparing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# replace username and key\n",
        "!KAGGLE_USERNAME=xxx KAGGLE_KEY=xxx kaggle datasets download -d mrmorj/hate-speech-and-offensive-language-dataset"
      ],
      "metadata": {
        "id": "6pnu6cmKMBCi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48abb781-4d94-4e40-ab0d-9411d1621189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hate-speech-and-offensive-language-dataset.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p /input/hate-speech-and-offensive-language-dataset/\n",
        "!unzip -o /content/hate-speech-and-offensive-language-dataset.zip -d /input/hate-speech-and-offensive-language-dataset/"
      ],
      "metadata": {
        "id": "COTWNFw3RYtq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f3174ce-c298-402c-ce35-040578b75fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/hate-speech-and-offensive-language-dataset.zip\n",
            "  inflating: /input/hate-speech-and-offensive-language-dataset/labeled_data.csv  \n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "rkrreVusFRvO"
      },
      "cell_type": "code",
      "source": [
        "PATH = '../input/hate-speech-and-offensive-language-dataset/'\n",
        "df = pd.read_csv(PATH+'labeled_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "TY4lZ3O5FRvO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1661a1f1-7490-4d15-f6e2-67d412a0fff3"
      },
      "cell_type": "code",
      "source": [
        "nRowsRead = None # specify 'None' if want to read whole file\n",
        "# labeled_data.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows\n",
        "df0 = pd.read_csv('../input/hate-speech-and-offensive-language-dataset/labeled_data.csv', delimiter=',', nrows = nRowsRead)\n",
        "df0.dataframeName = 'labeled_data.csv'\n",
        "nRow, nCol = df0.shape\n",
        "print('There are {} rows and {} columns'.format(nRow, nCol))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 24783 rows and 7 columns\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "fMTsmoD9FRvO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "957ed7d5-f5cc-45e8-d68c-33e74ff58af8"
      },
      "cell_type": "code",
      "source": [
        "df0.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Unnamed: 0  count  hate_speech  offensive_language  neither  class  \\\n",
              "0           0      3            0                   0        3      2   \n",
              "1           1      3            0                   3        0      1   \n",
              "2           2      3            0                   3        0      1   \n",
              "3           3      3            0                   2        1      1   \n",
              "4           4      6            0                   6        0      1   \n",
              "\n",
              "                                               tweet  \n",
              "0  !!! RT @mayasolovely: As a woman you shouldn't...  \n",
              "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...  \n",
              "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...  \n",
              "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
              "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...  "
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              "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "DYA58oAyFRvP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ef4e855b-051e-4a33-cb4e-c4989db11b2c"
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      "cell_type": "code",
      "source": [
        "#Doing some adjustments\n",
        "\n",
        "c=df0['class']\n",
        "df0.rename(columns={'tweet' : 'text',\n",
        "                   'class' : 'category'}, \n",
        "                    inplace=True)\n",
        "a=df0['text']\n",
        "b=df0['category'].map({0: 'hate_speech', 1: 'offensive_language',2: 'neither'})\n",
        "\n",
        "df= pd.concat([a,b,c], axis=1)\n",
        "df.rename(columns={'class' : 'label'}, \n",
        "                    inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text            category  \\\n",
              "0      !!! RT @mayasolovely: As a woman you shouldn't...             neither   \n",
              "1      !!!!! RT @mleew17: boy dats cold...tyga dwn ba...  offensive_language   \n",
              "2      !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...  offensive_language   \n",
              "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  offensive_language   \n",
              "4      !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...  offensive_language   \n",
              "...                                                  ...                 ...   \n",
              "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...  offensive_language   \n",
              "24779  you've gone and broke the wrong heart baby, an...             neither   \n",
              "24780  young buck wanna eat!!.. dat nigguh like I ain...  offensive_language   \n",
              "24781              youu got wild bitches tellin you lies  offensive_language   \n",
              "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...             neither   \n",
              "\n",
              "       label  \n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24778</th>\n",
              "      <td>you's a muthaf***in lie &amp;#8220;@LifeAsKing: @2...</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24779</th>\n",
              "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
              "      <td>neither</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24780</th>\n",
              "      <td>young buck wanna eat!!.. dat nigguh like I ain...</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24781</th>\n",
              "      <td>youu got wild bitches tellin you lies</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24782</th>\n",
              "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
              "      <td>neither</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24783 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52544abc-e459-440b-8ad5-4fa18d6b7474')\"\n",
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              "\n",
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              "          document.querySelector('#df-52544abc-e459-440b-8ad5-4fa18d6b7474 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-52544abc-e459-440b-8ad5-4fa18d6b7474');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "6JhjkvfrFRvP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "33953f56-8ff2-4eb6-d199-eb4fb8732ad2"
      },
      "cell_type": "code",
      "source": [
        "# Grouping data by label\n",
        "df.groupby('label').count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        text  category\n",
              "label                 \n",
              "0       1430      1430\n",
              "1      19190     19190\n",
              "2       4163      4163"
            ],
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-e3b18033-916f-4bcd-8c0c-060634f4b0f2 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3b18033-916f-4bcd-8c0c-060634f4b0f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "PS4dSjrvFRvP"
      },
      "cell_type": "markdown",
      "source": [
        "This is an unbalanced dataset. "
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "b9YYGHIZFRvP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08ec3b2e-2e67-4f02-89da-2ba0b76f85c2"
      },
      "cell_type": "code",
      "source": [
        "hate, ofensive, neither = np.bincount(df['label'])\n",
        "total = hate + ofensive + neither\n",
        "print('Examples:\\n    Total: {}\\n    hate: {} ({:.2f}% of total)\\n'.format(\n",
        "    total, hate, 100 * hate / total))\n",
        "print('Examples:\\n    Total: {}\\n    Ofensive: {} ({:.2f}% of total)\\n'.format(\n",
        "    total, ofensive, 100 * ofensive / total))\n",
        "print('Examples:\\n    Total: {}\\n    Neither: {} ({:.2f}% of total)\\n'.format(\n",
        "    total, neither, 100 * neither / total))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Examples:\n",
            "    Total: 24783\n",
            "    hate: 1430 (5.77% of total)\n",
            "\n",
            "Examples:\n",
            "    Total: 24783\n",
            "    Ofensive: 19190 (77.43% of total)\n",
            "\n",
            "Examples:\n",
            "    Total: 24783\n",
            "    Neither: 4163 (16.80% of total)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "DviRMu0yFRvQ"
      },
      "cell_type": "markdown",
      "source": [
        "### Splitting the data between train, validation and test sets:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "HGn5vsjHFRvQ"
      },
      "cell_type": "code",
      "source": [
        "X_train_, X_test, y_train_, y_test = train_test_split(\n",
        "    df.index.values,\n",
        "    df.label.values,\n",
        "    test_size=0.10,\n",
        "    random_state=42,\n",
        "    stratify=df.label.values,    \n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "nLFmmeBHFRvQ"
      },
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(\n",
        "    df.loc[X_train_].index.values,\n",
        "    df.loc[X_train_].label.values,\n",
        "    test_size=0.10,\n",
        "    random_state=42,\n",
        "    stratify=df.loc[X_train_].label.values,  \n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "MoWyqeZIFRvQ"
      },
      "cell_type": "code",
      "source": [
        "df['data_type'] = ['not_set']*df.shape[0]\n",
        "df.loc[X_train, 'data_type'] = 'train'\n",
        "df.loc[X_val, 'data_type'] = 'val'\n",
        "df.loc[X_test, 'data_type'] = 'test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "iahBQXCtFRvQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "441c5d31-e8d3-4fab-da3b-680c73b00ed8"
      },
      "cell_type": "code",
      "source": [
        "df.groupby(['category', 'label', 'data_type']).count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     text\n",
              "category           label data_type       \n",
              "hate_speech        0     test         143\n",
              "                         train       1158\n",
              "                         val          129\n",
              "neither            2     test         416\n",
              "                         train       3372\n",
              "                         val          375\n",
              "offensive_language 1     test        1920\n",
              "                         train      15543\n",
              "                         val         1727"
            ],
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              "                                                     [key], {});\n",
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              "                                                    text            category  \\\n",
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              "24781              youu got wild bitches tellin you lies  offensive_language   \n",
              "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...             neither   \n",
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              "<p>24783 rows × 4 columns</p>\n",
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    {
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        "df_train = df.loc[df[\"data_type\"]==\"train\"]\n",
        "df_train.head(5)"
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              "                                                text            category  \\\n",
              "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...  offensive_language   \n",
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              "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...  offensive_language   \n",
              "6  !!!!!!\"@__BrighterDays: I can not just sit up ...  offensive_language   \n",
              "\n",
              "   label data_type  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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        "df_val.head(5)"
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              "                                                 text            category  \\\n",
              "5   !!!!!!!!!!!!!!!!!!\"@T_Madison_x: The shit just...  offensive_language   \n",
              "27  \" i met that pussy on Ocean Dr . i gave that p...  offensive_language   \n",
              "31  \" i'd say im back to the old me but my old bit...  offensive_language   \n",
              "44     \" post a picture of that pussy get 200 likes \"  offensive_language   \n",
              "46        \" quick piece of pussy call it a drive by \"  offensive_language   \n",
              "\n",
              "    label data_type  \n",
              "5       1       val  \n",
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              "46      1       val  "
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>category</th>\n",
              "      <th>label</th>\n",
              "      <th>data_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>!!!!!!!!!!!!!!!!!!\"@T_Madison_x: The shit just...</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>val</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>\" i met that pussy on Ocean Dr . i gave that p...</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>val</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>\" i'd say im back to the old me but my old bit...</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>val</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>\" post a picture of that pussy get 200 likes \"</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>val</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>\" quick piece of pussy call it a drive by \"</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>val</td>\n",
              "    </tr>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "BK6-RPk6FRvR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0b2dcb3e-a65a-45bf-d9f9-46915e2a2409"
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      "cell_type": "code",
      "source": [
        "df_test = df.loc[df[\"data_type\"]==\"test\"]\n",
        "df_test.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 text            category  \\\n",
              "0   !!! RT @mayasolovely: As a woman you shouldn't...             neither   \n",
              "12  \" So hoes that smoke are losers ? \" yea ... go...  offensive_language   \n",
              "14                            \" bitch get up off me \"  offensive_language   \n",
              "17                          \" bitch who do you love \"  offensive_language   \n",
              "25     \" her pussy lips like Heaven doors \" &#128524;  offensive_language   \n",
              "\n",
              "    label data_type  \n",
              "0       2      test  \n",
              "12      1      test  \n",
              "14      1      test  \n",
              "17      1      test  \n",
              "25      1      test  "
            ],
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
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              "      <th>label</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
              "      <td>neither</td>\n",
              "      <td>2</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>\" So hoes that smoke are losers ? \" yea ... go...</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>\" bitch get up off me \"</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>\" bitch who do you love \"</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>\" her pussy lips like Heaven doors \" &amp;#128524;</td>\n",
              "      <td>offensive_language</td>\n",
              "      <td>1</td>\n",
              "      <td>test</td>\n",
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              "\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-3edbf648-630b-478a-b7e2-93933e190717');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "zcd2RCCnFRvR",
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        },
        "outputId": "e0c2384c-a60a-4f81-e347-2592993ac5cc"
      },
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text         object\n",
              "category     object\n",
              "label         int64\n",
              "data_type    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Ox7Yu6i2FRvR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "8890dc1d-3212-408f-a7e3-8cc96d08990f"
      },
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud, STOPWORDS\n",
        "stopwords = set(STOPWORDS)\n",
        "stopwords.add(\"RT\")\n",
        "\n",
        "print(type(STOPWORDS))\n",
        "\n",
        "import random\n",
        "\n",
        "def random_color_func(word=None, font_size=None, position=None,  orientation=None, font_path=None, random_state=None):\n",
        "    h = 344\n",
        "    s = int(100.0 * 255.0 / 255.0)\n",
        "    l = int(100.0 * float(random_state.randint(60, 120)) / 255.0)\n",
        "    return \"hsl({}, {}%, {}%)\".format(h, s, l)\n",
        "\n",
        "wordcloud = WordCloud(\n",
        "                          background_color='white',\n",
        "                          stopwords=stopwords,\n",
        "                          max_words=200,\n",
        "                          max_font_size=60, \n",
        "                          random_state=42\n",
        "                         ).generate(str(df.loc[df[\"category\"]==\"offensive_language\"].text))\n",
        "print(wordcloud)\n",
        "fig = plt.figure(1)\n",
        "plt.imshow(wordcloud.recolor(color_func= random_color_func, random_state=3),\n",
        "           interpolation=\"bilinear\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'set'>\n",
            "<wordcloud.wordcloud.WordCloud object at 0x7fbf35f4c370>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "AgmUhmCRFRvR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "05118b87-4197-4e43-db84-888c344a4a0a"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def random_color_func(word=None, font_size=None, position=None,  orientation=None, font_path=None, random_state=None):\n",
        "    h = 20\n",
        "    s = int(100.0 * 255.0 / 255.0)\n",
        "    l = int(100.0 * float(random_state.randint(60, 120)) / 255.0)\n",
        "    return \"hsl({}, {}%, {}%)\".format(h, s, l)\n",
        "\n",
        "wordcloud = WordCloud(\n",
        "                          background_color='white',\n",
        "                          stopwords=stopwords,\n",
        "                          max_words=200,\n",
        "                          max_font_size=60, \n",
        "                          random_state=42\n",
        "                         ).generate(str((df.loc[df[\"category\"]==\"neither\"].text)))\n",
        "print(wordcloud)\n",
        "fig = plt.figure(1)\n",
        "plt.imshow(wordcloud.recolor(color_func= random_color_func, random_state=3),\n",
        "           interpolation=\"bilinear\")\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<wordcloud.wordcloud.WordCloud object at 0x7fbf4b4d1520>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "-4qHG8GIFRvS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "a35d2749-018e-4bd0-8f03-0785c3ecab29"
      },
      "cell_type": "code",
      "source": [
        "stopwords.add(\"Name\")\n",
        "\n",
        "def random_color_func(word=None, font_size=None, position=None,  orientation=None, font_path=None, random_state=None):\n",
        "    h = 180\n",
        "    s = int(100.0 * 255.0 / 255.0)\n",
        "    l = int(100.0 * float(random_state.randint(60, 120)) / 255.0)\n",
        "    return \"hsl({}, {}%, {}%)\".format(h, s, l)\n",
        "\n",
        "wordcloud = WordCloud(\n",
        "                          background_color='white',\n",
        "                          stopwords=stopwords,\n",
        "                          max_words=200,\n",
        "                          max_font_size=60, \n",
        "                          random_state=42\n",
        "                         ).generate(str((df.loc[df[\"category\"]==\"hate_speech\"].text)))\n",
        "print(wordcloud)\n",
        "fig = plt.figure(1)\n",
        "plt.imshow(wordcloud.recolor(color_func= random_color_func, random_state=3),\n",
        "           interpolation=\"bilinear\")\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<wordcloud.wordcloud.WordCloud object at 0x7fbf18ae6e80>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CgC-Jdi5FRvS"
      },
      "cell_type": "markdown",
      "source": [
        "## Build TensorFlow input \n",
        "[Reference](https://www.tensorflow.org/guide/data)"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "7tFK36cYFRvS"
      },
      "cell_type": "code",
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((df_train.text.values, df_train.label.values))\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((df_val.text.values, df_val.label.values))\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((df_test.text.values, df_test.label.values))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "hb96Y5daFRvS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b226eacd-76f1-4a28-cd8f-41066de708b2"
      },
      "cell_type": "code",
      "source": [
        "train_ds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<TensorSliceDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "metadata": {
        "id": "jUBqYoupFRvS"
      },
      "cell_type": "markdown",
      "source": [
        "While tf.data tries to propagate shape information, the default settings of Dataset.batch result in an unknown batch size because the last batch may not be full. Note the Nones in the shape:\n",
        "\n",
        "batched_dataset\n",
        "```\n",
        "<BatchDataset shapes: ((None,), (None,)), types: (tf.int64, tf.int64)>\n",
        "```\n",
        "Use the drop_remainder argument to ignore that last batch, and get full shape propagation:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "c3cO60xrFRvS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b96cd584-30ce-43e8-db7a-b3846d422a53"
      },
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.shuffle(len(df_train)).batch(32, drop_remainder=False)\n",
        "train_ds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BatchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "2QZFSyghFRvT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ca268a5-f192-47fb-8f4f-5b88b492c249"
      },
      "cell_type": "code",
      "source": [
        "val_ds = val_ds.shuffle(len(df_val)).batch(32, drop_remainder=False)\n",
        "val_ds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BatchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "XgJHu9mvFRvT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fa30534-98f4-4b12-825e-aaf77ac6784f"
      },
      "cell_type": "code",
      "source": [
        "test_ds = test_ds.shuffle(len(df_test)).batch(32, drop_remainder=False)\n",
        "test_ds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BatchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "id": "1U3-aDTrFRvT"
      },
      "cell_type": "markdown",
      "source": [
        "# Printing some Tweets"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "uzEoGsXgFRvT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0488b9f5-f767-4d79-a013-50421a01292b"
      },
      "cell_type": "code",
      "source": [
        "for feat, targ in train_ds.take(1):\n",
        "  print ('Features: {}, Target: {}'.format(feat, targ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features: [b\"Chuck E Cheese isn't the same as when I was a kid. Chuck a skinny bitch &amp; they took away the ball pit. Man wtf\"\n",
            " b'All I ask for is love n loyalty. . nothing else. No arguing over other bitches/niggas , no he say she say , no social media bs... Just love'\n",
            " b'RT @IcySoleOnline: Should of been a cork sole RT @Johnny_Blaz3: why is there no cork on the WTL XI?'\n",
            " b\"I can't argue with a bitch over a nigga...\"\n",
            " b'@ArianaGrande this bitch @cumwiththewind'\n",
            " b'Any where the $$$ at is where the pussy go...'\n",
            " b'@_chrisbrownn14 fuck bitches get money'\n",
            " b'RT @RuNeshaShamia: Drunk inlove with my gun bae bitch Beyonc&#233; &#128520;&#128527;'\n",
            " b'#porn,#android,#iphone,#ipad,#sex,#xxx, | #CloseUp | cumshot on beauty pussy closeup http://t.co/UonsdrAuV0'\n",
            " b'Photo: Mustard on the beat hoe #wdywt http://t.co/DKYqcnRuNO'\n",
            " b\"@daggerbyte @MichaelSmartGuy @spacej_me @pizzahut Given the rickety slant of that roof I'd say more like Pizza Shanty\"\n",
            " b\"Don't throw me the pussy! Let me come get it\"\n",
            " b'&#8220;@_getMEAUXmoney: Nobody cares about KIRK Bosley no&#8221;I care bout my cousin hoe'\n",
            " b'RT @_MindAtEase: Fuck twerking bitch can you cook'\n",
            " b'Reminiscin on my swinger days, when I drove a Caddy and my bitch sported a finger wave'\n",
            " b'You just salty you ugly and white RT @JayWiz614: @ItsNotHarold they all some hoes'\n",
            " b'Bull color schemes?\\n\\nDULL, you stupid bitch. DUKE WEARS BOLD PASTEL COLORS.\\n\\nBull makes no sense.'\n",
            " b'She wanna talk shit.. but then have a friendship .. fuck you bitch and I hope u offended'\n",
            " b'&#8220;@2hood2bgud: @uce_INA &#128064;&#128066; a bitch is all ears...&#8221; My mama told me not to talk to bitches so...&#128564;&#128514;jk'\n",
            " b\"' I will never date a female with another niggah name on her lol . Wtf .\"\n",
            " b'RT @Mike_daniels_YG: My last bitch was 18...bitch came in da trap tryna Vine da Dope fuh likes...her funeral was 3 days after dat'\n",
            " b'Every gook in #LosAngeles should be deported or killed.'\n",
            " b\"RT @prime13_time: Words from jac.. You can't save every hoe.. You got to let them hurt.\"\n",
            " b'If my bitch wear a bacon bra....im taking that ass to funky town'\n",
            " b'RT @Maxicat: Charlie Rangel Re-Writes History: On GOP &#8220;They Think They Won The Civil War&#8221; http://t.co/moCeUBRUTf'\n",
            " b\"I ain't cutting my hair for u hoes at hc\"\n",
            " b'Breakfast fried chicken jerk chicken Tater tots white rice nd press yellow rice nd beans Mac nd cheese http://t.co/Usz8gJnZl0'\n",
            " b'RT @StevStiffler: If her bio says \"Only God can judge me\" she\\'s a hoe.'\n",
            " b\"Dudes proudly volunteer to be baby daddy #2,3,4 and 5.Whether they'll be a father or not all over some pussy property.\"\n",
            " b'Nyomi Banxxx and Skin Diamond the last of the good ebony porn bitches.'\n",
            " b'The hoes be rated E !'\n",
            " b'Look at this pussy @YatchakHannah http://t.co/cPyO5YP005'], Target: [1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 0 1 1 2 1 2 1 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "zNx1iVn2FRvT"
      },
      "cell_type": "markdown",
      "source": [
        "# Loading models from TensorFlow Hub"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "xDoQeIMIFRvT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2faf8e37-3719-406e-c7f1-538f71e3466d"
      },
      "cell_type": "code",
      "source": [
        "bert_model_name = 'small_bert/bert_en_uncased_L-4_H-512_A-8' \n",
        "#bert_model_name = 'bert_en_uncased_L-12_H-768_A-12'\n",
        "\n",
        "map_name_to_handle = {\n",
        "    'bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3',\n",
        "    'bert_en_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/3',\n",
        "    'bert_multi_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/3',\n",
        "    'small_bert/bert_en_uncased_L-2_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-768_A-12/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-128_A-2/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-256_A-4/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-512_A-8/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-768_A-12/1',\n",
        "    'albert_en_base':\n",
        "        'https://tfhub.dev/tensorflow/albert_en_base/2',\n",
        "    'electra_small':\n",
        "        'https://tfhub.dev/google/electra_small/2',\n",
        "    'electra_base':\n",
        "        'https://tfhub.dev/google/electra_base/2',\n",
        "    'experts_pubmed':\n",
        "        'https://tfhub.dev/google/experts/bert/pubmed/2',\n",
        "    'experts_wiki_books':\n",
        "        'https://tfhub.dev/google/experts/bert/wiki_books/2',\n",
        "    'talking-heads_base':\n",
        "        'https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_base/1',\n",
        "}\n",
        "\n",
        "map_model_to_preprocess = {\n",
        "    'bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'bert_en_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_cased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-2_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-4_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-6_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-8_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-10_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-128_A-2':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-256_A-4':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-512_A-8':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'small_bert/bert_en_uncased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'bert_multi_cased_L-12_H-768_A-12':\n",
        "        'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/1',\n",
        "    'albert_en_base':\n",
        "        'https://tfhub.dev/tensorflow/albert_en_preprocess/1',\n",
        "    'electra_small':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'electra_base':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'experts_pubmed':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'experts_wiki_books':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "    'talking-heads_base':\n",
        "        'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "}\n",
        "\n",
        "tfhub_handle_encoder = map_name_to_handle[bert_model_name]\n",
        "tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name]\n",
        "\n",
        "print(f'BERT model selected           : {tfhub_handle_encoder}')\n",
        "print(f'Preprocess model auto-selected: {tfhub_handle_preprocess}')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT model selected           : https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1\n",
            "Preprocess model auto-selected: https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "ZZ5KDC9DFRvU"
      },
      "cell_type": "markdown",
      "source": [
        "I've chosen \"bert_en_uncased_L-12_H-768_A-12\"\n",
        "\n",
        "This TF Hub model uses the implementation of BERT from the TensorFlow Models repository on GitHub at tensorflow/models/official/nlp/bert. It uses L=12 hidden layers (i.e., Transformer blocks), a hidden size of H=768, and A=12 attention heads."
      ]
    },
    {
      "metadata": {
        "id": "QxFlWyGSFRvU"
      },
      "cell_type": "markdown",
      "source": [
        "### The preprocessing model\n",
        "\n",
        "Text inputs need to be transformed to numeric token ids and arranged in several Tensors before being input to BERT. TensorFlow Hub provides a matching preprocessing model for each of the BERT models, which implements this transformation using TF ops from the TF.text library. Hence, It is not necessary to run pure Python code outside the TensorFlow model to preprocess text.\n",
        "\n",
        "The preprocessing model must be the one referenced by the documentation of the BERT model, which can be read at the URL printed above. For BERT models from the drop-down above, the preprocessing model is selected automatically."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "qK7tsj0NFRvU"
      },
      "cell_type": "code",
      "source": [
        "bert_preprocess_model = hub.KerasLayer(tfhub_handle_preprocess)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oHpgchbbFRvU"
      },
      "cell_type": "markdown",
      "source": [
        "Let's try the preprocessing model on some text and see the output:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "xVYTkEf4FRvU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c4ca530-4139-4683-cb31-67432c7cea5c"
      },
      "cell_type": "code",
      "source": [
        "for text_batch, label_batch in train_ds.take(1):\n",
        "  for i in range(1):\n",
        "    tweet = text_batch.numpy()[i]\n",
        "    print(f'Tweet: {text_batch.numpy()[i]}')\n",
        "    label = label_batch.numpy()[i]\n",
        "    print(f'Label : {label}')\n",
        "\n",
        "text_test = ['this is such an amazing movie!']\n",
        "text_test = [tweet]\n",
        "\n",
        "\n",
        "text_preprocessed = bert_preprocess_model(text_test)\n",
        "\n",
        "print(f'Keys       : {list(text_preprocessed.keys())}')\n",
        "print(f'Shape      : {text_preprocessed[\"input_word_ids\"].shape}')\n",
        "print(f'Word Ids   : {text_preprocessed[\"input_word_ids\"][0, :12]}')\n",
        "print(f'Input Mask : {text_preprocessed[\"input_mask\"][0, :12]}')\n",
        "print(f'Type Ids   : {text_preprocessed[\"input_type_ids\"][0, :12]}')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tweet: b'RT @memethagreat_: When you know you dealing with hoe &#128583;'\n",
            "Label : 1\n",
            "Keys       : ['input_word_ids', 'input_mask', 'input_type_ids']\n",
            "Shape      : (1, 128)\n",
            "Word Ids   : [  101 19387  1030  2033 11368  3270 17603  4017  1035  1024  2043  2017]\n",
            "Input Mask : [1 1 1 1 1 1 1 1 1 1 1 1]\n",
            "Type Ids   : [0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "rna5wcS1FRvV"
      },
      "cell_type": "code",
      "source": [
        "bert_model = hub.KerasLayer(tfhub_handle_encoder)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "AARRd8FwFRvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f86d474d-6f1e-4845-f03f-fd035c41b0e8"
      },
      "cell_type": "code",
      "source": [
        "bert_results = bert_model(text_preprocessed)\n",
        "\n",
        "print(f'Loaded BERT: {tfhub_handle_encoder}')\n",
        "print(f'Pooled Outputs Shape:{bert_results[\"pooled_output\"].shape}')\n",
        "print(f'Pooled Outputs Values:{bert_results[\"pooled_output\"][0, :12]}')\n",
        "print(f'Sequence Outputs Shape:{bert_results[\"sequence_output\"].shape}')\n",
        "print(f'Sequence Outputs Values:{bert_results[\"sequence_output\"][0, :12]}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded BERT: https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1\n",
            "Pooled Outputs Shape:(1, 512)\n",
            "Pooled Outputs Values:[ 0.70658135  0.9744342  -0.10705773  0.28930727  0.01819332  0.8509703\n",
            "  0.9979883  -0.99360496 -0.02548804 -0.997787    0.21190482 -0.6805903 ]\n",
            "Sequence Outputs Shape:(1, 128, 512)\n",
            "Sequence Outputs Values:[[ 0.6834601   0.48724097  0.6317546  ...  0.6032436   0.6348365\n",
            "   0.76171243]\n",
            " [-0.07604086  0.2479679   0.06419381 ...  0.2410545   0.9862181\n",
            "   0.6980718 ]\n",
            " [-0.35939074  0.75332546  0.3006973  ... -0.21788383 -0.40428442\n",
            "   1.4083261 ]\n",
            " ...\n",
            " [ 0.3959934   0.50379336  1.2242035  ... -0.22907454 -0.22878148\n",
            "  -0.3488557 ]\n",
            " [ 0.5115414  -0.6881789  -0.01754081 ... -1.9684261  -0.19548538\n",
            "   0.25261897]\n",
            " [ 0.5218599   0.8258102  -0.24864265 ... -0.7810521  -0.39489534\n",
            "   0.7557944 ]]\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "LhKlsuQoFRvV"
      },
      "cell_type": "markdown",
      "source": [
        "# Techniques to deal with unbalanced data"
      ]
    },
    {
      "metadata": {
        "id": "R9R4RbFkFRvV"
      },
      "cell_type": "markdown",
      "source": [
        "### Calculate class weights\n",
        "\n",
        "One of the goals is to identify hate speech, but we don't have very many of those samples to work with, so I would want to have the classifier heavily weight the few examples that are available. I am going to do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ChzMuXRhFRvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "426d049e-322a-46f4-fa38-d7ef66b8c7af"
      },
      "cell_type": "code",
      "source": [
        "weight_for_0 = (1 / hate)*(total)/3.0 \n",
        "weight_for_1 = (1 / ofensive)*(total)/3.0\n",
        "weight_for_2 = (1 / neither)*(total)/3.0\n",
        "\n",
        "\n",
        "class_weight = {0: weight_for_0, 1: weight_for_1, 2: weight_for_2}\n",
        "\n",
        "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
        "print('Weight for class 1: {:.2f}'.format(weight_for_1))\n",
        "print('Weight for class 2: {:.2f}'.format(weight_for_2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weight for class 0: 5.78\n",
            "Weight for class 1: 0.43\n",
            "Weight for class 2: 1.98\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "7sEfBfCDFRvV"
      },
      "cell_type": "markdown",
      "source": [
        "### Set the correct initial bias\n",
        "\n",
        "These initial guesses (for the bias) are not great. The dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines)). This can help with initial convergence.\n",
        "\n",
        "With the default bias initialization the loss should be about log(1/n_classes): math.log(3) = 1,098612"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "-pXwCoxfFRvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea66da54-f8d8-4329-c113-8dcd58c74ab4"
      },
      "cell_type": "code",
      "source": [
        "#initial_output_bias = np.array([3.938462, 6.535164, 5.])\n",
        "initial_output_bias = np.array([3.938462, 15, 5.])\n",
        "initial_output_bias "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.938462, 15.      ,  5.      ])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "Ey9BmFmpFRvW"
      },
      "cell_type": "markdown",
      "source": [
        "# BERT + MLP\n",
        "\n",
        "I am going to create a simple fine-tuned model, with the preprocessing model, the selected BERT model, one Dense and a Dropout layer."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "iMTAzNKyFRvW"
      },
      "cell_type": "code",
      "source": [
        "def build_classifier_model(output_bias=None):\n",
        "    if output_bias is not None:\n",
        "        output_bias = tf.keras.initializers.Constant(output_bias)\n",
        "        #print(output_bias)\n",
        "        \n",
        "    text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
        "    preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')\n",
        "    encoder_inputs = preprocessing_layer(text_input)\n",
        "    encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder')\n",
        "    outputs = encoder(encoder_inputs)\n",
        "    net = outputs['pooled_output']\n",
        "    net = tf.keras.layers.Dense(512, activation=\"relu\")(net)\n",
        "    net = tf.keras.layers.Dropout(0.2)(net)\n",
        "#   net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net)\n",
        "    net = tf.keras.layers.Dense(3, activation=\"softmax\", name='classifier', bias_initializer=output_bias)(net)\n",
        "    \n",
        "    return tf.keras.Model(text_input, net)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "fMXFr9ivFRvW"
      },
      "cell_type": "code",
      "source": [
        "classifier_model = build_classifier_model(output_bias=initial_output_bias)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_raw_result = classifier_model(tf.constant(text_test))\n",
        "print(tf.sigmoid(bert_raw_result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8rYeeX-DvMU",
        "outputId": "29ae9a1e-85cc-43b0-baac-e2198bcc1f43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([[0.50000215 0.731055   0.5000024 ]], shape=(1, 3), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ABU563dqFRvW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3183bc5b-2a3b-40a7-d8c0-62ea2de5d32a"
      },
      "cell_type": "code",
      "source": [
        "classifier_model.get_weights()[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.938462, 15.      ,  5.      ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Nhvy3UL6FRvW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc74203b-36c2-4ee3-f639-7d6e1cd5ec96"
      },
      "cell_type": "code",
      "source": [
        "classifier_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " text (InputLayer)              [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " preprocessing (KerasLayer)     {'input_word_ids':   0           ['text[0][0]']                   \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_type_ids':                                                \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_mask': (Non                                               \n",
            "                                e, 128)}                                                          \n",
            "                                                                                                  \n",
            " BERT_encoder (KerasLayer)      {'sequence_output':  28763649    ['preprocessing[0][0]',          \n",
            "                                 (None, 128, 512),                'preprocessing[0][1]',          \n",
            "                                 'default': (None,                'preprocessing[0][2]']          \n",
            "                                512),                                                             \n",
            "                                 'pooled_output': (                                               \n",
            "                                None, 512),                                                       \n",
            "                                 'encoder_outputs':                                               \n",
            "                                 [(None, 128, 512),                                               \n",
            "                                 (None, 128, 512),                                                \n",
            "                                 (None, 128, 512),                                                \n",
            "                                 (None, 128, 512)]}                                               \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 512)          262656      ['BERT_encoder[0][5]']           \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)            (None, 512)          0           ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            " classifier (Dense)             (None, 3)            1539        ['dropout_2[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 29,027,844\n",
            "Trainable params: 29,027,843\n",
            "Non-trainable params: 1\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Zo4kMYEFFRvW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "f8668dec-70cf-4f06-e4fe-a9170871c40d"
      },
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(classifier_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "lT8Wr1xrFRvW"
      },
      "cell_type": "code",
      "source": [
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "metrics = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n",
        "#metrics = tf.metrics.Accuracy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "EsNdj_pHFRvW"
      },
      "cell_type": "code",
      "source": [
        "epochs = 15\n",
        "steps_per_epoch = tf.data.experimental.cardinality(train_ds).numpy()\n",
        "num_train_steps = steps_per_epoch * epochs\n",
        "num_warmup_steps = int(0.1*num_train_steps)\n",
        "\n",
        "init_lr = 3e-5\n",
        "optimizer = optimization.create_optimizer(init_lr=init_lr,\n",
        "                                          num_train_steps=num_train_steps,\n",
        "                                          num_warmup_steps=num_warmup_steps,\n",
        "                                          optimizer_type='adamw')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs, steps_per_epoch, num_train_steps, num_warmup_steps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IADkXHnPMUFe",
        "outputId": "c9369505-6940-40ff-f883-c367904c01eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 628, 9420, 942)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "jU6g-j6RFRvW"
      },
      "cell_type": "code",
      "source": [
        "#  classifier_model.compile(optimizer=optimizer,\n",
        "#                           loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "#                           metrics=['accuracy'])\n",
        "classifier_model.compile(optimizer=optimizer,\n",
        "                         loss=loss,\n",
        "                         metrics=metrics)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "OPyseUFVFRvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7f798a4-22aa-440d-c195-e6a730221a47"
      },
      "cell_type": "code",
      "source": [
        "print(f'Training model with {tfhub_handle_encoder}')\n",
        "history = classifier_model.fit(x=train_ds,\n",
        "                               validation_data=val_ds,\n",
        "                               epochs=epochs,\n",
        "                               # The class weights go here\n",
        "                               class_weight=class_weight\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model with https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1\n",
            "Epoch 1/15\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/keras/backend.py:5585: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
            "  output, from_logits = _get_logits(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "628/628 [==============================] - 172s 260ms/step - loss: 1.9218 - accuracy: 0.6873 - val_loss: 0.4483 - val_accuracy: 0.8292\n",
            "Epoch 2/15\n",
            "628/628 [==============================] - 162s 258ms/step - loss: 0.5943 - accuracy: 0.8236 - val_loss: 0.4338 - val_accuracy: 0.8180\n",
            "Epoch 3/15\n",
            "628/628 [==============================] - 162s 258ms/step - loss: 0.4613 - accuracy: 0.8532 - val_loss: 0.3169 - val_accuracy: 0.8897\n",
            "Epoch 4/15\n",
            "628/628 [==============================] - 158s 252ms/step - loss: 0.4038 - accuracy: 0.8870 - val_loss: 0.3273 - val_accuracy: 0.8929\n",
            "Epoch 5/15\n",
            "628/628 [==============================] - 161s 256ms/step - loss: 0.3218 - accuracy: 0.9049 - val_loss: 0.3407 - val_accuracy: 0.8929\n",
            "Epoch 6/15\n",
            "628/628 [==============================] - 158s 252ms/step - loss: 0.2585 - accuracy: 0.9289 - val_loss: 0.3880 - val_accuracy: 0.8817\n",
            "Epoch 7/15\n",
            "628/628 [==============================] - 157s 250ms/step - loss: 0.1978 - accuracy: 0.9468 - val_loss: 0.4062 - val_accuracy: 0.9045\n",
            "Epoch 8/15\n",
            "628/628 [==============================] - 160s 255ms/step - loss: 0.1618 - accuracy: 0.9621 - val_loss: 0.4794 - val_accuracy: 0.8920\n",
            "Epoch 9/15\n",
            "628/628 [==============================] - 157s 250ms/step - loss: 0.1261 - accuracy: 0.9721 - val_loss: 0.5110 - val_accuracy: 0.9072\n",
            "Epoch 10/15\n",
            "628/628 [==============================] - 161s 257ms/step - loss: 0.1078 - accuracy: 0.9778 - val_loss: 0.6166 - val_accuracy: 0.8879\n",
            "Epoch 11/15\n",
            "628/628 [==============================] - 161s 256ms/step - loss: 0.0832 - accuracy: 0.9830 - val_loss: 0.6481 - val_accuracy: 0.9027\n",
            "Epoch 12/15\n",
            "628/628 [==============================] - 157s 250ms/step - loss: 0.0553 - accuracy: 0.9885 - val_loss: 0.7068 - val_accuracy: 0.9041\n",
            "Epoch 13/15\n",
            "628/628 [==============================] - 157s 251ms/step - loss: 0.0600 - accuracy: 0.9896 - val_loss: 0.7108 - val_accuracy: 0.9077\n",
            "Epoch 14/15\n",
            "628/628 [==============================] - 158s 251ms/step - loss: 0.0371 - accuracy: 0.9929 - val_loss: 0.7470 - val_accuracy: 0.9072\n",
            "Epoch 15/15\n",
            "628/628 [==============================] - 158s 251ms/step - loss: 0.0368 - accuracy: 0.9927 - val_loss: 0.7540 - val_accuracy: 0.9054\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "iyIkTCxlFRvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6e894a1-d161-41fc-81b7-52dfd6d6b7bd"
      },
      "cell_type": "code",
      "source": [
        "loss, accuracy = classifier_model.evaluate(test_ds)\n",
        "\n",
        "print(f'Loss: {loss}')\n",
        "print(f'Accuracy: {accuracy}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "78/78 [==============================] - 10s 125ms/step - loss: 0.7863 - accuracy: 0.8975\n",
            "Loss: 0.786299467086792\n",
            "Accuracy: 0.8975393176078796\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "9QxXXAbFFRvX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "5443a9d9-5a4d-493f-8768-5bfc75df1187"
      },
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "print(history_dict.keys())\n",
        "\n",
        "acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "# acc = history_dict['binary_accuracy']\n",
        "# val_acc = history_dict['val_binary_accuracy']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "fig = plt.figure(figsize=(12, 10))\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.subplot(2, 1, 1)\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "# plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(epochs, acc, 'r', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f73b440e280>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZlkpD3IwFRvX"
      },
      "cell_type": "markdown",
      "source": [
        "## Export for inference\n",
        "\n",
        "Now you just save your fine-tuned model for later use."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "z4F1ZgkyFRvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0ba59bb-add5-4657-efaf-1e8e6eb548d6"
      },
      "cell_type": "code",
      "source": [
        "dataset_name = 'mpl_hate_speech'\n",
        "saved_model_path = './{}_bert'.format(dataset_name.replace('/', '_'))\n",
        "\n",
        "classifier_model.save(saved_model_path, include_optimizer=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as restored_function_body, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 124). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "CO3dJu3oFRvX"
      },
      "cell_type": "markdown",
      "source": [
        "# Results for MLP"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "qlHgotskFRvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1913e081-e099-4716-c477-bf7293b6d0bf"
      },
      "cell_type": "code",
      "source": [
        "result =  classifier_model.predict(test_ds)\n",
        "print(result.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "78/78 [==============================] - 12s 141ms/step\n",
            "(2479, 3)\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "5HuQJ80PFRvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b30f604-919c-4011-ad3a-168cc4ca8853"
      },
      "cell_type": "code",
      "source": [
        "result[0:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00000052, 0.99999785, 0.00000165],\n",
              "       [0.00000052, 0.99998534, 0.00001417]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Pq6d4j90FRvX"
      },
      "cell_type": "code",
      "source": [
        "classes = np.argmax(result, axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AnDOh5iVFRvY"
      },
      "cell_type": "markdown",
      "source": [
        "### Doing predictions and saving to np.array"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "v3CveqdJFRvY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a798845d-e902-424d-a803-8ec8678e5866"
      },
      "cell_type": "code",
      "source": [
        "tweet = []\n",
        "test_labels = []\n",
        "predictions = []\n",
        "for tweet, labels in test_ds.take(-1):\n",
        "  tweet = tweet.numpy()\n",
        "  test_labels.append(labels.numpy())\n",
        "  predictions.append(classifier_model.predict(tweet))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 771ms/step\n",
            "1/1 [==============================] - 0s 84ms/step\n",
            "1/1 [==============================] - 0s 76ms/step\n",
            "1/1 [==============================] - 0s 83ms/step\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "1/1 [==============================] - 0s 88ms/step\n",
            "1/1 [==============================] - 0s 87ms/step\n",
            "1/1 [==============================] - 0s 77ms/step\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "1/1 [==============================] - 0s 75ms/step\n",
            "1/1 [==============================] - 0s 82ms/step\n",
            "1/1 [==============================] - 0s 75ms/step\n",
            "1/1 [==============================] - 0s 69ms/step\n",
            "1/1 [==============================] - 0s 76ms/step\n",
            "1/1 [==============================] - 0s 76ms/step\n",
            "1/1 [==============================] - 0s 72ms/step\n",
            "1/1 [==============================] - 0s 79ms/step\n",
            "1/1 [==============================] - 0s 87ms/step\n",
            "1/1 [==============================] - 0s 77ms/step\n",
            "1/1 [==============================] - 0s 88ms/step\n",
            "1/1 [==============================] - 0s 76ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
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            "1/1 [==============================] - 0s 87ms/step\n",
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            "1/1 [==============================] - 0s 76ms/step\n",
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            "1/1 [==============================] - 0s 72ms/step\n",
            "1/1 [==============================] - 0s 84ms/step\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "1/1 [==============================] - 0s 76ms/step\n",
            "1/1 [==============================] - 0s 76ms/step\n",
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            "1/1 [==============================] - 0s 75ms/step\n",
            "1/1 [==============================] - 0s 84ms/step\n",
            "1/1 [==============================] - 1s 722ms/step\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "tPtBYe6BFRvY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e947bd5-0087-4e8b-929c-3bc1b3cac4b6"
      },
      "cell_type": "code",
      "source": [
        "test_labels[0:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
              " array([1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 2, 0, 1, 1, 2, 1, 1, 1, 2, 1,\n",
              "        1, 1, 1, 0, 2, 1, 1, 2, 1, 1])]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "4JjivM3sFRvY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6afe955d-7767-4fe8-b9bc-e9e07ca7a16f"
      },
      "cell_type": "code",
      "source": [
        "predictions[0:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.00000006, 0.00000154, 0.99999845],\n",
              "        [0.00066261, 0.999332  , 0.00000539],\n",
              "        [0.00000088, 0.99999774, 0.00000145],\n",
              "        [0.00000096, 0.99999714, 0.00000188],\n",
              "        [0.0000005 , 0.99999845, 0.00000113],\n",
              "        [0.0000014 , 0.9999963 , 0.00000224],\n",
              "        [0.00000044, 0.9999976 , 0.00000186],\n",
              "        [0.0000008 , 0.99999714, 0.00000207],\n",
              "        [0.0026225 , 0.99736387, 0.00001364],\n",
              "        [0.00000101, 0.9999958 , 0.00000318],\n",
              "        [0.00032014, 0.9996766 , 0.00000333],\n",
              "        [0.00000168, 0.999997  , 0.0000013 ],\n",
              "        [0.00000011, 0.00000586, 0.99999404],\n",
              "        [0.9999267 , 0.00007198, 0.00000127],\n",
              "        [0.00000123, 0.99999475, 0.00000408],\n",
              "        [0.00000055, 0.99999774, 0.00000165],\n",
              "        [0.00121367, 0.9987759 , 0.00001046],\n",
              "        [0.00000089, 0.99999726, 0.00000187],\n",
              "        [0.00000229, 0.9999949 , 0.00000284],\n",
              "        [0.00000595, 0.9999914 , 0.00000268],\n",
              "        [0.00000382, 0.99999464, 0.00000154],\n",
              "        [0.00000107, 0.99999404, 0.00000488],\n",
              "        [0.0001139 , 0.9998839 , 0.00000215],\n",
              "        [0.00000049, 0.99999833, 0.00000116],\n",
              "        [0.99995697, 0.00004238, 0.0000006 ],\n",
              "        [0.00000162, 0.999995  , 0.00000328],\n",
              "        [0.00000084, 0.999995  , 0.00000414],\n",
              "        [0.00000209, 0.9999807 , 0.00001732],\n",
              "        [0.00000096, 0.99999726, 0.00000173],\n",
              "        [0.00000543, 0.99999213, 0.00000235],\n",
              "        [0.00000075, 0.9999963 , 0.00000301],\n",
              "        [0.00001862, 0.9999739 , 0.00000749]], dtype=float32),\n",
              " array([[0.00000042, 0.999998  , 0.00000155],\n",
              "        [0.00000426, 0.999992  , 0.0000037 ],\n",
              "        [0.00109527, 0.99889886, 0.00000587],\n",
              "        [0.00000035, 0.9999975 , 0.00000219],\n",
              "        [0.00000041, 0.00005752, 0.99994206],\n",
              "        [0.98130816, 0.01868972, 0.00000209],\n",
              "        [0.0293135 , 0.9698654 , 0.00082111],\n",
              "        [0.00062872, 0.99937004, 0.00000125],\n",
              "        [0.00000103, 0.9999974 , 0.00000156],\n",
              "        [0.00000042, 0.99999774, 0.0000019 ],\n",
              "        [0.00000089, 0.999998  , 0.00000118],\n",
              "        [0.00000068, 0.999998  , 0.00000129],\n",
              "        [0.00000168, 0.00001529, 0.9999831 ],\n",
              "        [0.00292425, 0.99706656, 0.00000921],\n",
              "        [0.00000192, 0.9999969 , 0.00000116],\n",
              "        [0.9542036 , 0.04579007, 0.0000064 ],\n",
              "        [0.00000013, 0.00000313, 0.9999968 ],\n",
              "        [0.00000696, 0.9999907 , 0.00000241],\n",
              "        [0.00000062, 0.9999974 , 0.00000208],\n",
              "        [0.00002774, 0.99997103, 0.00000118],\n",
              "        [0.00063122, 0.00601086, 0.99335796],\n",
              "        [0.00000099, 0.9999975 , 0.00000158],\n",
              "        [0.00000068, 0.9999926 , 0.00000664],\n",
              "        [0.00000638, 0.9958839 , 0.00410963],\n",
              "        [0.0000165 , 0.9999771 , 0.00000648],\n",
              "        [0.99997306, 0.00002616, 0.00000084],\n",
              "        [0.00000027, 0.00000786, 0.9999919 ],\n",
              "        [0.00000057, 0.99999666, 0.00000272],\n",
              "        [0.00000023, 0.9999944 , 0.00000539],\n",
              "        [0.00000068, 0.00015671, 0.99984264],\n",
              "        [0.0000865 , 0.9999057 , 0.00000774],\n",
              "        [0.00000049, 0.99999785, 0.00000163]], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "MHWLNEejFRvY"
      },
      "cell_type": "code",
      "source": [
        "from itertools import chain\n",
        "flatten_list = list(chain.from_iterable(predictions))\n",
        "y_pred = np.argmax(flatten_list, axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "WUzvFYplFRvY"
      },
      "cell_type": "code",
      "source": [
        "y_test = np.array(list(chain.from_iterable(test_labels)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OmMXdwFlFRvY"
      },
      "cell_type": "markdown",
      "source": [
        "# Confusion Matrix MLP"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "o0zYKNYvFRvY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78f0a33d-93a4-4ed3-ed7c-fbb41b3dc4e5"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  50,   80,   13],\n",
              "       [  71, 1817,   32],\n",
              "       [   5,   53,  358]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save model"
      ],
      "metadata": {
        "id": "8g9WssqUBkuQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_model.save('/content/drive/MyDrive/AI/hate_speech/classifier_model.h5', save_format='h5')"
      ],
      "metadata": {
        "id": "AKohkdFlOq8g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## load manual with weight\n",
        "# bert_model_name = 'small_bert/bert_en_uncased_L-4_H-512_A-8'\n",
        "# map_name_to_handle = {\n",
        "#     'small_bert/bert_en_uncased_L-4_H-512_A-8':\n",
        "#         'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1',\n",
        "# }\n",
        "\n",
        "# map_model_to_preprocess = {\n",
        "#     'small_bert/bert_en_uncased_L-4_H-512_A-8':\n",
        "#         'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1',\n",
        "# }\n",
        "\n",
        "# tfhub_handle_encoder = map_name_to_handle[bert_model_name]\n",
        "# tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name]\n",
        "\n",
        "\n",
        "# initial_output_bias = np.array([3.938462, 15, 5.])\n",
        "\n",
        "\n",
        "# def build_classifier_model(output_bias=None):\n",
        "#     if output_bias is not None:\n",
        "#         output_bias = tf.keras.initializers.Constant(output_bias)\n",
        "        \n",
        "#     text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
        "#     preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')\n",
        "#     encoder_inputs = preprocessing_layer(text_input)\n",
        "#     encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder')\n",
        "#     outputs = encoder(encoder_inputs)\n",
        "#     net = outputs['pooled_output']\n",
        "#     net = tf.keras.layers.Dense(512, activation=\"relu\")(net)\n",
        "#     net = tf.keras.layers.Dropout(0.2)(net)\n",
        "#     net = tf.keras.layers.Dense(3, activation=\"softmax\", name='classifier', bias_initializer=output_bias)(net)\n",
        "#     return tf.keras.Model(text_input, net)\n",
        "\n",
        "\n",
        "# ## compile\n",
        "# epochs = 15\n",
        "# steps_per_epoch = 628\n",
        "# num_train_steps = steps_per_epoch * epochs\n",
        "# num_warmup_steps = int(0.1*num_train_steps)\n",
        "# init_lr = 3e-5\n",
        "\n",
        "# optimizer = optimization.create_optimizer(init_lr=init_lr,\n",
        "#                                           num_train_steps=num_train_steps,\n",
        "#                                           num_warmup_steps=num_warmup_steps,\n",
        "#                                           optimizer_type='adamw')\n",
        "# loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "# metrics = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n",
        "\n",
        "\n",
        "# classifier_model = build_classifier_model(output_bias=initial_output_bias)\n",
        "\n",
        "# classifier_model.compile(optimizer=optimizer,\n",
        "#                          loss=loss,\n",
        "#                          metrics=metrics)\n",
        "\n",
        "# classifier_model.load_weights(checkpoint_path)\n"
      ],
      "metadata": {
        "id": "Ugop_mNNtLL8"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}