Fine_Tune_BERT_for_Text_Classification_with_TensorFlow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGCJYkQj_Uu2"
      },
      "source": [
        "<h2 align=center> Fine-Tune BERT for Text Classification with TensorFlow</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4y2m1S6e12il"
      },
      "source": [
        "<div align=\"center\">\n",
        "    <img width=\"512px\" src='https://drive.google.com/uc?id=1fnJTeJs5HUpz7nix-F9E6EZdgUflqyEu' />\n",
        "    <p style=\"text-align: center;color:gray\">Figure 1: BERT Classification Model</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYYYWqWr_WCC"
      },
      "source": [
        "In this [project](https://www.coursera.org/projects/fine-tune-bert-tensorflow/), you will learn how to fine-tune a BERT model for text classification using TensorFlow and TF-Hub."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yQG5PCO_WFx"
      },
      "source": [
        "The pretrained BERT model used in this project is [available](https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2) on [TensorFlow Hub](https://tfhub.dev/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7pKNS21u_WJo"
      },
      "source": [
        "### Learning Objectives"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3NHSMXv_WMv"
      },
      "source": [
        "By the time you complete this project, you will be able to:\n",
        "\n",
        "- Build TensorFlow Input Pipelines for Text Data with the [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API\n",
        "- Tokenize and Preprocess Text for BERT\n",
        "- Fine-tune BERT for text classification with TensorFlow 2 and [TF Hub](https://tfhub.dev)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6BEe-3-AVRQ"
      },
      "source": [
        "### Prerequisites"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sc9f-8rLAVUS"
      },
      "source": [
        "In order to be successful with this project, it is assumed you are:\n",
        "\n",
        "- Competent in the Python programming language\n",
        "- Familiar with deep learning for Natural Language Processing (NLP)\n",
        "- Familiar with TensorFlow, and its Keras API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYXXV5n3Ab-4"
      },
      "source": [
        "### Contents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhK-SYGyAjxe"
      },
      "source": [
        "This project/notebook consists of several Tasks.\n",
        "\n",
        "- **[Task 1]()**: Introduction to the Project.\n",
        "- **[Task 2]()**: Setup your TensorFlow and Colab Runtime\n",
        "- **[Task 3]()**: Download and Import the Quora Insincere Questions Dataset\n",
        "- **[Task 4]()**: Create tf.data.Datasets for Training and Evaluation\n",
        "- **[Task 5]()**: Download a Pre-trained BERT Model from TensorFlow Hub\n",
        "- **[Task 6]()**: Tokenize and Preprocess Text for BERT\n",
        "- **[Task 7]()**: Wrap a Python Function into a TensorFlow op for Eager Execution\n",
        "- **[Task 8]()**: Create a TensorFlow Input Pipeline with `tf.data`\n",
        "- **[Task 9]()**: Add a Classification Head to the BERT `hub.KerasLayer`\n",
        "- **[Task 10]()**: Fine-Tune BERT for Text Classification\n",
        "- **[Task 11]()**: Evaluate the BERT Text Classification Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaArqXjRAcBa"
      },
      "source": [
        "## Task 2: Setup your TensorFlow and Colab Runtime."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDDhjzZ5A4Q_"
      },
      "source": [
        "You will only be able to use the Colab Notebook after you save it to your Google Drive folder. Click on the File menu and select “Save a copy in Drive…\n",
        "\n",
        "![Copy to Drive](https://drive.google.com/uc?id=1CH3eDmuJL8WR0AP1r3UE6sOPuqq8_Wl7)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpe6GhLuBJWB"
      },
      "source": [
        "### Check GPU Availability\n",
        "\n",
        "Check if your Colab notebook is configured to use Graphical Processing Units (GPUs). If zero GPUs are available, check if the Colab notebook is configured to use GPUs (Menu > Runtime > Change Runtime Type).\n",
        "\n",
        "![Hardware Accelerator Settings](https://drive.google.com/uc?id=1qrihuuMtvzXJHiRV8M7RngbxFYipXKQx)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8V9c8vzSL3aj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb8c0deb-a9b9-4b0b-a248-7bf1227e306d"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Oct 15 10:17:51 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Obch3rAuBVf0"
      },
      "source": [
        "### Install TensorFlow and TensorFlow Model Garden"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUQEY3dFB0jX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f759a43-c793-4883-997c-4e566c49d9d0"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.version.VERSION)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aU3YLZ1TYKUt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd758104-fde4-486a-f625-0aeb82e59987"
      },
      "source": [
        "!pip install -q tensorflow==2.3.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow==2.3.0 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.11.1, 2.12.0rc0, 2.12.0rc1, 2.12.0, 2.12.1, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow==2.3.0\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFRTC-zwUy6D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20dc8b1a-bcf1-45f1-f3a8-f35ce1fbf0f3"
      },
      "source": [
        "!git clone --depth 1 -b v2.3.0 https://github.com/tensorflow/models.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 2650, done.\u001b[K\n",
            "remote: Counting objects: 100% (2650/2650), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2311/2311), done.\u001b[K\n",
            "remote: Total 2650 (delta 505), reused 1389 (delta 306), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (2650/2650), 34.02 MiB | 32.19 MiB/s, done.\n",
            "Resolving deltas: 100% (505/505), done.\n",
            "Note: switching to '400d68abbccda2f0f6609e3a924467718b144233'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by switching back to a branch.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -c with the switch command. Example:\n",
            "\n",
            "  git switch -c <new-branch-name>\n",
            "\n",
            "Or undo this operation with:\n",
            "\n",
            "  git switch -\n",
            "\n",
            "Turn off this advice by setting config variable advice.detachedHead to false\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3H2G0571zLLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "099e62ef-03dc-46b0-db0b-54f0152c7244"
      },
      "source": [
        "# install requirements to use tensorflow/models repository\n",
        "!pip install -Uqr models/official/requirements.txt\n",
        "# you may have to restart the runtime afterwards"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.6/12.6 MB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m220.4/220.4 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m64.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m32.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.6/283.6 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.2/241.2 kB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m612.1/612.1 kB\u001b[0m \u001b[31m24.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m63.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.8/341.8 kB\u001b[0m \u001b[31m22.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cupy-cuda11x 11.0.0 requires numpy<1.26,>=1.20, but you have numpy 1.26.1 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.1 which is incompatible.\n",
            "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.26.1 which is incompatible.\n",
            "tensorflow 2.13.0 requires numpy<=1.24.3,>=1.22, but you have numpy 1.26.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVjksk4yCXur"
      },
      "source": [
        "## Restart the Runtime\n",
        "\n",
        "**Note**\n",
        "After installing the required Python packages, you'll need to restart the Colab Runtime Engine (Menu > Runtime > Restart runtime...)\n",
        "\n",
        "![Restart of the Colab Runtime Engine](https://drive.google.com/uc?id=1xnjAy2sxIymKhydkqb0RKzgVK9rh3teH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMsEoT3Fg4Wg"
      },
      "source": [
        "## Task 3: Download and Import the Quora Insincere Questions Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmqEylyFYTdP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8833d93-43a5-4217-fada-1b47792fa913"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import sys\n",
        "sys.path.append('models')\n",
        "from official.nlp.data import classifier_data_lib\n",
        "from official.nlp.bert import tokenization\n",
        "from official.nlp import optimization"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuX1lB8pPJ-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "babe1dd6-3ce1-493c-b42f-8b4d37a21150"
      },
      "source": [
        "print(\"TF Version: \", tf.__version__)\n",
        "print(\"Eager mode: \", tf.executing_eagerly())\n",
        "print(\"Hub version: \", hub.__version__)\n",
        "print(\"GPU is\", \"available\" if tf.config.experimental.list_physical_devices(\"GPU\") else \"NOT AVAILABLE\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TF Version:  2.13.0\n",
            "Eager mode:  True\n",
            "Hub version:  0.15.0\n",
            "GPU is available\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QtbwpWgyEZg7"
      },
      "source": [
        "A downloadable copy of the [Quora Insincere Questions Classification data](https://www.kaggle.com/c/quora-insincere-questions-classification/data) can be found [https://archive.org/download/fine-tune-bert-tensorflow-train.csv/train.csv.zip](https://archive.org/download/fine-tune-bert-tensorflow-train.csv/train.csv.zip). Decompress and read the data into a pandas DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nI-9itVwCCQ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeHE98KiMvDd"
      },
      "source": [
        "df = pd.read_csv('https://archive.org/download/fine-tune-bert-tensorflow-train.csv/train.csv.zip',\n",
        "                 compression = 'zip', low_memory=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leRFRWJMocVa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "3c950a7d-5f3b-466d-aeff-c9f45e801262"
      },
      "source": [
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1306122, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    qid                                      question_text  \\\n",
              "0  00002165364db923c7e6  How did Quebec nationalists see their province...   \n",
              "1  000032939017120e6e44  Do you have an adopted dog, how would you enco...   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.target.plot(kind='hist', title=\"Target Distribution\")"
      ],
      "metadata": {
        "id": "Hx3mz4Hc3J4G",
        "outputId": "6f3d2fdb-d122-455f-854b-6185c23325ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Target Distribution'}, ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELjswHcFHfp3"
      },
      "source": [
        "## Task 4: Create tf.data.Datasets for Training and Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fScULIGPwuWk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c02f74a5-eafe-43aa-8b50-d879a96c2f57"
      },
      "source": [
        "train_df, remaining = train_test_split(df, random_state = 42, train_size = 0.0075, stratify = df['target'])\n",
        "val_df, _ = train_test_split(remaining, random_state=42, train_size = 0.00075, stratify = remaining['target'])\n",
        "\n",
        "print(train_df.shape, val_df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9795, 3) (972, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQYMGT5_qLPX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "827f400d-fb2d-4da4-8216-84a2824fac77"
      },
      "source": [
        "with tf.device('/cpu:0'):\n",
        "  train_data = tf.data.Dataset.from_tensor_slices((train_df['question_text'].values, train_df['target'].values))\n",
        "  val_data = tf.data.Dataset.from_tensor_slices((val_df['question_text'].values, val_df['target'].values))\n",
        "\n",
        "  for text, label in train_data.take(2):\n",
        "    print(f\"text: {text}\\nlabel: {label}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text: b'Why are unhealthy relationships so desirable?'\n",
            "label: 0\n",
            "text: b'Which war changed the course of history of the world?'\n",
            "label: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2-ReN88Hvy_"
      },
      "source": [
        "## Task 5: Download a Pre-trained BERT Model from TensorFlow Hub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMb5M86b4-BU"
      },
      "source": [
        "\"\"\"\n",
        "Each line of the dataset is composed of the review text and its label\n",
        "- Data preprocessing consists of transforming text to BERT input features:\n",
        "input_word_ids, input_mask, segment_ids\n",
        "- In the process, tokenizing the text is done with the provided BERT model tokenizer\n",
        "\"\"\"\n",
        "\n",
        "# Label categories\n",
        "label_list = [0,1]\n",
        "# maximum length of (token) input sequences\n",
        "max_seq_length = 128\n",
        "train_batch_size = 32\n",
        "\n",
        "# Get BERT layer and tokenizer:\n",
        "# More details here: https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2\n",
        "\n",
        "bert_layer = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2\",trainable=True)\n",
        "vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\n",
        "do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\n",
        "tokenizer = tokenization.FullTokenizer(vocab_file,do_lower_case)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEUezMK-zkkI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18586270-aa16-4696-bacd-63fc0316c00e"
      },
      "source": [
        "tokenizer.wordpiece_tokenizer.tokenize('looks like you have some problems')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['looks', 'like', 'you', 'have', 'some', 'problems']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AFsmTO5JSmc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d72d5266-39f4-48d4-ba69-7e8f4df3452d"
      },
      "source": [
        "tokenizer.convert_tokens_to_ids(tokenizer.wordpiece_tokenizer.tokenize('looks like you have some problems'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3504, 2066, 2017, 2031, 2070, 3471]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QinzNq6OsP1"
      },
      "source": [
        "## Task 6: Tokenize and Preprocess Text for BERT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3FTqJ698zZ1e"
      },
      "source": [
        "<div align=\"center\">\n",
        "    <img width=\"512px\" src='https://drive.google.com/uc?id=1-SpKFELnEvBMBqO7h3iypo8q9uUUo96P' />\n",
        "    <p style=\"text-align: center;color:gray\">Figure 2: BERT Tokenizer</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWYkggYe6HZc"
      },
      "source": [
        "We'll need to transform our data into a format BERT understands. This involves two steps. First, we create InputExamples using `classifier_data_lib`'s constructor `InputExample` provided in the BERT library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-21A5aNJM0W"
      },
      "source": [
        "# This provides a function to convert row to input features and label\n",
        "\n",
        "def to_feature(text, label, label_list=label_list, max_seq_length=max_seq_length, tokenizer=tokenizer):\n",
        "  example = classifier_data_lib.InputExample(guid = None,\n",
        "                                             text_a = text.numpy(),\n",
        "                                             text_b = None,\n",
        "                                             label = label.numpy())\n",
        "  feature = classifier_data_lib.convert_single_example(0,example,label_list, max_seq_length, tokenizer)\n",
        "\n",
        "  return (feature.input_ids, feature.input_mask, feature.segment_ids, feature.label_id)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_HQSsHwWCsK"
      },
      "source": [
        "You want to use [`Dataset.map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map) to apply this function to each element of the dataset. [`Dataset.map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map) runs in graph mode.\n",
        "\n",
        "- Graph tensors do not have a value.\n",
        "- In graph mode you can only use TensorFlow Ops and functions.\n",
        "\n",
        "So you can't `.map` this function directly: You need to wrap it in a [`tf.py_function`](https://www.tensorflow.org/api_docs/python/tf/py_function). The [`tf.py_function`](https://www.tensorflow.org/api_docs/python/tf/py_function) will pass regular tensors (with a value and a `.numpy()` method to access it), to the wrapped python function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaNlkKVfWX0Q"
      },
      "source": [
        "## Task 7: Wrap a Python Function into a TensorFlow op for Eager Execution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGACBcfCWC2O"
      },
      "source": [
        "def to_feature_map(text, label):\n",
        "  input_ids, input_mask, segment_ids, label_id = tf.py_function(to_feature, inp=[text, label],\n",
        "                                Tout=[tf.int32, tf.int32, tf.int32, tf.int32])\n",
        "\n",
        "  # py_func doesn't set the shape of the returned tensors.\n",
        "  input_ids.set_shape([max_seq_length])\n",
        "  input_mask.set_shape([max_seq_length])\n",
        "  segment_ids.set_shape([max_seq_length])\n",
        "  label_id.set_shape([])\n",
        "\n",
        "  x = {\n",
        "        'input_word_ids': input_ids,\n",
        "        'input_mask': input_mask,\n",
        "        'input_type_ids': segment_ids\n",
        "    }\n",
        "  return (x, label_id)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhdO6MjTbtn1"
      },
      "source": [
        "## Task 8: Create a TensorFlow Input Pipeline with `tf.data`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHRdiO3dnPNr"
      },
      "source": [
        "with tf.device('/cpu:0'):\n",
        "  # train\n",
        "  train_data = (train_data.map(to_feature_map,\n",
        "                              num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "                          .shuffle(1000)\n",
        "                          .batch(32, drop_remainder=True)\n",
        "                          .prefetch(tf.data.experimental.AUTOTUNE))\n",
        "\n",
        "\n",
        "  # valid\n",
        "  valid_data = (val_data.map(to_feature_map,\n",
        "                              num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "                          .batch(32, drop_remainder=True)\n",
        "                          .prefetch(tf.data.experimental.AUTOTUNE))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KLUWnfx-YDi2"
      },
      "source": [
        "The resulting `tf.data.Datasets` return `(features, labels)` pairs, as expected by [`keras.Model.fit`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0Z2cy9GHQ8x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa0573f0-77ab-4801-a29d-52b55cc5dd17"
      },
      "source": [
        "# train data spec\n",
        "train_data.element_spec"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'input_word_ids': TensorSpec(shape=(32, 128), dtype=tf.int32, name=None),\n",
              "  'input_mask': TensorSpec(shape=(32, 128), dtype=tf.int32, name=None),\n",
              "  'input_type_ids': TensorSpec(shape=(32, 128), dtype=tf.int32, name=None)},\n",
              " TensorSpec(shape=(32,), dtype=tf.int32, name=None))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGAH-ycYOmao",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4fd6de7-142f-40b7-e42e-a92a73ac2f09"
      },
      "source": [
        "# valid data spec\n",
        "valid_data.element_spec"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'input_word_ids': TensorSpec(shape=(32, 128), dtype=tf.int32, name=None),\n",
              "  'input_mask': TensorSpec(shape=(32, 128), dtype=tf.int32, name=None),\n",
              "  'input_type_ids': TensorSpec(shape=(32, 128), dtype=tf.int32, name=None)},\n",
              " TensorSpec(shape=(32,), dtype=tf.int32, name=None))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZxe-7yhPyQe"
      },
      "source": [
        "## Task 9: Add a Classification Head to the BERT Layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9THH5V0Dw2HO"
      },
      "source": [
        "<div align=\"center\">\n",
        "    <img width=\"512px\" src='https://drive.google.com/uc?id=1fnJTeJs5HUpz7nix-F9E6EZdgUflqyEu' />\n",
        "    <p style=\"text-align: center;color:gray\">Figure 3: BERT Layer</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9il4gtlADcp"
      },
      "source": [
        "# Building the model\n",
        "def create_model():\n",
        "  input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,\n",
        "                                      name=\"input_word_ids\")\n",
        "  input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,\n",
        "                                  name=\"input_mask\")\n",
        "  input_type_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,\n",
        "                                  name=\"input_type_ids\")\n",
        "\n",
        "  pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, input_type_ids])\n",
        "\n",
        "  drop = tf.keras.layers.Dropout(0.4)(pooled_output)\n",
        "  output = tf.keras.layers.Dense(1, activation=\"sigmoid\", name=\"output\")(drop)\n",
        "\n",
        "  model = tf.keras.Model(\n",
        "    inputs={\n",
        "        'input_word_ids': input_word_ids,\n",
        "        'input_mask': input_mask,\n",
        "        'input_type_ids': input_type_ids\n",
        "    },\n",
        "    outputs=output)\n",
        "  return model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6maM-vr7YaJ"
      },
      "source": [
        "## Task 10: Fine-Tune BERT for Text Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptCtiiONsBgo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f021c61-8807-45aa-e31f-11731235a803"
      },
      "source": [
        "model = create_model()\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),\n",
        "                     loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                     metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_word_ids (InputLayer  [(None, 128)]                0         []                            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " input_mask (InputLayer)     [(None, 128)]                0         []                            \n",
            "                                                                                                  \n",
            " input_type_ids (InputLayer  [(None, 128)]                0         []                            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)    [(None, 768),                1094822   ['input_word_ids[0][0]',      \n",
            "                              (None, 128, 768)]           41         'input_mask[0][0]',          \n",
            "                                                                     'input_type_ids[0][0]']      \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (None, 768)                  0         ['keras_layer[1][0]']         \n",
            "                                                                                                  \n",
            " output (Dense)              (None, 1)                    769       ['dropout_1[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 109483010 (417.64 MB)\n",
            "Trainable params: 109483009 (417.64 MB)\n",
            "Non-trainable params: 1 (1.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GJaFnkbMtPL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "fd09a5d4-643c-451b-85e9-b8cfb9668814"
      },
      "source": [
        "tf.keras.utils.plot_model(model=model,show_shapes=True,dpi=76)"
      ],
      "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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcREcgPUHr9O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3253517-d5e4-42bb-bf6d-c985f8f6e4c9"
      },
      "source": [
        "# Train model\n",
        "epochs=5\n",
        "history = model.fit(train_data,\n",
        "                    validation_data=valid_data,\n",
        "                    epochs=epochs,\n",
        "                    verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "306/306 [==============================] - 304s 832ms/step - loss: 0.1597 - binary_accuracy: 0.9406 - val_loss: 0.1199 - val_binary_accuracy: 0.9594\n",
            "Epoch 2/5\n",
            "306/306 [==============================] - 262s 851ms/step - loss: 0.0939 - binary_accuracy: 0.9649 - val_loss: 0.1265 - val_binary_accuracy: 0.9552\n",
            "Epoch 3/5\n",
            "306/306 [==============================] - 262s 853ms/step - loss: 0.0486 - binary_accuracy: 0.9828 - val_loss: 0.1694 - val_binary_accuracy: 0.9542\n",
            "Epoch 4/5\n",
            "306/306 [==============================] - 262s 854ms/step - loss: 0.0192 - binary_accuracy: 0.9937 - val_loss: 0.2499 - val_binary_accuracy: 0.9552\n",
            "Epoch 5/5\n",
            "306/306 [==============================] - 260s 846ms/step - loss: 0.0102 - binary_accuracy: 0.9973 - val_loss: 0.3005 - val_binary_accuracy: 0.9510\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNZl1lx_cA5Y"
      },
      "source": [
        "## Task 11: Evaluate the BERT Text Classification Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCjgrUYH_IsE"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_graphs(history, metric):\n",
        "  plt.plot(history.history[metric])\n",
        "  plt.plot(history.history['val_'+metric], '')\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(metric)\n",
        "  plt.legend([metric, 'val_'+metric])\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6lrFRra_KmA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "1fcac89b-5822-4a6d-9e08-f17e5293ff3a"
      },
      "source": [
        "plot_graphs(history,'loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opu9neBA_98R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "177344a6-2230-47dd-972f-97797a013809"
      },
      "source": [
        "plot_graphs(history,'binary_accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkhtCCgnUbY6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a87d112-d0d7-4d5e-ee20-6d7fe8decc70"
      },
      "source": [
        "sample_example = [\"Are Americans Idiots?\",\\\n",
        "                  \"Why are Indians Brown?\",\\\n",
        "                  \"Is trump retarded?\",\\\n",
        "                  \"Why is United Kingdom is the worst place?\",\\\n",
        "                  \"Why is Messi the GOAT?\",\n",
        "                  ]\n",
        "test_data = tf.data.Dataset.from_tensor_slices((sample_example, [0]*len(sample_example)))\n",
        "test_data = (test_data.map(to_feature_map).batch(1))\n",
        "preds = model.predict(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 1s 24ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4B8NQBLd9rN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a9b0648-468e-4106-ec0e-0a5a07b97ae1"
      },
      "source": [
        "['Toxic' if pred >=0.5 else 'Sincere' for pred in preds]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Toxic', 'Toxic', 'Toxic', 'Sincere', 'Sincere']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeVNOGfFJT9O"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_YWudFRJT__"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hENB__IlJUCk"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkYpiGrhJUFK"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYqbQZJnJUHw"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiKuBGgfJUKv"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}