validation-of-annotated-labels.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oaNu3HJtTTo"
      },
      "source": [
        "# Testing the performance of a Language Model (LM) on annotated dataset\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xFqG3VVtxpi"
      },
      "source": [
        "## Installs\n",
        "\n",
        "Installation of **[transformers](https://github.com/huggingface/transformers)** and **[ml_things](https://github.com/gmihaila/ml_things)**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUz_vmR8tyoc",
        "outputId": "a2422edb-aa37-42da-f5f1-6e6a84eadc02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 10.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 182 kB 54.0 MB/s \n",
            "\u001b[?25h  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 53 kB 1.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 9.4 MB 30.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 965 kB 57.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 295 kB 76.1 MB/s \n",
            "\u001b[?25h  Building wheel for ml-things (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git\n",
        "\n",
        "!pip install -q git+https://github.com/gmihaila/ml_things.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a08TPwdwpPpk"
      },
      "source": [
        "Install matplotlib 3.1.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "iHpUfcw6pO06",
        "outputId": "93b07301-f6fc-44b7-b114-a098d860a379"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: matplotlib 3.6.2\n",
            "Uninstalling matplotlib-3.6.2:\n",
            "  Would remove:\n",
            "    /usr/local/lib/python3.8/dist-packages/matplotlib-3.6.2-py3.8-nspkg.pth\n",
            "    /usr/local/lib/python3.8/dist-packages/matplotlib-3.6.2.dist-info/*\n",
            "    /usr/local/lib/python3.8/dist-packages/matplotlib/*\n",
            "    /usr/local/lib/python3.8/dist-packages/mpl_toolkits/axes_grid1/*\n",
            "    /usr/local/lib/python3.8/dist-packages/mpl_toolkits/axisartist/*\n",
            "    /usr/local/lib/python3.8/dist-packages/mpl_toolkits/mplot3d/*\n",
            "    /usr/local/lib/python3.8/dist-packages/mpl_toolkits/tests/*\n",
            "    /usr/local/lib/python3.8/dist-packages/pylab.py\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled matplotlib-3.6.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting matplotlib==3.1.3\n",
            "  Downloading matplotlib-3.1.3-cp38-cp38-manylinux1_x86_64.whl (13.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.1 MB 13.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib==3.1.3) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib==3.1.3) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib==3.1.3) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib==3.1.3) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib==3.1.3) (1.21.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib==3.1.3) (1.15.0)\n",
            "Installing collected packages: matplotlib\n",
            "\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",
            "ml-things 0.0.1 requires matplotlib>=3.4.0, but you have matplotlib 3.1.3 which is incompatible.\u001b[0m\n",
            "Successfully installed matplotlib-3.1.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!python -m pip uninstall matplotlib\n",
        "!pip install matplotlib==3.1.3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vF3UbENTtz2_"
      },
      "source": [
        "## Imports\n",
        "\n",
        "Import necessary libraries\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gjr_J342tOPq"
      },
      "outputs": [],
      "source": [
        "import io\n",
        "import os\n",
        "import torch\n",
        "import urllib\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "from collections import defaultdict\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn import functional as F\n",
        "from matplotlib import pyplot as plt\n",
        "from ml_things import plot_dict, plot_confusion_matrix, fix_text\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from transformers import (AutoConfig, \n",
        "                          AutoModelForSequenceClassification, \n",
        "                          AutoTokenizer, AdamW, \n",
        "                          get_linear_schedule_with_warmup,\n",
        "                          set_seed,\n",
        "                          )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO93STIzl2Vi"
      },
      "source": [
        "## Declaration of parameters\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlrGCdd-bC9n"
      },
      "source": [
        "Declare parameters used in this notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "k10-1eOOl-Uu"
      },
      "outputs": [],
      "source": [
        "# Set seed for reproducibility\n",
        "set_seed(123)\n",
        "\n",
        "\n",
        "# Number of batch_size - depending on the max sequence length and avaialable GPU memory\n",
        "# For 512 sequence length batch of 10 works without cuda memory issues\n",
        "# For small sequence length one can try batch of 32 or higher\n",
        "batch_size = 16\n",
        "\n",
        "\n",
        "# Padd or truncate text sequences to a specific length\n",
        "# if `None` it will use maximum sequence of word piece tokens allowed by model\n",
        "max_length = None\n",
        "\n",
        "# Look for gpu to use. Will use `cpu` by default if no gpu found\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Name of the transformers model - will use already pretrained model\n",
        "# Path of transformer model - will load your own model from local disk\n",
        "\n",
        "model_name_or_path = \"Rewire/XTC\"\n",
        "#model_name_or_path = \"/content/drive/MyDrive/XTC\"\n",
        "\n",
        "# Dictionary of labels and their ids \n",
        "# this will be used to convert string labels to number.\n",
        "labels_ids = {'non-hate': 0, 'hate': 1}\n",
        "\n",
        "# How many labels are we using in training\n",
        "# This is used to decide the size of the classification head\n",
        "n_labels = len(labels_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78UA1RvdxDTp"
      },
      "source": [
        "## Load the test dataset (annotated in LabelStudio)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zB6DIS61xE2j",
        "outputId": "e9126eea-b197-4951-8920-6b2f00969b65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdqCYu86W5w4"
      },
      "source": [
        "Load the dataset(s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqOZdzvFlI0l",
        "outputId": "ec95cd86-e9c2-4ed0-a8c6-f77d768291a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of annotation test dataset--> 115\n"
          ]
        }
      ],
      "source": [
        "#---- multilingual hatecheck test suites---------------\n",
        "os.chdir(\"/content/drive/MyDrive/\")\n",
        "test = pd.read_csv(\"project-10-at-2022-12-20-11-34-4607adba.csv\")\n",
        "\n",
        "\n",
        "print (\"Size of annotation test dataset-->\", len(test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAAOyEq8ejij"
      },
      "source": [
        "Preparing the test dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ebrZa1yV4Wi",
        "outputId": "70c3ceae-83ab-430a-dc5c-2e3a6db82312"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['message', 'messageSentiment.score', 'messageHate.score', 'id',\n",
              "       'sentiment', 'annotator', 'annotation_id', 'created_at', 'updated_at',\n",
              "       'lead_time'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6GUUkh779T9",
        "outputId": "1c43e17e-d7d7-4399-d58b-31cd1438a5c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                               message  \\\n",
            "85                        सुलेमानी कीड़ा जग गया होगा...   \n",
            "88                                  Indian secularism.   \n",
            "42   अगर देश के मुसलमानों ने अपना आदर्श डॉक्टर कलाम...   \n",
            "82   370 हटने के बाद कश्मीर में अब जो शांति है ,शान...   \n",
            "94   MERA BHARAT MAHAN-NAMO NAMO NAMAMI-JAI HIND JA...   \n",
            "90                                                  😂😂   \n",
            "100                   धोबी का कु... ना घर का ना घाट...   \n",
            "13   हर हर महादेव 🙏 पेज को जरूर फॉलो करें🙏 दुनिया क...   \n",
            "59   इस्लामिक जिहादियों ने फिर पार कीं क्रूरता की ह...   \n",
            "111                                      #AkhandBharat   \n",
            "\n",
            "     messageSentiment.score  messageHate.score     id           sentiment  \\\n",
            "85                        0                  1  32532             Hateful   \n",
            "88                        0                  1  32386         Non-hateful   \n",
            "42                        0                  1  31861  negative discourse   \n",
            "82                        0                  1  31828             Hateful   \n",
            "94                        2                  1  32141         Non-hateful   \n",
            "90                        1                  0  32362         Non-hateful   \n",
            "100                       0                  1  32167             Hateful   \n",
            "13                        1                  0  31893  negative discourse   \n",
            "59                        0                  1  31846  negative discourse   \n",
            "111                       0                  1  31873         Non-hateful   \n",
            "\n",
            "     annotator  annotation_id                   created_at  \\\n",
            "85           3            429  2022-10-27T11:24:51.936385Z   \n",
            "88           3            426  2022-10-27T11:23:04.900733Z   \n",
            "42           2            467  2022-11-07T14:25:22.105507Z   \n",
            "82           2            435  2022-11-02T13:02:38.655329Z   \n",
            "94           3            420  2022-10-27T11:21:28.871158Z   \n",
            "90           3            424  2022-10-27T11:22:39.669024Z   \n",
            "100          3            414  2022-10-27T11:02:30.565715Z   \n",
            "13           2            496  2022-11-08T11:26:49.539448Z   \n",
            "59           2            451  2022-11-07T07:36:01.708906Z   \n",
            "111          3            403  2022-10-27T10:58:56.338525Z   \n",
            "\n",
            "                      updated_at  lead_time  \n",
            "85   2022-10-27T11:24:51.936410Z     18.519  \n",
            "88   2022-10-27T11:23:04.900761Z      1.964  \n",
            "42   2022-11-07T14:25:22.105533Z     18.923  \n",
            "82   2022-11-02T13:02:38.655375Z     12.561  \n",
            "94   2022-10-27T11:21:28.871198Z      7.603  \n",
            "90   2022-10-27T11:22:39.669067Z      2.228  \n",
            "100  2022-10-27T11:02:30.565742Z     20.627  \n",
            "13   2022-11-08T11:26:49.539472Z     30.159  \n",
            "59   2022-11-07T07:36:01.708935Z     12.393  \n",
            "111  2022-10-27T10:58:56.338554Z      4.391  \n"
          ]
        }
      ],
      "source": [
        "print (test.sample(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Select and rename only relevant columns"
      ],
      "metadata": {
        "id": "h65S2JuHZoYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test = test [['message', 'sentiment']]\n",
        "\n",
        "test = test.rename(columns={'message': 'text', 'sentiment': 'label'})"
      ],
      "metadata": {
        "id": "IcH2l3ysXmlV"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = test.replace({'negative discourse': 'Negative discourse', 'needs more context': 'Insufficient context'})"
      ],
      "metadata": {
        "id": "EA6fxJgSZJom"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(5)\n",
        "bins = np.linspace(0, 4, 10)\n",
        "\n",
        "fig, ax = plt.subplots (figsize=(6, 4), dpi=140)\n",
        "plt.title ('Histogram of the annotated labels')\n",
        "\n",
        "test['label'].hist(bins=bins, weights=np.ones_like(test[test.columns[0]])*100./len(test), color = \"skyblue\")\n",
        "\n",
        "ax.set_ylabel ('Percentage (%)')\n",
        "#ax.set_xlabel (r'Predicted labels')\n",
        "ax.set_xticks (x)\n",
        "#ax.set_xticklabels (list(labels_ids_sentiment.keys()))\n",
        "ax.grid(color='slategrey', linestyle='--', linewidth=1, alpha=0.2)\n",
        "#ax.legend (loc='upper right')\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "a4Sb5Z7QVwrH",
        "outputId": "1a23ad65-af47-47f6-a85a-605624cf9be3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x560 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Choose only the hate and non-hate labels"
      ],
      "metadata": {
        "id": "Kp2AtM5IZu1t"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKEBZvdcTMEt",
        "outputId": "7cf27e77-b71f-49c2-ad80-0e2e79654bbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "65\n",
            "                                                 text  label\n",
            "1   Attended the 5th meeting of NITI Aayog’s Gover...      0\n",
            "2   जला तो दिया तुमने निर्दोष पुजारी बाबा को कल को...      1\n",
            "4   भारतभूमि में जन्में सभी पंथ एवं सम्प्रदायों के...      0\n",
            "5   अयोध्या के श्री राम जी का भव्य मन्दिर का निर्म...      0\n",
            "8   फिल्म में होंगे मोदी के चाय बेचने से लेकर कैंट...      0\n",
            "16  दीपा मलिक भारतीय पैरालम्पिक खिलाड़ियों की जीत ...      0\n",
            "19  पाकिस्तान से आए हिंदू सरणार्थी साउथ दिल्ली के ...      0\n",
            "22  # मोडलींग छोड़िये # अपनी हिन्दू संस्कृति के अन...      0\n",
            "23  ओलिंपिक में ब्रॉन्ज मेडल जीतने वाली भारतीय हॉक...      0\n",
            "24  उत्तर प्रदेश के इस शहर में है अनुष्का और विराट...      0\n",
            "25  महाराज की सम्पत्ति जानकार अम्बानी-अडानी को भी ...      0\n",
            "28  आतंकवाद और कश्मीरी पंडितों की हत्या के मामले म...      0\n",
            "30                     कांग्रेस वाले पागल हो चुके है।      0\n",
            "32  राष्ट्रपिता महात्मा गांधी जी की पुण्यतिथि पर उ...      0\n",
            "34  अभी जो राज्य सरकारें 3 मई के बाद भी केंद्र सरक...      0\n",
            "35  सचिन तेंदुलकर की बेटी सारा की इन 10 खूबसूरत तस...      0\n",
            "37  न्यूजीलैंड की मस्ज़िद में गोलीबारी कर के आतंक म...      1\n",
            "38  महाकाल मंदिर में खुदाई से निकले करीब 2100 साल ...      0\n",
            "45  राम मंदिर निर्माण के यज्ञ में संजय जैसे न जाने...      1\n",
            "50  we are a political party (Akhand bharat party)...      0\n"
          ]
        }
      ],
      "source": [
        "test = test [ (test['label'] == 'Non-hateful') | (test['label'] == 'Hateful')]\n",
        "test = test.replace({'Hateful': 1, 'Non-hateful': 0})\n",
        "\n",
        "#print (len(test))\n",
        "print (test.head(20))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3j3lyrvXTUh"
      },
      "source": [
        "Extract testing texts and labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "zhHTi_sFXJxT"
      },
      "outputs": [],
      "source": [
        "test_text = list(test ['text'])\n",
        "test_labels = list(test ['label'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdnKsoFbt1Wd"
      },
      "source": [
        "## Define helper class and functions\n",
        "\n",
        "Classes and functions that are used in this notebook:\n",
        "\n",
        "* MyDataset\n",
        "\n",
        "* validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jR1SDf1k7q_H"
      },
      "source": [
        "Define `MyDatset` class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "U4tFUqm6ihFs"
      },
      "outputs": [],
      "source": [
        "class MyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9LBGdet8cLH"
      },
      "source": [
        "**Define `validation` function:**\n",
        "\n",
        "Validation function to evaluate model performance on a separate set of data. This function will return the true and predicted labels so we can use later to evaluate the model's performance.\n",
        "\n",
        "  Arguments:\n",
        "\n",
        "    dataloader (:obj:`torch.utils.data.dataloader.DataLoader`):\n",
        "          Parsed data into batches of tensors.\n",
        "\n",
        "    device_ (:obj:`torch.device`):\n",
        "          Device used to load tensors before feeding to model.\n",
        "\n",
        "  Returns:\n",
        "    \n",
        "    :obj:`List[List[int], List[int], float]`: List of [True Labels, Predicted\n",
        "        Labels, Train Average Loss]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "1H2wtmA6c1cG"
      },
      "outputs": [],
      "source": [
        "def validation(dataloader, device_):\n",
        "\n",
        "  # Use global variable for model.\n",
        "  global model\n",
        "\n",
        "  # Tracking variables\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "  #total loss for this epoch\n",
        "  total_loss = 0\n",
        "\n",
        "  # Put the model in evaluation mode (the dropout layers behave differently during evaluation)\n",
        "  model.eval()\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "\n",
        "    # Add original labels\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "\n",
        "    # Move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "\n",
        "    # Telling the model not to compute or store gradients, saving memory and speeding up validation\n",
        "    with torch.no_grad():        \n",
        "\n",
        "        # Forward pass, calculate logit predictions. This will return the logits rather than the loss because we have not provided labels.\n",
        "        # `token_type_ids` is the same as the \"segment ids\", which differentiates sentence 1 and 2 in 2-sentence tasks.\n",
        "        # The documentation for this `model` function is here: \n",
        "        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
        "        outputs = model(**batch)\n",
        "\n",
        "        # The call to `model` always returns a tuple, so we need to pull the loss value out of the tuple along with the logits. \n",
        "        # We will use logits later to to calculate training accuracy.\n",
        "        # loss, logits = outputs[:2] # UNCOMMENT THIS IF YOU DON'T WANT TO CALCULATE THE LOSS YOURSELF\n",
        "\n",
        "        # Alternatively, you can just get the logits and calculate the loss yourself as follows\n",
        "        labels = batch.pop(\"labels\")\n",
        "        loss = F.cross_entropy(outputs.logits, labels)\n",
        "        logits = outputs.logits\n",
        "        \n",
        "        # Move logits and labels to CPU\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "\n",
        "        # Accumulate the training loss over all of the batches so that we can calculate the average loss at the end. \n",
        "        # `loss` is a Tensor containing a single value; the `.item()` function just returns the Python value from the tensor.\n",
        "        total_loss += loss.item()\n",
        "        \n",
        "        # get predicitons to list\n",
        "        predict_content = logits.argmax(axis=-1).flatten().tolist()\n",
        "\n",
        "        # update list\n",
        "        predictions_labels += predict_content\n",
        "\n",
        "  # Calculate the average loss over the training data.\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "\n",
        "  # Return all true labels and prediciton for future evaluations.\n",
        "  return true_labels, predictions_labels, avg_epoch_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7JS0QZ0tLvi"
      },
      "source": [
        "## Load config, tokenizer and the model\n",
        "\n",
        "Loading the three esential parts of pretrained transformers: \n",
        "\n",
        "* configuration, \n",
        "* tokenizer and \n",
        "* model. \n",
        "\n",
        "We also need to load the model to the device we're planning to use (GPU / CPU)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
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          "height": 277,
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        },
        "id": "R4IPr-6LtZNW",
        "outputId": "3c01e41b-e741-4539-afbf-47a802e45f7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model configuraiton...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/741 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d2be474229444c2b42c94afeefd391f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model tokenizer...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/358 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c778c6d8a78a4984bd4710d256063bba"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "553c925fbd394e7db6dae1d7b0e2202b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8219f4a4a5b04e329ee8c7a79b3dfe6c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c954b03d6946429488339b37d7d4c739"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading the model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e3bd079475745d7bc38e174892373a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded to `cuda`\n"
          ]
        }
      ],
      "source": [
        "# Get model configuration\n",
        "print('Loading model configuraiton...')\n",
        "model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_name_or_path, \n",
        "                                          num_labels=n_labels)\n",
        "\n",
        "# Get model's tokenizer\n",
        "print('Loading model tokenizer...')\n",
        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name_or_path)\n",
        "\n",
        "# Get the actual model\n",
        "print('Loading the model...')\n",
        "model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name_or_path, \n",
        "                                                           config=model_config)\n",
        "\n",
        "# Load model to the defined device\n",
        "model.to(device)\n",
        "print('Model loaded to `%s`'%device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ySH5UUcu4YN"
      },
      "source": [
        "## Data encoding and create DataLoader object\n",
        "\n",
        "Create data encodings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "EsN5iXYYn_t0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9bc8e92-f3f2-4d8c-e595-73c739da30a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        }
      ],
      "source": [
        "test_encodings = tokenizer(test_text, truncation=True, padding=True, max_length=max_length)\n",
        "\n",
        "test_dataset = MyDataset(test_encodings, test_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJczXnGZseQn"
      },
      "source": [
        "Create DataLoader objects that will be fed into our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLhJ-7UwiFcl",
        "outputId": "c67950a4-8b47-4bc4-f8ef-0946f98f76f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created `test_dataloader` with 5 batches!\n"
          ]
        }
      ],
      "source": [
        "test_dataloader = DataLoader (test_dataset, batch_size=batch_size, shuffle=False)\n",
        "print('Created `test_dataloader` with %d batches!'%len(test_dataloader))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-ytwooIVV1f"
      },
      "source": [
        "## Evaluation on the annotated test dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591,
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        },
        "id": "X6rmJ-tj0mHn",
        "outputId": "dae247a9-8312-4579-91ef-c68b4353d547"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    non-hate       1.00      0.71      0.83        48\n",
            "        hate       0.55      1.00      0.71        17\n",
            "\n",
            "    accuracy                           0.78        65\n",
            "   macro avg       0.77      0.85      0.77        65\n",
            "weighted avg       0.88      0.78      0.80        65\n",
            "\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get model predictions on the test data\n",
        "true_labels, predictions_labels, avg_epoch_loss = validation (test_dataloader, device)\n",
        "\n",
        "# Create the evaluation report\n",
        "evaluation_report = classification_report (true_labels, predictions_labels, labels=list(labels_ids.values()), target_names=list(labels_ids.keys()))\n",
        "\n",
        "# Print the evaluation report\n",
        "print(evaluation_report)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_true=true_labels, y_pred=predictions_labels, \n",
        "                      classes=list(labels_ids.keys()), normalize=True, \n",
        "                      magnify=0.1, \n",
        "                      use_title='Normalized confusion matrix'\n",
        "                      );"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "lY88GyoMrKyR",
        "outputId": "3ef7bced-4f5e-4040-bdf2-d0a8a040d850"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ],
            "image/png": 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        }
      ],
      "source": [
        "x = np.arange(n_labels)\n",
        "bins = np.linspace(0, 1, 10)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 7))\n",
        "plt.title('Distribution of the test labels\\n')\n",
        "plt.hist([true_labels, predictions_labels], bins, label=['Annotated labels', 'Model predicted labels'] )\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(list(labels_ids.keys()))\n",
        "ax.legend(loc='lower center')\n",
        "plt.show()"
      ]
    }
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