Multiclass Classification/Malicious URLs/Malicious_URLs_DeBERTa.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YI17H3Melofy"
      },
      "source": [
        "## Malicious URLs\n",
        "\n",
        "Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LN9bRho1xOPn"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwmNhZh7x9j5",
        "outputId": "860820ef-f098-49b5-9441-98fd0c97ba70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "%pip install torch transformers datasets evaluate sentencepiece\n",
        "%pip install --upgrade accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_MLXgPWxUlt"
      },
      "source": [
        "#### HuggingFace Login Via Command Line"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Qu3qdwTx9Tx",
        "outputId": "a285dbf3-9518-43ce-a8bb-53d357d54d7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_LTj-6UUxOMw"
      },
      "source": [
        "#### Mount Google Drive to Notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGZlSpON0sD2",
        "outputId": "b2595ffc-08e5-44ab-bc94-b45be23b9c3a"
      },
      "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": "DswtRoySlof0"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rae-2jwElof0",
        "outputId": "ac537b52-a804-4e36-e20b-a37ae7e2cfce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: Failed to call git rev-parse --git-dir: exit status 128 \n",
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "from zipfile import ZipFile\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import datasets\n",
        "from datasets import Dataset, DatasetDict\n",
        "\n",
        "import torch\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "from transformers import TrainingArguments, Trainer, set_seed, DataCollatorWithPadding\n",
        "\n",
        "import evaluate\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1fukwgzlof1"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xu22V8llof1",
        "outputId": "dfde2e7b-6576-4c28-dd8a-e207bf964eaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Python: 3.10.1\n",
            "         NumPy: 1.22.4\n",
            "        Pandas: 1.5.3\n",
            "      Datasets: 2.13.0\n",
            "  Transformers: 4.30.2\n",
            "         Torch: 2.0.1+cu118\n",
            "      Evaluate: 0.4.0\n"
          ]
        }
      ],
      "source": [
        "print(\"Python:\".rjust(15), sys.version[0:6])\n",
        "print(\"NumPy:\".rjust(15), np.__version__)\n",
        "print(\"Pandas:\".rjust(15), pd.__version__)\n",
        "print(\"Datasets:\".rjust(15), datasets.__version__)\n",
        "print(\"Transformers:\".rjust(15), transformers.__version__)\n",
        "print(\"Torch:\".rjust(15), torch.__version__)\n",
        "print(\"Evaluate:\".rjust(15), evaluate.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vW_Rsj4CxyMg"
      },
      "source": [
        "#### Unzip Dataset File"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "2dmgkGTWx24P"
      },
      "outputs": [],
      "source": [
        "#with ZipFile(\"/content/drive/MyDrive/projects/malicious URLs/archive (3).zip\", 'r') as zObject:\n",
        "#    zObject.extractall(path=\"/content/drive/MyDrive/projects/malicious URLs/\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vob1xXeDlof2"
      },
      "source": [
        "#### Load & Start Preprocessing Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "znplYD_rlof2",
        "outputId": "55496632-ae8a-42a1-ae71-d166af462495"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 641119 entries, 0 to 641118\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   text    641119 non-null  object\n",
            " 1   label   641119 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 14.7+ MB\n"
          ]
        }
      ],
      "source": [
        "names = [\"text\",\n",
        "         \"label\"]\n",
        "\n",
        "data_types = {\n",
        "    \"text\": str,\n",
        "    \"label\": str,\n",
        "    }\n",
        "\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/projects/malicious URLs/malicious_phish.csv\",\n",
        "                   names=names,\n",
        "                   dtype=data_types,\n",
        "                   header=0)\n",
        "\n",
        "data['text'] = data['text'].drop_duplicates().reset_index(drop=True)\n",
        "\n",
        "data['label'] = data['label'].apply(lambda x: x.strip())\n",
        "\n",
        "data = data.dropna()\n",
        "\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zksx8xsklof2"
      },
      "source": [
        "#### Define Dictionaries to Convert Between String & Integer Class Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hd_T8NJllof2",
        "outputId": "ccc0398a-6c9c-4d34-b082-a0a9a282143a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['phishing' 'benign' 'defacement' 'malware']\n",
            "4\n",
            "{'phishing': 0, 'benign': 1, 'defacement': 2, 'malware': 3}\n",
            "{0: 'phishing', 1: 'benign', 2: 'defacement', 3: 'malware'}\n"
          ]
        }
      ],
      "source": [
        "unique_labels = data['label'].unique()\n",
        "\n",
        "id2label = {idx:label for idx, label in enumerate(unique_labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(unique_labels)}\n",
        "\n",
        "NUM_OF_LABELS = len(unique_labels)\n",
        "\n",
        "print(unique_labels)\n",
        "print(NUM_OF_LABELS)\n",
        "print(label2id)\n",
        "print(id2label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUshgDR8lof3"
      },
      "source": [
        "#### Visual: Histogram of Text Lengths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "8gKf23Wqlof3",
        "outputId": "f8044e61-9542-4ac0-c27c-3788ccbac792"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The maximum length of the URLs is 2175 characters.\n"
          ]
        }
      ],
      "source": [
        "data['text_length'] = data['text'].str.len()\n",
        "\n",
        "n_bin=200\n",
        "data.hist('text_length', grid=False, figsize=(18,8), range=(0,n_bin + 1), bins=(n_bin + 1))\n",
        "plt.title('Histogram: Length of URLs')\n",
        "plt.xlabel('# of Characters in URL')\n",
        "plt.ylabel('# of Samples')\n",
        "plt.show()\n",
        "\n",
        "print(f\"The maximum length of the URLs is {max(data['text_length'])} characters.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5LLbXellof3"
      },
      "source": [
        "#### Visual: Tweet Length by Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "kZduaMjslof3",
        "outputId": "1d42112d-0ead-454a-abd8-716a25b56d1e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "data.boxplot(\"text_length\", by=\"label\", grid=False, showfliers=False, color=\"Blue\", figsize=(10,8))\n",
        "\n",
        "plt.suptitle('')\n",
        "plt.title('URL Character Length by Type/Class')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('Characters per URL')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqgMPq7elof3"
      },
      "source": [
        "#### Final Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rG5s5ceDlof3",
        "outputId": "703e3b22-153c-49cd-fdf0-1cc890ffa00c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 630719 entries, 0 to 641118\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   text    630719 non-null  object\n",
            " 1   label   630719 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 14.4+ MB\n"
          ]
        }
      ],
      "source": [
        "data = data[data['text_length'] < 201]\n",
        "\n",
        "data = data.drop(columns=[\"text_length\"])\n",
        "\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWYCTizQlof3"
      },
      "source": [
        "#### Barplot Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "zkWv7iirw0uZ"
      },
      "outputs": [],
      "source": [
        "def barplot_visual(feature_to_visualize: pd.Series,\n",
        "                   plot_title: str,\n",
        "                   x_label: str,\n",
        "                   y_label: str = \"# of Samples\",\n",
        "                   show_class_amounts: bool = True\n",
        "                   ):\n",
        "    \"\"\"\n",
        "    This function returns a histogram for a feature.\n",
        "\n",
        "    Inputs:\n",
        "    - feature_to_visualize: Feature to plot in the form\n",
        "    of pandas Series.\n",
        "    - plot_title: The title of the plot/visual.\n",
        "    - x_label: The string label value for the x label.\n",
        "    - y_label: The string label value for the y label.\n",
        "\n",
        "    Output:\n",
        "    - A Bar Plot.\n",
        "    \"\"\"\n",
        "\n",
        "    assert type(feature_to_visualize) == pd.Series, \\\n",
        "        \"Make sure the dataset input type is a Pandas Series.\"\n",
        "    assert type(plot_title) != 'str', \\\n",
        "        \"The title value must be a string value.\"\n",
        "    assert type(x_label) !=  'str', \\\n",
        "        \"The x label value must be a string value.\"\n",
        "    assert type(y_label) != 'str', \\\n",
        "        \"The y label value must be a string value.\"\n",
        "\n",
        "    ax = feature_to_visualize.value_counts().plot.bar(\n",
        "                                                      figsize=(8, 6),\n",
        "                                                      rot=0,\n",
        "                                                      legend=False,\n",
        "                                                      color=[\"lightgreen\",\n",
        "                                                             \"blue\",\n",
        "                                                             \"tan\",\n",
        "                                                             \"lightblue\",\n",
        "                                                             \"brown\",\n",
        "                                                             \"yellow\",\n",
        "                                                             \"orange\",\n",
        "                                                             \"green\",\n",
        "                                                             \"red\"]\n",
        "                                                      )\n",
        "\n",
        "    if show_class_amounts:\n",
        "        ax.bar_label(ax.containers[0])\n",
        "\n",
        "    plt.suptitle('')\n",
        "    plt.title(plot_title)\n",
        "    plt.ylabel(y_label)\n",
        "    plt.xlabel(x_label)\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvEGg9HJw0uZ"
      },
      "source": [
        "#### Number of Samples in Each Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ih7P8VEmlof4",
        "outputId": "0a1b2b32-84eb-4f47-c3f2-abb98e214f47"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "barplot_visual(data[\"label\"],\n",
        "               \"Bar Plot of URL Type\",\n",
        "               \"Output Category\",\n",
        "               \"# of Samples\",\n",
        "               )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Glro356Qw0ua"
      },
      "source": [
        "#### Convert Label Values From String to Integer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pXQaua5Mw0ua"
      },
      "outputs": [],
      "source": [
        "data['label'] = data['label'].replace(label2id).astype(np.int8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8AvzHcLlof4"
      },
      "source": [
        "#### Convert DataFrame to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "TKpuexuklof4"
      },
      "outputs": [],
      "source": [
        "dataset = Dataset.from_pandas(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7BbSvYhlof4"
      },
      "source": [
        "#### Split Dataset into Training Evaluation Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLG2lp4olof4",
        "outputId": "b3e89ad9-5aa6-4596-dafe-1efad315ceea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Dataset Shape: (422581, 3)\n",
            "Evaluation Dataset Shape: (208138, 3)\n"
          ]
        }
      ],
      "source": [
        "train_eval = dataset.train_test_split(train_size=0.67)\n",
        "\n",
        "ds = DatasetDict({\n",
        "    'train' : train_eval['train'],\n",
        "    'eval' : train_eval['test'],\n",
        "})\n",
        "\n",
        "print(\"Training Dataset Shape:\", ds['train'].shape)\n",
        "print(\"Evaluation Dataset Shape:\", ds['eval'].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehEBnf31w0ua"
      },
      "source": [
        "#### Find Longest Tokenized Input Value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "9cc8Bu5Rw0ua",
        "outputId": "bef14d1e-6a05-4223-c340-380a5237318a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MODEL_CKPT = \"microsoft/codebert-base\"\\n\\ntokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT,\\n                                             truncation=True,\\n                                             padding=True,\\n                                             batched=True,\\n                                             batch_size=2500)\\n\\ntext_len = [len(tokenizer.encode(a)) for a in dataset[\"text\"]]\\n\\nprint(f\"The longest article length is {max(text_len)} tokens.\")'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "\"\"\"MODEL_CKPT = \"microsoft/codebert-base\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT,\n",
        "                                             truncation=True,\n",
        "                                             padding=True,\n",
        "                                             batched=True,\n",
        "                                             batch_size=2500)\n",
        "\n",
        "text_len = [len(tokenizer.encode(a)) for a in dataset[\"text\"]]\n",
        "\n",
        "print(f\"The longest article length is {max(text_len)} tokens.\")\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nJka3nflof4"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nBu-l_Nlof4",
        "outputId": "a2a08bd9-81c1-4865-b62a-2b1a54b991f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This project will have a maximum length for the input text of 239 tokens.\n",
            "The model checkpoint used for this project is: microsoft/deberta-base-mnli\n"
          ]
        }
      ],
      "source": [
        "set_seed = 42\n",
        "NUM_OF_EPOCHS = 1\n",
        "\n",
        "BATCH_SIZE = 64\n",
        "LEARNING_RATE = 2e-5\n",
        "\n",
        "MAX_LENGTH = 239 #max(text_len) + 2\n",
        "MODEL_CKPT = \"microsoft/deberta-base-mnli\"\n",
        "\n",
        "WEIGHT_DECAY = 0.01\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "\n",
        "MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Malicious_URLs\"\n",
        "METRIC_NAME = \"f1\"\n",
        "\n",
        "STRATEGY = 'epoch'\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "GRAD_ACC_STEPS = 4\n",
        "\n",
        "print(f\"This project will have a maximum length for the input text of {MAX_LENGTH} tokens.\")\n",
        "print(f\"The model checkpoint used for this project is: {MODEL_CKPT}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7liPWrBolof4"
      },
      "source": [
        "#### Tokenize Entire Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343,
          "referenced_widgets": [
            "52b3a095dab34197be11b90ffc68ad8b",
            "e2fbaf550e7e4fe798f9edff2396f279",
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        },
        "id": "VSAjrx9Tlof4",
        "outputId": "2b39ecc8-ad05-43b9-9257-d6c6c2d7783d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52b3a095dab34197be11b90ffc68ad8b"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/728 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70e18c63e9624293848bed610775b036"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78c248c254d24de0a7620cc5c7261f72"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2df5dfb8ef5e4b4a91b9e0425c3af40a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/422581 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb305233e91c403db82e255c4fc226a0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/208138 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c51c1e74ce340269e5372eed156743f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (422581, 6)\n",
            "Validation data shape: (208138, 6)\n",
            "{'text': Value(dtype='string', id=None), 'label': Value(dtype='int8', id=None), '__index_level_0__': Value(dtype='int64', id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}\n",
            "Dataset({\n",
            "    features: ['text', 'label', '__index_level_0__', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 422581\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Define Tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)\n",
        "\n",
        "# Tokenize Method\n",
        "def tokenize(batch):\n",
        "    return tokenizer(batch['text'],\n",
        "                     padding=True,\n",
        "                     truncation=True,\n",
        "                     max_length=MAX_LENGTH,\n",
        "                     return_tensors=\"pt\")\n",
        "\n",
        "# Tokenize entire dataset\n",
        "encoded_data = ds.map(tokenize,\n",
        "                      batched=True,\n",
        "                      batch_size=2500)\n",
        "\n",
        "print('Training data shape:', encoded_data['train'].shape)\n",
        "print('Validation data shape:', encoded_data['eval'].shape)\n",
        "\n",
        "print(encoded_data['train'].features)\n",
        "print(encoded_data['train'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZ0t2_Ohlof4"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "vauFY6fOlof4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae8e96b4-f2f3-4629-b4b5-eb2a4fbe13a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at microsoft/deberta-base-mnli were not used when initializing DebertaForSequenceClassification: ['config']\n",
            "- This IS expected if you are initializing DebertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DebertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DebertaForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-base-mnli and are newly initialized because the shapes did not match:\n",
            "- classifier.weight: found shape torch.Size([3, 768]) in the checkpoint and torch.Size([4, 768]) in the model instantiated\n",
            "- classifier.bias: found shape torch.Size([3]) in the checkpoint and torch.Size([4]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    MODEL_CKPT,\n",
        "    num_labels=NUM_OF_LABELS,\n",
        "    ignore_mismatched_sizes=True\n",
        "    ).to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkIZ7Q8Llof5"
      },
      "source": [
        "#### Define Function to Compute Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ZS-cTQ9plof5"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(p):\n",
        "    \"\"\"\n",
        "    This function takes the predictions output from\n",
        "    trainer.evaluate & returns metrics (accuracy, F1,\n",
        "    recall, & precision) for a multiclass\n",
        "    classification project.\n",
        "    \"\"\"\n",
        "    accuracy_metric = evaluate.load(\"accuracy\")\n",
        "    accuracy = accuracy_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                             axis=1),\n",
        "                                       references=p.label_ids)['accuracy']\n",
        "\n",
        "    ### ------------------- F1 scores -------------------\n",
        "\n",
        "    f1_score_metric = evaluate.load(\"f1\")\n",
        "    weighted_f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                      axis=1),\n",
        "                                                references=p.label_ids,\n",
        "                                                average='weighted')[\"f1\"]\n",
        "\n",
        "    micro_f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                   axis=1),\n",
        "                                             references=p.label_ids,\n",
        "                                             average='micro')['f1']\n",
        "\n",
        "    macro_f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                   axis=1),\n",
        "                                             references=p.label_ids,\n",
        "                                             average='macro')[\"f1\"]\n",
        "\n",
        "    ### ------------------- recall -------------------\n",
        "\n",
        "    recall_metric = evaluate.load(\"recall\")\n",
        "    weighted_recall = recall_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                  axis=1),\n",
        "                                            references=p.label_ids,\n",
        "                                            average='weighted')[\"recall\"]\n",
        "\n",
        "    micro_recall = recall_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                               axis=1),\n",
        "                                         references=p.label_ids,\n",
        "                                         average='micro')[\"recall\"]\n",
        "\n",
        "    macro_recall = recall_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                               axis=1),\n",
        "                                         references=p.label_ids,\n",
        "                                         average='macro')[\"recall\"]\n",
        "\n",
        "    ### ------------------- precision -------------------\n",
        "\n",
        "    precision_metric = evaluate.load(\"precision\")\n",
        "    weighted_precision = precision_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                        axis=1),\n",
        "                                                  references=p.label_ids,\n",
        "                                                  average='weighted')[\"precision\"]\n",
        "\n",
        "    micro_precision = precision_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                     axis=1),\n",
        "                                               references=p.label_ids,\n",
        "                                               average='micro')[\"precision\"]\n",
        "\n",
        "    macro_precision = precision_metric.compute(predictions=np.argmax(p.predictions,\n",
        "                                                                     axis=1),\n",
        "                                               references=p.label_ids,\n",
        "                                               average='macro')[\"precision\"]\n",
        "\n",
        "    return {\"accuracy\" : accuracy,\n",
        "            \"Weighted F1\" : weighted_f1_score,\n",
        "            \"Micro F1\" : micro_f1_score,\n",
        "            \"Macro F1\" : macro_f1_score,\n",
        "            \"Weighted Recall\" : weighted_recall,\n",
        "            \"Micro Recall\" : micro_recall,\n",
        "            \"Macro Recall\" : macro_recall,\n",
        "            \"Weighted Precision\" : weighted_precision,\n",
        "            \"Micro Precision\" : micro_precision,\n",
        "            \"Macro Precision\" : macro_precision\n",
        "            }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1EYt1FZslof5"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "WP3ndiTRlof5"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=MODEL_NAME,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    auto_find_batch_size=True,\n",
        "    gradient_checkpointing=True,\n",
        "    gradient_accumulation_steps=GRAD_ACC_STEPS,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    log_level=\"error\",\n",
        "    logging_strategy='steps',\n",
        "    group_by_length=True,\n",
        "    report_to=REPORTS_TO,\n",
        "    logging_steps=250,\n",
        "    evaluation_strategy='no',\n",
        "    save_strategy=STRATEGY,\n",
        "    logging_first_step=True,\n",
        "    disable_tqdm=False,\n",
        "    hub_private_repo=True,\n",
        "    fp16=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3sPPrpwIlof5"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "kJ05TD8rlof5"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer,\n",
        "                                        padding=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzEpgs7Slof5"
      },
      "source": [
        "#### Subclass Trainer to Handle Class Imbalance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "P93xfT6Vlof5"
      },
      "outputs": [],
      "source": [
        "class CustomTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.get(\"labels\")\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get(\"logits\")\n",
        "        loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor(\n",
        "            [\n",
        "                1.0,\n",
        "                1.0,\n",
        "                1.0,\n",
        "                1.0\n",
        "                ]\n",
        "            ).to(DEVICE)\n",
        "        )\n",
        "        loss = loss_fct(logits.view(-1,\n",
        "                                    self.model.config.num_labels\n",
        "                                    ),\n",
        "                        labels.view(-1)\n",
        "                        )\n",
        "        return (loss, outputs) if return_outputs else loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JlxDWivlof5"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "TUOElPI1lof5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "544a1ffd-de1d-40d5-ab41-34347b5bb4bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/deberta-base-mnli-Malicious_URLs is already a clone of https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/deberta-base-mnli-Malicious_URLs is already a clone of https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ],
      "source": [
        "trainer = CustomTrainer(model=model,\n",
        "                  args=training_args,\n",
        "                  compute_metrics=compute_metrics,\n",
        "                  train_dataset=encoded_data['train'],\n",
        "                  eval_dataset=encoded_data['eval'],\n",
        "                  data_collator=data_collator,\n",
        "                  tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umV50LBJlof5"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "8Nd7rywHlof5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e58e5585-f9bd-4074-e51e-155b70c2971c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='13205' max='13205' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [13205/13205 2:58:58, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.749600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.832500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.837200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.820600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.800200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>0.809200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.811000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1750</td>\n",
              "      <td>0.790300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.813200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2250</td>\n",
              "      <td>0.795000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.771100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2750</td>\n",
              "      <td>0.813200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.776100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3250</td>\n",
              "      <td>0.804500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.788500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3750</td>\n",
              "      <td>0.777900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.789100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4250</td>\n",
              "      <td>0.793700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.764500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4750</td>\n",
              "      <td>0.773100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.788900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5250</td>\n",
              "      <td>0.772400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.782500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5750</td>\n",
              "      <td>0.780800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.780000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6250</td>\n",
              "      <td>0.761300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.776000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6750</td>\n",
              "      <td>0.777800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.772500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7250</td>\n",
              "      <td>0.755600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.759700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7750</td>\n",
              "      <td>0.784200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.771300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8250</td>\n",
              "      <td>0.791600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.765300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8750</td>\n",
              "      <td>0.785700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.770300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9250</td>\n",
              "      <td>0.785700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.762900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9750</td>\n",
              "      <td>0.779000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.757500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10250</td>\n",
              "      <td>0.759500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>0.771700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10750</td>\n",
              "      <td>0.778000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>0.775900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11250</td>\n",
              "      <td>0.745700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11500</td>\n",
              "      <td>0.760900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11750</td>\n",
              "      <td>0.740300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.766600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12250</td>\n",
              "      <td>0.768900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12500</td>\n",
              "      <td>0.757700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12750</td>\n",
              "      <td>0.767100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>0.763600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KmSywCHlof5"
      },
      "source": [
        "#### Save Model & Metrics on Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "gJ3iXuR-lof5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584,
          "referenced_widgets": [
            "cc71c8fce1024f23832ca73e067fd179",
            "b2d95418e1dc48faaefbeea047996a5c",
            "207c1d07f2f94e519947fd38a29fe6d8",
            "9f064a1650dc40228eeb34e9c5bb24c8",
            "f31f0475e962460394a2c88084aa2c24",
            "fcf85775aab24d41b4f74b9f6f1bd298",
            "a6cf86da88f5484bbb780554f0237b7a",
            "6a0e524e3f5c41108f4b42634c0bdcc4",
            "584966d628a5465f84fdf7cd72e4ab03",
            "86d7d6f58add438b9f39bfe5455a85dc",
            "58a72e35ef3f4065829830b04cff517b",
            "d9071962b66f423aba31635e6dd21a0a",
            "f4e065bf007641799d740b90a0b4b895",
            "680b9d2610744d3d8c78428db1573cb7",
            "ecbda5fa43474674b0ece52e00e21a9b",
            "6c7448887b2a4c67b3507ce9369b03cd",
            "426609b84e00415eb345fea290e73658",
            "1f4fdb2b5c4041d599e04122c37f9721",
            "49eb30c6b521437ca27c9986134b8f56",
            "cc178dbd2b824c949cc8609a7bb9e959",
            "6456392dc21c4c4aa69f9ef9a34d86c1",
            "3c1ef3c9c3fe41fa9fdc2e5d2bf6f3cc"
          ]
        },
        "outputId": "cc8b59f4-70a0-4908-a5c3-4c59eed23e7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/531M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc71c8fce1024f23832ca73e067fd179"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Jun22_15-29-47_f7092f1b3349/events.out.tfevents.1687447922.f7092f1b3349.497.1:   0%|         …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9071962b66f423aba31635e6dd21a0a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   68682fa..a8f9119  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   68682fa..a8f9119  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   a8f9119..5496feb  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   a8f9119..5496feb  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        1.0\n",
            "  total_flos               = 36450310GF\n",
            "  train_loss               =       0.78\n",
            "  train_runtime            = 2:58:59.84\n",
            "  train_samples_per_second =     39.347\n",
            "  train_steps_per_second   =       1.23\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate Model"
      ],
      "metadata": {
        "id": "1Z1WqstdXfoT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = trainer.evaluate()\n",
        "\n",
        "print(metrics)"
      ],
      "metadata": {
        "id": "2mEOUUN9Xfg4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272,
          "referenced_widgets": [
            "fa00852a65904bc38a76c303266ffa4c",
            "2b0959d664a04d6189879e33958dc251",
            "1a866e3905dd4dac80fc1cabbe83bdeb",
            "cb0a7d66fa3e428caf2e9bc4dd53a712",
            "f787f2a8bb5042999e75b4f120e52886",
            "3850a284b6534ec3b96560b62e93015c",
            "07a9f6a167db40119ebab27c0fb14796",
            "6fa869a36e7540b093021baae6ed7972",
            "90fbed2f53774f938d3825a4ec8592a0",
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            "296e1a651b304b3685ae921bc40a52e2",
            "ede1709c929b4815a4753fb22062f5ba",
            "9a2b8c93e8b94bf2bc5077cadfcc2ee8",
            "d5486f3c92d94eb0b873c5e9a3f6e382",
            "22a15baa503f4bb691dd9712fe12a224",
            "64441f60ea5842e488c22bd44e54856d",
            "62c49bd263664cc29c129af087ab8e01",
            "db6cf869ce7c4045813d27b4e9f694f8",
            "f39de9bbd88546e69096f23c2e27c9b2",
            "622820030c78400e8f45c23ec6b91dd4",
            "c7f0f5e09c96432aaa927ce8e3abcbc3",
            "b13ac38e1b854d6592d08240e9e2a5e8",
            "e085c403365c4914b9aca18ad0de7b24",
            "932d412a64a2404e8a257b86cf56fab8",
            "985166a1b2fb48ef9248d0b5d8c4e32c",
            "ec21a7c8bf184dd08e2d56dc73473b5b",
            "38197cc5afda460d9446700eaade1d55",
            "4f2b0d6db9a54e9ab9e8033036809f6c",
            "f962eee4696b4904a1ad160710b0d9f5",
            "07cff1e631674beda4289dab17ad0fbb",
            "ee4538b5652748a5add29cafecb6d157",
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            "4715ba16be604e4eb12a88ece3616605",
            "12802642d21e4a62b819d87784a4786e",
            "4e36b839fecc443cb379279b4823c43b",
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            "dab7f7d8c73b4b4bae3351c01374ba4e",
            "900873bb5f0645e89147f4e89a5e690a",
            "8b79d97b3e6643429fce8135d58d7415",
            "12e38ddd786c4cad8cba8b5740d3ac52",
            "323c9f5014f344f1884be86514d568dd",
            "2ed3cd6d58d449429db0b58b72683742",
            "aa908f1682c94db8ba5d24c1183df920",
            "25df587b900146229b2c99421f7997c9"
          ]
        },
        "outputId": "353f13a3-9ade-424d-94e9-9abc66202e63"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='26018' max='26018' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [26018/26018 20:33]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa00852a65904bc38a76c303266ffa4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ede1709c929b4815a4753fb22062f5ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e085c403365c4914b9aca18ad0de7b24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12802642d21e4a62b819d87784a4786e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.758034884929657, 'eval_accuracy': 0.7353390538969338, 'eval_Weighted F1': 0.6585598476041514, 'eval_Micro F1': 0.7353390538969338, 'eval_Macro F1': 0.45325833939858134, 'eval_Weighted Recall': 0.7353390538969338, 'eval_Micro Recall': 0.7353390538969338, 'eval_Macro Recall': 0.43146982136714085, 'eval_Weighted Precision': 0.629120963844447, 'eval_Micro Precision': 0.7353390538969338, 'eval_Macro Precision': 0.5684138681336973, 'eval_runtime': 1250.5743, 'eval_samples_per_second': 166.434, 'eval_steps_per_second': 20.805, 'epoch': 1.0}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCkk__fqlof6"
      },
      "source": [
        "#### Push Model to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "BpR4tiIFlof6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
            "eb028cdcdc48493abd7c75dad27576d3",
            "03aa434d05ed4dfeb93cf15ab697e545",
            "7a9b56e7ba3749e790cff38fb75bafbe",
            "c7bf26b67c0a4699a576fd6d0bbe8459",
            "f0670be9fd0e4a49a963fa1d9f7d985d",
            "5c74b9bfb9444eb69634a06bb577afeb",
            "e79e3e17715d4110b2e287ac08d78244",
            "4c788c18ee9c44039949791b5da0091d",
            "cda9170239a444c1b0521c75fd4ba88c",
            "d795d97138214c569661573016fcb23e",
            "bce39e251a4d41bd86b5755ff0636a34"
          ]
        },
        "outputId": "61784a63-0459-4ec5-c1f1-2757546206fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Jun22_15-29-47_f7092f1b3349/events.out.tfevents.1687459988.f7092f1b3349.497.2: 100%|#########…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb028cdcdc48493abd7c75dad27576d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   5496feb..5aef2d0  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   5496feb..5aef2d0  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   5aef2d0..d3b7e40  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs\n",
            "   5aef2d0..d3b7e40  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/DunnBC22/deberta-base-mnli-Malicious_URLs/commit/5aef2d0e5c2e8aa9de6e6c47a4b0e3c8bd33019b'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "trainer.push_to_hub(commit_message=\"All Dunn!!!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnF_C77Wlof6"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "-\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAgsejO9lof6"
      },
      "source": [
        "### Citations\n",
        "- Model Checkpoint (RoBERTa)\n",
        "    > @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}}\n",
        "\n",
        "- Model Checkpoint (DeBERTa)\n",
        "    > @misc{he2021debertav3, title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, author={Pengcheng He and Jianfeng Gao and Weizhu Chen}, year={2021}, eprint={2111.09543}, archivePrefix={arXiv}, primaryClass={cs.CL}}\n"
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