Fine_tune_for_Cybersecurity_NER.ipynb

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            "_view_name": "LayoutView",
            "align_content": null,
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            "_model_module": "@jupyter-widgets/controls",
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            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
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            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/balnarendrasapa/cybersecurity-ner/blob/master/Fine_tune_for_Cybersecurity_NER.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/balnarendrasapa/cybersecurity-ner/raw/master/MITRE.zip"
      ],
      "metadata": {
        "id": "0vM3yftZ1mwH",
        "outputId": "41d325d3-5546-41fc-c5a9-b89179dea3f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-11-20 22:26:59--  https://github.com/balnarendrasapa/cybersecurity-ner/raw/master/MITRE.zip\n",
            "Resolving github.com (github.com)... 140.82.112.4\n",
            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/balnarendrasapa/cybersecurity-ner/master/MITRE.zip [following]\n",
            "--2023-11-20 22:26:59--  https://raw.githubusercontent.com/balnarendrasapa/cybersecurity-ner/master/MITRE.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 238392 (233K) [application/zip]\n",
            "Saving to: ‘MITRE.zip’\n",
            "\n",
            "MITRE.zip           100%[===================>] 232.80K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2023-11-20 22:27:00 (8.78 MB/s) - ‘MITRE.zip’ saved [238392/238392]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbFqLSOvZUrl",
        "outputId": "130a0e01-5c39-4e65-f0ff-a015dad503c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  MITRE.zip\n",
            "   creating: MITRE/\n",
            "  inflating: MITRE/test.txt          \n",
            "  inflating: MITRE/train.txt         \n",
            "  inflating: MITRE/valid.txt         \n"
          ]
        }
      ],
      "source": [
        "!unzip MITRE.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -q install accelerate transformers datasets seqeval"
      ],
      "metadata": {
        "id": "fcqGScRWZfPC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18f6232e-1bbb-4ff9-ead1-76ce40ca59e6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/261.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m256.0/261.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/521.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Login to the Huggingface\n",
        "\n",
        "- get huggingface write access token from huggingface account"
      ],
      "metadata": {
        "id": "JYi77skV6AIq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "369bc4a1b01e47e0952de7fda05af4a7",
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            "a0daac912d164f928ee754129f74edfd",
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            "7f4ca024572a44cab23338b35a58b46a",
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            "7691839c534947ad96da5904e2fdeab1",
            "24d6a474ce4842f78525bd8fa722dbd0",
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            "18c0c682db9343c1b92f244a9aff5454"
          ]
        },
        "id": "VB27r1WuoBat",
        "outputId": "7c5b6efb-5df7-42c1-8a2d-9991f6b3c1d9"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "369bc4a1b01e47e0952de7fda05af4a7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Select the base model and task and batch size"
      ],
      "metadata": {
        "id": "_RziBETz6FjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "task = \"ner\" # Should be one of \"ner\", \"pos\" or \"chunk\"\n",
        "model_checkpoint = \"distilbert-base-uncased\"\n",
        "batch_size = 16"
      ],
      "metadata": {
        "id": "G0nPq5f8oY23"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the train.txt, test.txt and valid.txt using datasets library."
      ],
      "metadata": {
        "id": "N_TAsMuu6LDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "raw_dataset = load_dataset(\"text\", data_files={\"train\": \"MITRE/train.txt\", \"test\": \"MITRE/test.txt\", \"valid\": \"MITRE/valid.txt\"})"
      ],
      "metadata": {
        "id": "IYZrA5K_oe6b"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUuwtub2Ph8B",
        "outputId": "974de523-2db2-46dc-a4ab-bb07ea11c028"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 71001\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 20017\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 20342\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Preprocessing"
      ],
      "metadata": {
        "id": "XXS79B7n6TM8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We need to convert the raw dataset to huggingface compatible dataset.\n",
        "- This is general preprocessing. In order to use it for fine-tuning, you need to again preprocess it according to the model's requirements"
      ],
      "metadata": {
        "id": "8XyVc3fq6ZNv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The function below seperates sentences and tags from the raw dataset"
      ],
      "metadata": {
        "id": "nb6r8BQX2dT4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def seperate_sentence_tags(dataset):\n",
        "    sentences = []\n",
        "    tags = []\n",
        "    sentence = []\n",
        "    tag = []\n",
        "    for i in dataset:\n",
        "        if i[\"text\"] != \"\":\n",
        "            word, word_tag = i[\"text\"].split(\"\\t\")\n",
        "            sentence += [word]\n",
        "            tag += [word_tag]\n",
        "            if word == \".\":\n",
        "                sentences += [sentence]\n",
        "                tags += [tag]\n",
        "                sentence = []\n",
        "                tag = []\n",
        "\n",
        "    return sentences, tags"
      ],
      "metadata": {
        "id": "aSvvVv-novjX"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The function below finds unique tags and converts them into a python dictionary"
      ],
      "metadata": {
        "id": "6INZY7ln2jwh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unique_tags_dict(tags):\n",
        "    unique_tags = set()\n",
        "    for i in tags:\n",
        "        for j in i:\n",
        "            unique_tags.add(j)\n",
        "\n",
        "    tags_dict = dict()\n",
        "    j = 0\n",
        "    unique_tags = sorted(list(unique_tags))\n",
        "    for i in unique_tags:\n",
        "        tags_dict[i] = j\n",
        "        j += 1\n",
        "\n",
        "    return unique_tags, tags_dict"
      ],
      "metadata": {
        "id": "L0beQYm6taS5"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The function below converts tags to its assigned numbers in each sentence"
      ],
      "metadata": {
        "id": "7hzz1ps_2wjA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tag_to_num(tags_dict, tags):\n",
        "    tags_nums = []\n",
        "    for i in tags:\n",
        "        temp = [tags_dict[j] for j in i]\n",
        "        tags_nums += [temp]\n",
        "        temp = []\n",
        "    return tags_nums"
      ],
      "metadata": {
        "id": "DQ5rRV9B-CQO"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The function below creates a huggingface compatible dataset.\n",
        "\n",
        "- It creates a dataset with the help of the functions above. it takes in sentences, tags_num (which is nothing but tags converted to numbers), unique_tags(list of unique tags in the dataset)."
      ],
      "metadata": {
        "id": "KtmrS1U23Gjo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, Features, ClassLabel, Value, Sequence\n",
        "\n",
        "def dataset_hf(sentences, tags_nums, unique_tags):\n",
        "    dataset = Dataset.from_dict({\n",
        "        \"id\": list(range(len(sentences))),\n",
        "        \"tokens\": sentences,\n",
        "        \"ner_tags\": tags_nums\n",
        "        },\n",
        "        features = Features({\n",
        "            \"id\": Value(dtype='string'),\n",
        "            \"ner_tags\" : Sequence(ClassLabel(names = list(unique_tags))),\n",
        "            \"tokens\": Sequence(Value(dtype = \"string\"))\n",
        "        }\n",
        "    ))\n",
        "    return dataset"
      ],
      "metadata": {
        "id": "8fIlcC6bF4Kb"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Combine the functions above into a single function\n",
        "\n",
        "- The function below returns a huggingface compatible dataset by using all the above functions defined to pre-process the dataset"
      ],
      "metadata": {
        "id": "P1cK2SFV3n8f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def dataset_preprocess(dataset):\n",
        "    sentences, tags = seperate_sentence_tags(dataset)\n",
        "    unique_tags, tags_dict = unique_tags_dict(tags)\n",
        "    tags_nums = tag_to_num(tags_dict, tags)\n",
        "    return dataset_hf(sentences, tags_nums, unique_tags)"
      ],
      "metadata": {
        "id": "Aq7DjMrvvVXj"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_dataset[\"test\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2N94CPUPBRN",
        "outputId": "1c87e82c-35e5-4f9f-b023-28b48c03a505"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'Why\\tO'}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocess the train, test, validation dataset"
      ],
      "metadata": {
        "id": "1IxHUYyB3332"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = dataset_preprocess(raw_dataset[\"train\"])\n",
        "test_dataset = dataset_preprocess(raw_dataset[\"test\"])\n",
        "validation_dataset = dataset_preprocess(raw_dataset[\"valid\"])"
      ],
      "metadata": {
        "id": "TGIXpSjAG4Lb"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Convert them into a single whole dataset dictionary"
      ],
      "metadata": {
        "id": "YZJ6F2Ma4H_B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict\n",
        "\n",
        "\n",
        "datasets = DatasetDict({\n",
        "    'train':train_dataset,\n",
        "    'test': test_dataset,\n",
        "    'validation':validation_dataset\n",
        "})\n",
        "\n",
        "datasets"
      ],
      "metadata": {
        "id": "x3rJquqNOvhq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17b08130-e58b-40d0-e9fb-c99ecd626810"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'ner_tags'],\n",
              "        num_rows: 2664\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'ner_tags'],\n",
              "        num_rows: 717\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'tokens', 'ner_tags'],\n",
              "        num_rows: 785\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Push it to the huggingface\n",
        "\n",
        "- here `bnsapa/cybersecurity-ner` is repository from my huggingface account."
      ],
      "metadata": {
        "id": "SDMZxDZR4NNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasets.push_to_hub(\"bnsapa/cybersecurity-ner\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
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        },
        "id": "h-p6pfx-dUGf",
        "outputId": "aedcd3a6-fc5e-4163-9b76-dc477c42a40d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/3 [00:00<?, ?ba/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "228c5e8ce1be4d758c6712908308ecd1"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e4d4948be984fb2a84d2f6e81c7d22e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "3980ebcd56284136a5b3e745fbcd7032"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68bd3d21ad5c485d904b6dd8a978b701"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc57834ede464fd0a9f0c8331211cfaf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/880 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73e95c867b8c4fbf875c0c169abdcd6a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### You can import the preprocessed dataset that is pushed to huggingface by below code"
      ],
      "metadata": {
        "id": "QBA9Gs134YLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "datasets = load_dataset(\"bnsapa/cybersecurity-ner\")"
      ],
      "metadata": {
        "id": "xOqIiQvm4eCR"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# See Random datapoints in the dataset\n",
        "\n",
        "- the function below prints the random elements from the dataset"
      ],
      "metadata": {
        "id": "U77cT9Ie4q07"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import ClassLabel, Sequence\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=10):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "\n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "        elif isinstance(typ, Sequence) and isinstance(typ.feature, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda x: [typ.feature.names[i] for i in x])\n",
        "    display(HTML(df.to_html()))"
      ],
      "metadata": {
        "id": "_lHngpsnMzFF"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(datasets[\"train\"], num_examples = 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "g6egDMuEOBhZ",
        "outputId": "b8146101-a9a5-4479-81e6-88890527db09"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tokens</th>\n",
              "      <th>ner_tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2619</td>\n",
              "      <td>[The, malware, was, able, to, perform, overlay, attacks, and, become, the, default, SMS, app, through, the, abuse, of, the, Accessibility, Service, .]</td>\n",
              "      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>456</td>\n",
              "      <td>[We, expect, it, to, churn, out, new, variants, with, even, more, sophisticated, techniques, .]</td>\n",
              "      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>102</td>\n",
              "      <td>[], com, hxxp, :, //mailsa-wqq, [, .]</td>\n",
              "      <td>[I-Indicator, I-Indicator, B-Indicator, I-Indicator, I-Indicator, I-Indicator, I-Indicator]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1126</td>\n",
              "      <td>[According, to, Google, ,, whom, we, have, contacted, to, alert, about, our, discoveries, ,, nearly, 25, variants, of, this, spyware, were, uploaded, on, Google, Play, Store, .]</td>\n",
              "      <td>[O, O, B-Organization, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-System, I-System, I-System, O]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1003</td>\n",
              "      <td>[Within, this, time, period, ,, we, identified, close, to, 300, samples, belonging, to, this, family, (, all, sample, hashes, are, listed, in, the, Appendix, ), .]</td>\n",
              "      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# See an example and dataset's features"
      ],
      "metadata": {
        "id": "4ke2zvam43oi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(datasets[\"train\"][1082])\n",
        "\n",
        "datasets[\"train\"].features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEwswer9cVkw",
        "outputId": "c9231ee3-a45c-48bc-de6c-547493914e00"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': '1082', 'tokens': ['RuMMS', 'samples', 'and', 'C2', 'servers', 'Figure', '8', 'shows', 'how', 'these', 'samples', ',', 'C2', 'servers', 'and', 'hosting', 'websites', 'are', 'related', 'to', 'each', 'other', ',', 'including', 'when', 'they', 'were', 'compiled', 'or', 'observed', '.'], 'ner_tags': [1, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': Value(dtype='string', id=None),\n",
              " 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'ner_tags': Sequence(feature=ClassLabel(names=['B-Indicator', 'B-Malware', 'B-Organization', 'B-System', 'B-Vulnerability', 'I-Indicator', 'I-Malware', 'I-Organization', 'I-System', 'I-Vulnerability', 'O'], id=None), length=-1, id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the pretrained model"
      ],
      "metadata": {
        "id": "1Qy8uxxA4-c2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "id": "cLFKYAP_OE8R"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing the dataset for the pretrained model\n",
        "\n",
        "- We need to preprocess the model according the pretrained model's requirements.\n",
        "- Get the tokenizer for the model"
      ],
      "metadata": {
        "id": "KbtAeEr15VxS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers"
      ],
      "metadata": {
        "id": "Tfrz20KaRG8N"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenizer converts the words into numbers that are understandable by the model"
      ],
      "metadata": {
        "id": "MKo31Gvs6RkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(\"Hello, this is one sentence!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGutm3DFRI1j",
        "outputId": "82f8ce94-46cb-4b1e-a1de-26e95e38cc39"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 7592, 1010, 2023, 2003, 2028, 6251, 999, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer([\"Hello\", \",\", \"this\", \"is\", \"one\", \"sentence\", \"split\", \"into\", \"words\", \".\"], is_split_into_words=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CUJrIOCRKos",
        "outputId": "1cfabedd-7970-4633-ec22-0657f47fe527"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 7592, 1010, 2023, 2003, 2028, 6251, 3975, 2046, 2616, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = datasets[\"train\"][4]\n",
        "print(example[\"tokens\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-M5tLm7RNU4",
        "outputId": "239c6757-1b60-49d4-dff6-5e8a8e9b3b97"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['The', 'authors', 'are', 'trying', 'to', 'latch', 'onto', 'the', 'popularity', 'of', 'the', 'Super', 'Mario', 'Run', 'game', 'to', 'target', 'eagerly', 'waiting', 'Android', 'users', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accounting for splitting of words by tokenizer\n",
        "\n",
        "- Sometimes tokenizer splits the words like `sheepmeat` is split into three words `sheap`, `##me`, `##at`.\n",
        "- we need to add ner tags for these words too.\n",
        "- This is done by the code in this section."
      ],
      "metadata": {
        "id": "wrPE1tCB7Df6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_input = tokenizer(example[\"tokens\"], is_split_into_words=True)\n",
        "tokens = tokenizer.convert_ids_to_tokens(tokenized_input[\"input_ids\"])\n",
        "print(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdcKAKyeRO5Y",
        "outputId": "b671b10b-4f15-4103-8545-b3800c63e68a"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'the', 'authors', 'are', 'trying', 'to', 'latch', 'onto', 'the', 'popularity', 'of', 'the', 'super', 'mario', 'run', 'game', 'to', 'target', 'eagerly', 'waiting', 'android', 'users', '.', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(example[f\"{task}_tags\"]), len(tokenized_input[\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyjJCfg1RXIj",
        "outputId": "a9fa465f-2644-4ac6-de77-bf8c358275fe"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenized_input.word_ids())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bl56iiGqRZb6",
        "outputId": "a7b9b589-33f2-44d0-ee59-1a5cf86a12fe"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[None, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, None]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set the labels of all special tokens to -100 (the index that is ignored by PyTorch)"
      ],
      "metadata": {
        "id": "Ifg38Ga475VW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_ids = tokenized_input.word_ids()\n",
        "aligned_labels = [-100 if i is None else example[f\"{task}_tags\"][i] for i in word_ids]\n",
        "print(len(aligned_labels), len(tokenized_input[\"input_ids\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYA6pYNORa8U",
        "outputId": "246fa19b-17eb-4b66-ae83-30d54deca8e2"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24 24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_all_tokens = True"
      ],
      "metadata": {
        "id": "Oa2yNlNeRpTj"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Write custom function to tokenize and align labels"
      ],
      "metadata": {
        "id": "Q2rIILBY8HbK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n",
        "\n",
        "    labels = []\n",
        "    for i, label in enumerate(examples[f\"{task}_tags\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "        previous_word_idx = None\n",
        "        label_ids = []\n",
        "        for word_idx in word_ids:\n",
        "            # Special tokens have a word id that is None. We set the label to -100 so they are automatically\n",
        "            # ignored in the loss function.\n",
        "            if word_idx is None:\n",
        "                label_ids.append(-100)\n",
        "            # We set the label for the first token of each word.\n",
        "            elif word_idx != previous_word_idx:\n",
        "                label_ids.append(label[word_idx])\n",
        "            # For the other tokens in a word, we set the label to either the current label or -100, depending on\n",
        "            # the label_all_tokens flag.\n",
        "            else:\n",
        "                label_ids.append(label[word_idx] if label_all_tokens else -100)\n",
        "            previous_word_idx = word_idx\n",
        "\n",
        "        labels.append(label_ids)\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs"
      ],
      "metadata": {
        "id": "b36sySluRch_"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenize_and_align_labels(datasets['train'][:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ab_4mjaRe-D",
        "outputId": "db8ad69f-c2ff-469c-d8aa-2b4749fbb550"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[101, 3565, 7986, 2448, 15451, 8059, 1001, 1016, 1516, 2852, 9314, 17364, 9350, 27911, 2015, 2293, 7986, 1998, 20421, 1010, 2021, 2061, 2079, 15451, 8059, 6048, 1012, 102], [101, 1037, 2261, 2420, 2067, 1010, 2057, 2626, 2055, 2019, 11924, 28791, 2099, 23445, 8349, 20540, 2004, 1996, 3565, 7986, 2448, 2208, 2005, 11924, 1012, 102], [101, 2057, 2031, 2179, 2178, 6013, 1997, 15451, 8059, 20540, 2004, 1996, 3565, 7986, 2448, 11924, 10439, 1010, 1998, 2023, 2051, 2009, 2038, 2579, 1996, 2433, 1997, 2852, 9314, 17364, 9350, 1006, 6556, 3229, 23445, 1007, 1012, 102], [101, 6947, 8400, 2626, 2055, 1996, 2852, 9314, 17364, 9350, 2217, 1011, 8209, 2007, 1996, 20421, 2175, 10439, 2067, 1999, 2251, 2355, 1025, 1996, 4489, 2182, 2003, 2008, 2045, 2003, 2053, 2208, 2443, 1999, 1996, 24391, 7427, 1012, 102], [101, 1996, 6048, 2024, 2667, 2000, 25635, 3031, 1996, 6217, 1997, 1996, 3565, 7986, 2448, 2208, 2000, 4539, 17858, 3403, 11924, 5198, 1012, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-100, 1, 6, 6, 6, 6, 10, 10, 10, 1, 1, 1, 6, 10, 10, 10, 3, 10, 3, 10, 10, 10, 10, 10, 10, 10, 10, -100], [-100, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 1, 1, 10, 10, 10, 10, 10, 3, 8, 8, 10, 10, 3, 10, -100], [-100, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 8, 8, 3, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1, 1, 1, 6, 10, 10, 10, 10, 10, 10, -100], [-100, 2, 2, 10, 10, 10, 1, 1, 1, 6, 10, 10, 10, 10, 10, 3, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, -100], [-100, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 8, 8, 10, 10, 10, 10, 10, 3, 10, 10, -100]]}"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = datasets.map(tokenize_and_align_labels, batched=True)"
      ],
      "metadata": {
        "id": "b4rwKsZ6RgsS"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### See the ner labels"
      ],
      "metadata": {
        "id": "px44iIbK8Ww3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_list = datasets[\"train\"].features[f\"{task}_tags\"].feature.names\n",
        "label_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZ8UFS6SSBrb",
        "outputId": "76275488-7be3-45e1-a55d-f7d5acc98c41"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['B-Indicator',\n",
              " 'B-Malware',\n",
              " 'B-Organization',\n",
              " 'B-System',\n",
              " 'B-Vulnerability',\n",
              " 'I-Indicator',\n",
              " 'I-Malware',\n",
              " 'I-Organization',\n",
              " 'I-System',\n",
              " 'I-Vulnerability',\n",
              " 'O']"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune the model"
      ],
      "metadata": {
        "id": "qKhKqOho8Z6n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoConfig"
      ],
      "metadata": {
        "id": "YKOdtoC40MFU"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_, tags = seperate_sentence_tags(raw_dataset[\"train\"])\n",
        "_, tags_dict = unique_tags_dict(tags)"
      ],
      "metadata": {
        "id": "FJuIvIOJ1zOt"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tags_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8bhQpyU2hep",
        "outputId": "fb7902c7-76f3-4c15-b88c-276ed1ffadf4"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'B-Indicator': 0,\n",
              " 'B-Malware': 1,\n",
              " 'B-Organization': 2,\n",
              " 'B-System': 3,\n",
              " 'B-Vulnerability': 4,\n",
              " 'I-Indicator': 5,\n",
              " 'I-Malware': 6,\n",
              " 'I-Organization': 7,\n",
              " 'I-System': 8,\n",
              " 'I-Vulnerability': 9,\n",
              " 'O': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids_dict = {value:key for key, value in tags_dict.items()}"
      ],
      "metadata": {
        "id": "DW0PZp512ouk"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ids_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILT-0nGU2z5-",
        "outputId": "bc706f53-9f6d-4458-8de4-4a631539662c"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'B-Indicator',\n",
              " 1: 'B-Malware',\n",
              " 2: 'B-Organization',\n",
              " 3: 'B-System',\n",
              " 4: 'B-Vulnerability',\n",
              " 5: 'I-Indicator',\n",
              " 6: 'I-Malware',\n",
              " 7: 'I-Organization',\n",
              " 8: 'I-System',\n",
              " 9: 'I-Vulnerability',\n",
              " 10: 'O'}"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained(model_checkpoint, num_labels=len(label_list), label2id=tags_dict, id2label=ids_dict)"
      ],
      "metadata": {
        "id": "P_No-U_f1I0W"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, config = config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffd3dDbWRsN5",
        "outputId": "cb0c3179-080e-4305-c081-cef9259d8e4d"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForTokenClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Instantiate TrainingArguments object\n",
        "\n",
        "- This objects holds all the training hyperparameters"
      ],
      "metadata": {
        "id": "560Il5ac8pEA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = TrainingArguments(\n",
        "    f\"cybersecurity-ner\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=8,\n",
        "    weight_decay=0.01,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "_AEQ4T5gRumx"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer)"
      ],
      "metadata": {
        "id": "ffMeOf4IRwKU"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"seqeval\")"
      ],
      "metadata": {
        "id": "wQr87ejgST41"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [label_list[i] for i in example[f\"{task}_tags\"]]\n",
        "metric.compute(predictions=[labels], references=[labels])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqQfzbSsSMh0",
        "outputId": "968d0faf-795d-47ff-af41-c9c613146e3a"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'System': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2},\n",
              " 'overall_precision': 1.0,\n",
              " 'overall_recall': 1.0,\n",
              " 'overall_f1': 1.0,\n",
              " 'overall_accuracy': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Due3MA4Ea06i",
        "outputId": "97d1b9f2-0b33-44a2-b2b9-cb56b3137e28"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-System', 'I-System', 'I-System', 'O', 'O', 'O', 'O', 'O', 'B-System', 'O', 'O']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Write function to calculate metrics"
      ],
      "metadata": {
        "id": "M5iIgB5685mi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    return {\n",
        "        \"precision\": results[\"overall_precision\"],\n",
        "        \"recall\": results[\"overall_recall\"],\n",
        "        \"f1\": results[\"overall_f1\"],\n",
        "        \"accuracy\": results[\"overall_accuracy\"],\n",
        "    }"
      ],
      "metadata": {
        "id": "KXviI3ewSbQQ"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create a Trainer object to train the model"
      ],
      "metadata": {
        "id": "gSsBrCdD9D3o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "YRicHRPfSi8a"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train the model"
      ],
      "metadata": {
        "id": "v_7SGoKQ9IuM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "uvVGCMy2Skp6",
        "outputId": "9335949c-1066-43fd-aef8-ba8f71c9b88b"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1336' max='1336' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1336/1336 05:00, Epoch 8/8]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.235000</td>\n",
              "      <td>0.681042</td>\n",
              "      <td>0.764780</td>\n",
              "      <td>0.720486</td>\n",
              "      <td>0.933544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.195180</td>\n",
              "      <td>0.783037</td>\n",
              "      <td>0.749057</td>\n",
              "      <td>0.765670</td>\n",
              "      <td>0.945671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.211700</td>\n",
              "      <td>0.191017</td>\n",
              "      <td>0.782424</td>\n",
              "      <td>0.753145</td>\n",
              "      <td>0.767505</td>\n",
              "      <td>0.947597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.211700</td>\n",
              "      <td>0.189538</td>\n",
              "      <td>0.776535</td>\n",
              "      <td>0.807547</td>\n",
              "      <td>0.791737</td>\n",
              "      <td>0.951899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.211700</td>\n",
              "      <td>0.208149</td>\n",
              "      <td>0.784735</td>\n",
              "      <td>0.792138</td>\n",
              "      <td>0.788419</td>\n",
              "      <td>0.951203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.050500</td>\n",
              "      <td>0.213356</td>\n",
              "      <td>0.771611</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.785549</td>\n",
              "      <td>0.951080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.050500</td>\n",
              "      <td>0.227449</td>\n",
              "      <td>0.791411</td>\n",
              "      <td>0.770755</td>\n",
              "      <td>0.780946</td>\n",
              "      <td>0.949891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.050500</td>\n",
              "      <td>0.223270</td>\n",
              "      <td>0.785625</td>\n",
              "      <td>0.790566</td>\n",
              "      <td>0.788088</td>\n",
              "      <td>0.951366</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are 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": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1336, training_loss=0.10545385240794655, metrics={'train_runtime': 303.8856, 'train_samples_per_second': 70.132, 'train_steps_per_second': 4.396, 'total_flos': 709028875718064.0, 'train_loss': 0.10545385240794655, 'epoch': 8.0})"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "wJSbc5XWSmVH",
        "outputId": "b765e841-d07d-4043-eee5-e244961c45da"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='50' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [50/50 00:02]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.22326971590518951,\n",
              " 'eval_precision': 0.785625,\n",
              " 'eval_recall': 0.7905660377358491,\n",
              " 'eval_f1': 0.7880877742946708,\n",
              " 'eval_accuracy': 0.9513664112754537,\n",
              " 'eval_runtime': 3.0606,\n",
              " 'eval_samples_per_second': 256.489,\n",
              " 'eval_steps_per_second': 16.337,\n",
              " 'epoch': 8.0}"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# See how it is working on Test dataset"
      ],
      "metadata": {
        "id": "Z6A9vvHe9MFB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions, labels, _ = trainer.predict(tokenized_datasets[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "# Remove ignored index (special tokens)\n",
        "true_predictions = [\n",
        "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "\n",
        "results = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "eMM9_5YfSptw",
        "outputId": "01560a6a-c941-4209-db31-e78f3f557e43"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Indicator': {'precision': 0.7929039722329349,\n",
              "  'recall': 0.888504753673293,\n",
              "  'f1': 0.8379865498267781,\n",
              "  'number': 2314},\n",
              " 'Malware': {'precision': 0.7255639097744361,\n",
              "  'recall': 0.6380165289256199,\n",
              "  'f1': 0.6789797713280562,\n",
              "  'number': 605},\n",
              " 'Organization': {'precision': 0.65,\n",
              "  'recall': 0.4315352697095436,\n",
              "  'f1': 0.5187032418952617,\n",
              "  'number': 241},\n",
              " 'System': {'precision': 0.39635535307517084,\n",
              "  'recall': 0.4371859296482412,\n",
              "  'f1': 0.4157706093189964,\n",
              "  'number': 398},\n",
              " 'Vulnerability': {'precision': 0.7090909090909091,\n",
              "  'recall': 0.8297872340425532,\n",
              "  'f1': 0.764705882352941,\n",
              "  'number': 47},\n",
              " 'overall_precision': 0.7300873246890712,\n",
              " 'overall_recall': 0.7653259361997226,\n",
              " 'overall_f1': 0.7472914409534128,\n",
              " 'overall_accuracy': 0.9340902685509522}"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Metrics\n",
        "\n",
        "- Accuracy on the test dataset is nearly 93 %.\n",
        "- The F1-Score is nearly 74 %."
      ],
      "metadata": {
        "id": "amFLIC-i_xzS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Push the model to Huggingface"
      ],
      "metadata": {
        "id": "1iFE137X9agk"
      }
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    {
      "cell_type": "code",
      "source": [
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}