Phi3_toxicity.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jlonge4/gen_ai_utils/blob/main/Phi3_toxicity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5Zkk6paTsJ4"
      },
      "source": [
        "## Fine Tuning Phi3 To Predict Toxic Language (NSFW)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VA1mMiuivRS",
        "outputId": "73e8bd33-c791-462d-8cf1-469f84ff42ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: flash-attn\n",
            "  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for flash-attn: filename=flash_attn-2.5.9.post1-cp310-cp310-linux_x86_64.whl size=120889689 sha256=5022ba11d48bf74926da9c16260f4ea2b9bb7f4e29bdb4bd6e1383ad1c55d16f\n",
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            "Installing collected packages: xxhash, shtab, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, einops, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, tyro, nvidia-cusolver-cu12, datasets, flash-attn, bitsandbytes, accelerate, trl, peft\n",
            "Successfully installed accelerate-0.30.1 bitsandbytes-0.43.1 datasets-2.19.1 dill-0.3.8 einops-0.8.0 flash-attn-2.5.9.post1 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 peft-0.11.1 shtab-1.7.1 trl-0.8.6 tyro-0.8.4 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets accelerate peft bitsandbytes trl flash-attn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yU9PCD9m2a05",
        "outputId": "97af9eb5-a281-4dd4-8b3a-8f847255d7d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.41.0\n",
            "    Uninstalling transformers-4.41.0:\n",
            "      Successfully uninstalled transformers-4.41.0\n",
            "Successfully installed transformers-4.41.1\n"
          ]
        }
      ],
      "source": [
        "!pip install -U transformers"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "rI4l60Udi1Wg",
        "outputId": "41a530d0-0c8e-466e-d393-ac4c4be8fafd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/35.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "513b5f35ad9d41aeb92439b065af9585"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/8.85M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e89d9e74493b41379acfb727302a2f27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/17.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85d80d1394374f119bd12c5477599bcc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/6.18M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f168a4faab149f89565c18469cd9507"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/127656 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3ef9a7224204284abe1a42b2b74c39b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/31915 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70b889d734b24efc889703a5e0de1f1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/63978 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9545f51cd41c4b60ae7bb7387dfa3c56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating balanced_train split:   0%|          | 0/25868 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29d3c8b91bd544eb873607e585f5be6a"
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          },
          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ec101d91-d041-4e1a-af6d-7d39e740fb02 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 25868,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25868,\n        \"samples\": [\n          \"8126decfebcbf312\",\n          \"d4e66ecf91ca3385\",\n          \"7a6d623984d9c5f7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25868,\n        \"samples\": [\n          \"ps - i dont need new articles, as the articles cited dont add up to the article on wikipedia. there are many simple and obvious issues and problems with the way the thing is typed out. by all means meet me on the talk pages, and explain how 13/13 outlets had machida, but espn does not. explain how dana f white is a neutral or relevant authority on mma judging, or how davis had no significant offence, and machida blocked takedowns, when davis takedowns and gnp is scored and machidas tdd is not. these are facts, not opinion. if you want me to cite the unified rules, and dana white litany of bs- i will happily do so. thanks. 77.101.41.108\",\n          \"Sory ToDST that was All My Roomates! They came in and Stormed my computer and Called You Gay! Which Your Not!\",\n          \"If you need a million badges on your page you've missed the point!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"OxAISH-AL-LLM/wiki_toxic\")\n",
        "train = dataset['balanced_train']\n",
        "df = train.to_pandas()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9FoQLRqBjxXA",
        "outputId": "c4cf40f6-26b1-4296-da1d-29f6ebc5b11d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id                                       comment_text  label\n",
              "0  653b15d1deea659e  \"\\nNot if I can help it. Mudslide is more like...      0\n",
              "1  57c5f0e88e9e5219      Footnotes/references\\nOk. I have no objection      0\n",
              "2  e78a6eb06dbddd9a  .\\nI am a loser and I need your help to get a ...      0\n",
              "3  435008cc6f750098  \"\\nI think I've decided that I have no strong ...      0\n",
              "4  2c60f0fc97d86718  Quite frankly nothing is helping due to his ar...      1"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <td>Footnotes/references\\nOk. I have no objection</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e78a6eb06dbddd9a</td>\n",
              "      <td>.\\nI am a loser and I need your help to get a ...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>435008cc6f750098</td>\n",
              "      <td>\"\\nI think I've decided that I have no strong ...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2c60f0fc97d86718</td>\n",
              "      <td>Quite frankly nothing is helping due to his ar...</td>\n",
              "      <td>1</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5fe8389-d7d9-4a2b-a096-2ff8c74e445c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d5fe8389-d7d9-4a2b-a096-2ff8c74e445c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d5fe8389-d7d9-4a2b-a096-2ff8c74e445c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8af41f29-4057-42d0-8432-1a7c583dca3e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8af41f29-4057-42d0-8432-1a7c583dca3e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8af41f29-4057-42d0-8432-1a7c583dca3e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 21511,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21511,\n        \"samples\": [\n          \"8e62849fbdeaeb9f\",\n          \"447bd7740d17a545\",\n          \"8f290a730b6c4a40\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21511,\n        \"samples\": [\n          \"So someone pisses at me and I'm supposed to just smile and do as I'm told because they can't be bothered to write something that THEY feel is notable?\",\n          \"Go fuck yourself this ain't any of your business and i fucking know what tor is. Asshole.\",\n          \"you wanna know what I think tony? \\n\\nand yeh\\n\\ngo ahed n label this az Iamandrewrice\\n\\nit wil only make me laugh at ur ignorance even mor\\n\\nur just a homofobe yeh blatez!\\n\\n'sup blud?\\n\\nshor mayte!\\n\\nwanna no wot the prob iz with u?\\n\\nu need 2 f'in get a lyf mayt!\\n\\nget a gf!\\n\\nget a bf!\\n\\nbut for f's sake!\\n\\ndont take out ur ugliness on other peeps!\\n\\nf yeah!\\n\\nryte then,\\n\\na'm off ter vandalize now\\n\\nblatez lolz\\n\\nyeh babe!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = df[df['comment_text'].apply(lambda x: len(x) <= 512 and len(x) > 15)]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p38tai6s7ro9",
        "outputId": "cbb24f9f-2102-41d3-adfa-4b744387755e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21511"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fRETjG2KNO_",
        "outputId": "b3bec129-15e5-4b90-da9b-1e4cca43d797"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418,
          "referenced_widgets": [
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        {
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        {
          "output_type": "stream",
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b2c8f957d514617bf19e0526af1997c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adc59217144845e69811c555759c76a4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "model_id = \"microsoft/Phi-3-mini-4k-instruct\"\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8a575QkP8Ot7",
        "outputId": "0134f20a-657c-4e88-f538-87c99908c1f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                               2c60f0fc97d86718\n",
              "comment_text    Quite frankly nothing is helping due to his ar...\n",
              "label                                                           1\n",
              "Name: 4, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.iloc[4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQA430us1nnB",
        "outputId": "6be03839-0ea8-4d80-a1c1-e57915eecb3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "You are not running the flash-attention implementation, expect numerical differences.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " True\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "\n",
        "torch.random.manual_seed(0)\n",
        "\n",
        "text = f\"Is the following language toxic? ###{df.iloc[4].comment_text} Reply with only True or False?\"\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": text}\n",
        "]\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "\n",
        "generation_args = {\n",
        "    \"max_new_tokens\": 1,\n",
        "    \"return_full_text\": False,\n",
        "    \"temperature\": 0.0,\n",
        "    \"do_sample\": False,\n",
        "}\n",
        "\n",
        "output = pipe(messages, **generation_args)\n",
        "print(output[0]['generated_text'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5WbLWrN-y9T",
        "outputId": "4b6e143f-7041-4c15-e20d-cffe79db6580"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "toxic        11306\n",
              "non-toxic    10205\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df_ = df.copy()\n",
        "df_['label'] = df_['label'].map({0: 'non-toxic', 1: 'toxic'})\n",
        "df_['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "habT-V2T5PSN"
      },
      "outputs": [],
      "source": [
        "# import pandas as pd\n",
        "\n",
        "# df_0 = df_[df_['label'] == 'non-toxic']\n",
        "# df_1 = df_[df_['label'] == 'toxic']\n",
        "\n",
        "# # Get 500 samples from each class\n",
        "# df_0_test = df_0.sample(n=500, random_state=42)\n",
        "# df_1_test = df_1.sample(n=500, random_state=42)\n",
        "\n",
        "# df_train = pd.concat([df_0_test, df_1_test], ignore_index=True)\n",
        "# df_train.label.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_.copy()"
      ],
      "metadata": {
        "id": "d2SfnmwcdsaU"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "q5_NNKSl7xyz"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "hf_dataset = Dataset.from_dict({'prompt': df_train['comment_text'].to_list(), 'label': df_train['label'].to_list()})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UVFDQBn8LWsh"
      },
      "outputs": [],
      "source": [
        "# del merged_model\n",
        "# del model\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "sjiNemD35tK5"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_dropout=0.1,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\"],#target_modules=[\"q_proj\", \"v_proj\"],\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "olhaUxgfFDRZ"
      },
      "outputs": [],
      "source": [
        "def formatting_func(example):\n",
        "    input = f\"\"\"\n",
        "    You are examining written text content. Here is the text:\n",
        "        [BEGIN DATA]\n",
        "        ************\n",
        "        [Text]: {example['prompt'][0]}\n",
        "        ************\n",
        "        [END DATA]\n",
        "\n",
        "    Examine the text and determine  whether the text is toxic or not. Toxicity is\n",
        "    defined as any comment that makes hateful statements, demeans or disparages\n",
        "    another user, uses inappropriate language, or threatens physical or emotional\n",
        "    violence. Please focus heavily on the concept of toxicity and do NOT mark\n",
        "    something toxic as non-toxic that is toxic.\n",
        "\n",
        "    Your response must be single word, either \"toxic\" or \"non-toxic\", and should not\n",
        "    contain any text or characters aside from that word. \"toxic\" means that the text\n",
        "    meets the definition of toxic. \"non-toxic\" means the text does not contain any\n",
        "    words, sentiments or meaning that could be considered toxic.\"\"\"\n",
        "    messages = [\n",
        "        {\"role\": \"user\", \"content\": input},\n",
        "        {\"role\": \"assistant\", \"content\": example['label'][0]}\n",
        "    ]\n",
        "    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors='pt')\n",
        "    return [text]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QP8cKCE0HVP7"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "fdb01b0260064fdf9e1b0b40ceb5bec3",
            "3a6bd15e2d7744caa0b39b1e835fe1b8",
            "d1f0abb2573a496ba2f7e8e55c23c901",
            "5a833bb9039a49b79babc97f0c2fbb73",
            "0b24fc79fd0c41108b7120f12b63efc4",
            "c351fe21ba5c4e3b9277c85f193787e2",
            "ab5ee73ebdc74af184129866135bc47f",
            "a8db214e2b7146ab9005837d880968c7",
            "51030d68840e4b5383f8f64e4296a6be",
            "cea31e265474407294cea131c10e98a4",
            "3c038b015d8e4020ac6a35fb985aa0fd"
          ]
        },
        "id": "WcAJW_uM4lin",
        "outputId": "60a29999-e5a9-4a19-c2bc-456d6edd0afc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:246: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/21511 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdb01b0260064fdf9e1b0b40ceb5bec3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:318: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='110' max='110' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [110/110 02:54, Epoch 20/22]\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>2.947200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.845200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.937900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.624300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.481400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.301700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.234900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.265600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.071500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.831100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.890900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.707100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.684000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.245000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.222100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.994200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.994100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.821300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.685300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.652000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.566900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.629000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.622000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.660900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.431900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.587400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.622500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.730900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.619300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.508900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.669600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.590200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.335300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.586800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.309000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.568200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.351800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.658000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.629000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.401200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.568600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.338900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.461300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.581100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.536400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.357000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.439300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.553800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.341200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.509200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.189900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.431000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.380600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.516200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.401900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.347000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.386200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.294600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.452200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.217600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.378500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.219600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.292900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.277400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.302100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.410900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.170500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.267700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.229300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.303600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.227200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.278600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.288100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.198900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.185300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.120600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.252700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.275200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.152700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.168800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.193700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.167800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.089400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.205600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.103700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.169800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.081400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.087100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.088100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.137600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.039700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.091400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.180300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.033700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.067600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.027200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.094700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.117800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.076500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.040300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.070500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>0.026500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.111800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.046600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.033700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>0.026100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.086300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.033300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>0.063600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.046400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=110, training_loss=0.5768926481462338, metrics={'train_runtime': 176.3431, 'train_samples_per_second': 2.495, 'train_steps_per_second': 0.624, 'total_flos': 2733936764313600.0, 'train_loss': 0.5768926481462338, 'epoch': 20.0})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "import transformers\n",
        "from trl import SFTTrainer\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=hf_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=10,\n",
        "        max_steps=110,\n",
        "        learning_rate=5e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "    ),\n",
        "    peft_config=lora_config,\n",
        "    formatting_func=formatting_func,\n",
        ")\n",
        "trainer.train()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2Znk1-WGGskK"
      },
      "outputs": [],
      "source": [
        "def format_input(text):\n",
        "    input = f\"\"\"\n",
        "    You are examining written text content. Here is the text:\n",
        "        [BEGIN DATA]\n",
        "        ************\n",
        "        [Text]: {text}\n",
        "        ************\n",
        "        [END DATA]\n",
        "\n",
        "    Examine the text and determine  whether the text is toxic or not. Toxicity is\n",
        "    defined as any comment that makes hateful statements, demeans or disparages\n",
        "    another user, uses inappropriate language, or threatens physical or emotional\n",
        "    violence. Please focus heavily on the concept of toxicity and do NOT mark\n",
        "    something toxic as non-toxic that is toxic.\n",
        "\n",
        "    Your response must be single word, either \"toxic\" or \"non-toxic\", and should not\n",
        "    contain any text or characters aside from that word. \"toxic\" means that the text\n",
        "    meets the definition of toxic. \"non-toxic\" means the text does not contain any\n",
        "    words, sentiments or meaning that could be considered toxic.\"\"\"\n",
        "    return input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMP8qdaC5utI",
        "outputId": "baed8653-359d-4509-d094-af87034d8cde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " toxic\n"
          ]
        }
      ],
      "source": [
        "input = format_input(df.iloc[4].comment_text)\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": input}\n",
        "]\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "\n",
        "generation_args = {\n",
        "    \"max_new_tokens\": 100,\n",
        "    \"return_full_text\": False,\n",
        "    \"temperature\": 0.0,\n",
        "    \"do_sample\": False,\n",
        "}\n",
        "\n",
        "output = pipe(messages, **generation_args)\n",
        "print(output[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "938032ed0c3143e4bead6a0eb05ad2e9",
            "3a471c0d4fcc4c119817f393d4578937",
            "fb2751a468ae4ae0a81a0b627c11db5b",
            "6d63b69e7e5b4fcea61dca642d113728",
            "4185971c61ef40cfbc7e584bfd48f68e",
            "504ab59d319a42078d067f0071ba1622",
            "b58c34c4ee5b4f7cacbe825c23d63af5",
            "e2b08b771335458f89a48f93768056ad",
            "ffb475144094476fab62249d5e17cf5c",
            "135c735f00cb4483b7a49bd7ec50deed",
            "40ae5c5307cf41ecbf623d5cbe1f44aa",
            "667f1efc34414f74b3fe97d478d0372c",
            "7c8bc7c66760456296cd54a09c554713",
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            "03100a0896b14ef5836d28deaa98f132",
            "9e52024b7c5642e2b4afbcded604b8c5",
            "d4acc0bc634e4762be904be06cb2e6fb",
            "9c0763bceb47428081e4ea84b2d630df",
            "c5c61d58f8b3449bab9061ced0697128",
            "1073e8733d8c41eea120c4fe3032b9a4",
            "a5667469e56744fba9705e7110a63e1f",
            "e8f2dadf14f947dca84bde09dbc7e88f",
            "23b2e152daec44e694221490c772cecd",
            "bcecc3e342024d089a6ad78532b02fc1",
            "4d8b27b39dce4de198f95579932c1bfc",
            "b5d41827e7e54884bccf2f375e8a6dd7",
            "b0c3453503fe4f288a233f66eb7fee21",
            "a312bbac3a58499aaf0994702f89ea20",
            "1e7c7b8911904af1ae9ce6588d381900"
          ]
        },
        "id": "jsMs2o2tMtFT",
        "outputId": "f6de0661-a4c6-4bb7-8516-d8edbffa5d31"
      },
      "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": "938032ed0c3143e4bead6a0eb05ad2e9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362,
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        },
        "id": "JVOF_ye1BmAX",
        "outputId": "9ae2e1c7-434e-49bb-c0ea-cf6fedbd4f6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "events.out.tfevents.1716837113.e97df906fa7a.1875.0:   0%|          | 0.00/7.67k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "037f3be3d45046acb85ad03ba02946ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "events.out.tfevents.1716837179.e97df906fa7a.1875.1:   0%|          | 0.00/6.84k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f35e0f5de49e4d6fa338a0f58a850b3e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 7 LFS files:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a583a6b526634f4d86d6364d001c3c6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/12.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "events.out.tfevents.1716837272.e97df906fa7a.1875.2:   0%|          | 0.00/44.3k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "ecba40d6728545ac97cc7bd07b59c2bf"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "events.out.tfevents.1716837580.e97df906fa7a.1875.3:   0%|          | 0.00/37.2k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "events.out.tfevents.1716837838.e97df906fa7a.1875.4:   0%|          | 0.00/28.7k [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "training_args.bin:   0%|          | 0.00/5.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91e249cb39ac416d9f84fed08e60a556"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Jlonge4/outputs/commit/ceca92730e88f44b5085250136d070b4c83927b3', commit_message='Jlonge4/outputs', commit_description='', oid='ceca92730e88f44b5085250136d070b4c83927b3', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "trainer.push_to_hub(\"Jlonge4/outputs\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nsj92ZL7TfR0"
      },
      "source": [
        "A Few More Examples..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYK075eDSR8R",
        "outputId": "aa5d04f6-aa57-4250-b8c7-9587e8bdedeb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                               ce83db2309349d6a\n",
              "comment_text    That's funny. I thought that all input counted...\n",
              "label                                                           0\n",
              "Name: 21239, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df.iloc[17670]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "awKgG2rX2_Ax"
      },
      "outputs": [],
      "source": [
        "# del model\n",
        "# del merged_model\n",
        "# del base_model\n",
        "# del model_peft\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pDGRW1wKSUHY",
        "outputId": "ec0e8252-e61d-44c6-c3d0-0b46d7c6c6a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " non-toxic\n"
          ]
        }
      ],
      "source": [
        "input = format_input(df.iloc[17670].comment_text)\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": input}\n",
        "]\n",
        "\n",
        "output = pipe(messages, **generation_args)\n",
        "print(output[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_3vEse_TVZt",
        "outputId": "8daf8ecd-f8ae-4e80-f277-af296e2d6a0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                               22ab5d37f2687983\n",
              "comment_text    You smell unfresh \\n\\nYou need to try a new br...\n",
              "label                                                           1\n",
              "Name: 605, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df.iloc[500]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fI7GcT1oSq9e",
        "outputId": "9942ae48-8b34-42f3-dcfb-8cd078aeed53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " non-toxic\n"
          ]
        }
      ],
      "source": [
        "input = format_input(df.iloc[501].comment_text)\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": input}\n",
        "]\n",
        "\n",
        "output = pipe(messages, **generation_args)\n",
        "print(output[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUk5UnfJ2mz4"
      },
      "source": [
        "Check performance of adapter merged into original model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "E5VJzMPZ2WOr",
        "outputId": "953fa191-f164-4224-d286-a7e3bbb99463"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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        {
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          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "757669c4c2534dc1b145d8f39876d668"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "02e496e25a794e939b13a77894753203"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "# config = PeftConfig.from_pretrained(\"Jlonge4/outputs\")\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\"microsoft/Phi-3-mini-4k-instruct\")\n",
        "model_peft = PeftModel.from_pretrained(base_model, \"Jlonge4/outputs\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "MUoId3P12gkZ"
      },
      "outputs": [],
      "source": [
        "merged_model = model_peft.merge_and_unload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5qUUK0UI36Vd",
        "outputId": "a6ee976e-979f-4a36-a36f-aabb3dd4a4be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "2  0002f87b16116a7f  \"::: Somebody will invariably try to add Relig...      0\n",
              "3  0003e1cccfd5a40a  \" \\n\\n It says it right there that it IS a typ...      0\n",
              "4  00059ace3e3e9a53  \" \\n\\n == Before adding a new product to the l...      0"
            ],
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              "      <td>\" \\n\\n == Before adding a new product to the l...</td>\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-47a4ac3b-115d-4b1a-9f2c-0ed6285d8598 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-47a4ac3b-115d-4b1a-9f2c-0ed6285d8598');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 63978,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 63978,\n        \"samples\": [\n          \"cbd83ffa0927bc8d\",\n          \"9fea96fb811c62aa\",\n          \"00978e3b8f542b9b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 63978,\n        \"samples\": [\n          \"\\\" \\n\\n == Alleged Original Research == \\n\\n I have added back a load of content that was removed supposedly under the reasoning that it was \\\"\\\"original research\\\"\\\". First, there was reliably sourced content that was removed. The \\\"\\\"Changes in direction\\\"\\\" section was already reliably sourced as were the quotes. How that falls under original research I'll never know. The show underwent some major changes and to omit that one of the main characters was killed off makes no sense whatsoever. The fact that two of the main stars, John Amos and Esther Rolle, publicly complained about the series and ultimately left because of it also doesn't fall under OR. This was covered by the media as evidenced by the sources that were present. If someone had a problem with Ebony and The LA Times being reliable sources, they should have dropped a note here instead of removing a shit ton of content that someone took the time to write (not me) and another person (me) took the time to source. Original research isn't including details about the work, it's drawing your own conclusions based on your own research. In television show articles, we can use the work itself as a source for the actual details of the show. Once the writing start veering off into overly detailed garbage, that's when the OR police can come out. Works of fiction should have some details about production, ratings, show changes, etc. If not, all we have is an article that say a show aired and some people were in it. It took me all of 20 minutes to find sources for the majority of the removed content - none of it was contentious nor was it OR which means it should not have been removed. Ironically, actual original research about the stupid opening titles was left in.  \\\"\",\n          \"\\u0412\\u0441\\u043f\\u043e\\u043c\\u0438\\u043d\\u0430\\u044f...== \\n\\n \\u0425\\u043e\\u0440\\u043e\\u0448\\u0438\\u0439, \\u0433\\u043e\\u0434\\u043d\\u044b\\u0439 \\u043c\\u0443\\u043b\\u044c\\u0442\\u0438\\u043a. \\u0416\\u0430\\u043b\\u043a\\u043e, \\u0447\\u0442\\u043e \\u043f\\u0440\\u043e \\u043d\\u0435\\u0433\\u043e \\u043d\\u0435\\u0442 \\u0432 \\u0420\\u0443\\u0412\\u0438\\u043a\\u0438.   \\n\\n ==\",\n          \"== Anthony Browne in Political Correctness == \\n\\n Hello, I'm contacting you because the Anthony Browne bit you added to the article Political Correctness was removed: https://en.wikipedia.org/w/index.php?title=Political_correctness&diff;=690760560&oldid;=690751090 \\n\\n If you would like to have it stay, then come to the talk page and state so: https://en.wikipedia.org/wiki/Talk:Political_correctness#Civitas_think_tank_pamphlet. \\n\\n I for one am for it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "test = dataset['test']\n",
        "df = test.to_pandas()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwAiBxor3Ke8",
        "outputId": "d49c20db-089c-4237-ccc5-b24e4cc9ab18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "non-toxic    45263\n",
              "toxic         5709\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df['label'] = df['label'].map({0: 'non-toxic', 1: 'toxic'})\n",
        "df = df[df['comment_text'].apply(lambda x: len(x) <= 512 and len(x) > 15)]\n",
        "df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9L3IKYR3BHW",
        "outputId": "938733bb-60ff-4d00-d3e3-8fbaf3c08bb7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "non-toxic    175\n",
              "toxic        175\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df_0 = df[df['label'] == 'non-toxic']\n",
        "df_1 = df[df['label'] == 'toxic']\n",
        "\n",
        "# Get 175 samples from each class\n",
        "df_0_test = df_0.sample(n=175, random_state=42)\n",
        "df_1_test = df_1.sample(n=175, random_state=42)\n",
        "\n",
        "df_test = pd.concat([df_0_test, df_1_test], ignore_index=True)\n",
        "df_test.label.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "RMLQO5Wb50_2",
        "outputId": "ab0a8350-4b55-49ad-d268-aa748e39aa14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"::::I googled [cnn sandra fluke] and [new york times sandra fluke] and [npr sandra fluke] and I don't know what all. I found countless editorials and blogs, some of them dealing with Limbaugh's vile comments, others dealing with the way she was treated on Capitol Hill. ← '''' →\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df_test.iloc[1].comment_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlwPZXLv8cgD",
        "outputId": "19c98fd4-78c3-42b2-cb07-9a491af69975"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Phi3ForCausalLM(\n",
              "  (model): Phi3Model(\n",
              "    (embed_tokens): Embedding(32064, 3072, padding_idx=32000)\n",
              "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x Phi3DecoderLayer(\n",
              "        (self_attn): Phi3Attention(\n",
              "          (o_proj): Linear(in_features=3072, out_features=3072, bias=False)\n",
              "          (qkv_proj): Linear(in_features=3072, out_features=9216, bias=False)\n",
              "          (rotary_emb): Phi3RotaryEmbedding()\n",
              "        )\n",
              "        (mlp): Phi3MLP(\n",
              "          (gate_up_proj): Linear(in_features=3072, out_features=16384, bias=False)\n",
              "          (down_proj): Linear(in_features=8192, out_features=3072, bias=False)\n",
              "          (activation_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): Phi3RMSNorm()\n",
              "        (resid_attn_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (post_attention_layernorm): Phi3RMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): Phi3RMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=3072, out_features=32064, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "merged_model.to(device)"
      ]
    },
    {
      "cell_type": "code",
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          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/3.17k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "model_id = \"microsoft/Phi-3-mini-4k-instruct\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "torch.random.manual_seed(0)\n",
        "\n",
        "def run_merged_model(text):\n",
        "  input = format_input(text)\n",
        "  messages = [\n",
        "      {\"role\": \"user\", \"content\": input}\n",
        "  ]\n",
        "\n",
        "  pipe = pipeline(\n",
        "      \"text-generation\",\n",
        "      model=merged_model,\n",
        "      tokenizer=tokenizer,\n",
        "  )\n",
        "\n",
        "  generation_args = {\n",
        "      \"max_new_tokens\": 4,\n",
        "      \"return_full_text\": False,\n",
        "      \"temperature\": 0.01,\n",
        "      \"do_sample\": True,\n",
        "  }\n",
        "\n",
        "  output = pipe(messages, **generation_args)\n",
        "  return output[0]['generated_text'].strip().lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "K1Rw7F__6WAY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17989422-7e69-49c2-ff1f-82c38ccd2578"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are not running the flash-attention implementation, expect numerical differences.\n"
          ]
        }
      ],
      "source": [
        "df_test['phi3'] = df_test.comment_text.apply(lambda x: run_merged_model(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "GR7z9xE3d1uW"
      },
      "outputs": [],
      "source": [
        "df_ = df_test.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXikWKlo-lnP",
        "outputId": "a3693e64-909a-45fd-c65c-d45a4493fa3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['non-toxic' 'toxic']\n"
          ]
        }
      ],
      "source": [
        "print(df_['phi3'].unique())\n",
        "# df_['phi3'] = df_['phi3'].replace(['true'], 'toxic')\n",
        "# df_['phi3'] = df_['phi3'].replace(['non-toxictoxic'], 'non-toxic')\n",
        "# print(df_['phi3'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "                 id                                       comment_text  label  \\\n",
              "0  2ae5c5951544c3d6  \" \\n :::You're welcome. Keep up the good work....      0   \n",
              "1  474ebe391172eab8  ::::I googled [cnn sandra fluke] and [new york...      0   \n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-01a6392d-7218-4fdf-a18e-18ccef261bff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_",
              "summary": "{\n  \"name\": \"df_\",\n  \"rows\": 350,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"38efd877eae6cd80\",\n          \"bb0f7fa2de06fc02\",\n          \"8d7414ab11779922\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"\\\" \\n\\n == Women's income - clarify == \\n\\n This sentence bears investigation: \\\"\\\"Women in New Jersey earn the highest per capita income as stated in a 2002 article in the Newark Star-Ledger.\\\"\\\" Highest over... other women? Men? In general? State to state? I'd appreciate a context.  Thanks.  \\\"\",\n          \"Jeff can go fuck himself - oh no he can't, he's dead hahaha\",\n          \"::Oh, and telling Cyphoidbomb to go die in a fire. It is where that f**ker belongs, in hell.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phi3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Encode string labels to numerical values\n",
        "le = LabelEncoder()\n",
        "df_['label'] = le.fit_transform(df_['label'])\n",
        "df_['phi3'] = le.fit_transform(df_['phi3'])\n",
        "df_.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fRq-jy9W-gJn"
      },
      "outputs": [],
      "source": [
        "df_.to_csv('phi3_toxic_final.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wLpRweB6ACrr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "# Read in and drop the single 2 that ended up in the binary data\n",
        "df_sample = pd.read_csv('/content/phi3toxic.csv').drop(125)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_49kVfCaF_6"
      },
      "source": [
        "Previous Train:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "hVK3sqf7WpOD",
        "outputId": "6f01f326-fefc-4153-97df-00ed5123c9ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.62      0.76       115\n",
            "           1       0.75      0.99      0.86       134\n",
            "\n",
            "    accuracy                           0.82       249\n",
            "   macro avg       0.87      0.80      0.81       249\n",
            "weighted avg       0.86      0.82      0.81       249\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "cm = confusion_matrix(df_sample['toxic'], df_sample['phi3'])\n",
        "\n",
        "# Plot the confusion matrix\n",
        "print(classification_report(df_sample['toxic'], df_sample['phi3']))\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title(r'Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-O-vJsIaJFk"
      },
      "source": [
        "New Numbers:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "wI7wL6zjW4te",
        "outputId": "cab46182-9031-4c49-f157-21d69c4c4cf1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.82      0.86       175\n",
            "           1       0.84      0.91      0.87       175\n",
            "\n",
            "    accuracy                           0.87       350\n",
            "   macro avg       0.87      0.87      0.87       350\n",
            "weighted avg       0.87      0.87      0.87       350\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "cm = confusion_matrix(df_['toxic'], df_['phi3'])\n",
        "\n",
        "# Plot the confusion matrix\n",
        "print(classification_report(df_['toxic'], df_['phi3']))\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title(r'Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "bmOCun8i66XS",
        "outputId": "7a545d6f-77a3-4404-b774-6785af08acb2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"0b41b222ee299028\",\n          \"2132af23d73a538d\",\n          \"0a5bd157ab7e5610\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"\\\" \\n :::::They word it carefully because they need to avoid prosecution, of course, but to extract the key phrases: \\\"\\\"we simply recognise that...  human populations which have undergone.... evolutionary changes.... have developed differences in... behavioural tendencies..\\\"\\\" (I accept that is being very selective, but the sense is there.) That seems to me to be an explicit statement. I'm never happy about including large chunks of text in footnotes - it always seems rather untidy, and I would personally prefer to simply direct readers to the original (it's on p17 by the way), but let's try it. I don't think the 2007 manifeston does override the 2005 for these purposes; the latter was for a national General Election. \\n :::::I'm also repositioning the 'denied by the BNP'in the infobox to appear AFTER the references. This is for clarity, as none of the references are there in connection with any denial. It is still my view that this should not be in the infobox anyway, since it is covered in the article. \\n :::::I will also remove WhiteTiger86's addition. This is unnecessary, being too long and the issue of membership is already mentioned in the introduction anyway. Note that this addition also removed the sentence we have been discussing!   \\\"\",\n          \"\\\" \\n\\n :Thanks for the advice. And good luck in trying to get an administrator to do anything sensible or appropriate about Eric's appalling behaviour. There is so much bullshit and hypocrisy going on with some of the administrators regarding Eric that it has become absurd and farcical. You may instead, like myself, find yourself receiving a rebuke from an administrator instead of Eric. One administrator has been ignoring Eric's repeated incivility to me and other editors while, in a bullying manner, blaming me and demanding that I withdraw a comment in response to his incivility to me. There is, of course, no way that I am going to withdraw my comment in such circumstances. That really would be hypocritical on my part and rewarding bullies such as Eric. The administrator simply doesn't \\\"\\\"get it\\\"\\\" and is continuing to make disingenous comments about me whenever s/he has the opportunityy. Eric, meanwhile, seeks pity from other editors over my comment and has been suggesting, falsely and without any evidence whatsoever, of pretending to be a priest and also of being a sockpuppet of some former administrator. The whole situation is very bizarre. Wasn't there once a wise person who said something about seeing a speck in someone else's eye whilst ignoring the beam in your own? All the best.   \\\"\",\n          \"===answer to last comment=== \\n look at hydrolisys \\n CO2 + H20  ->  H+ + HCO3- \\n ()\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phi3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-71151dd1-4e45-4cf9-a5e5-784d3d97ba37\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>label</th>\n",
              "      <th>phi3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ac1ecb54774c6b2c</td>\n",
              "      <td>== External Links == \\n Hello everyone, how ar...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>69f37ba4ab7d93aa</td>\n",
              "      <td>\" \\n : Let me phrase this properly: this artic...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f05dd87a0c3e68a3</td>\n",
              "      <td>Because archives are generally much bigger, in...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6184c8d272f0321b</td>\n",
              "      <td>]] for use by California schools}}</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d83955b945f03172</td>\n",
              "      <td>I will add more later</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>8467292ea30cbbff</td>\n",
              "      <td>grab your left ear with your right hand and yo...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>f861fc7e6a69fff6</td>\n",
              "      <td>== Please help! == \\n Some idiot decided to mo...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>0bdd3e5962caf581</td>\n",
              "      <td>::Arguing with a conpiracy theorist is like tr...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>08e7dd4506fa68ac</td>\n",
              "      <td>You are a big hairy ape like mamith.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>912c06ca4d96a46e</td>\n",
              "      <td>, 27 January 2010 (UTC) \\n ::::::H C P what I'...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71151dd1-4e45-4cf9-a5e5-784d3d97ba37')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-71151dd1-4e45-4cf9-a5e5-784d3d97ba37 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-71151dd1-4e45-4cf9-a5e5-784d3d97ba37');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97f16b00-331d-4d51-98e4-d29457ea6b21')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-97f16b00-331d-4d51-98e4-d29457ea6b21 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b542c8b9-643e-42c5-b731-3e0657006965\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b542c8b9-643e-42c5-b731-3e0657006965 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   id                                       comment_text  \\\n",
              "0    ac1ecb54774c6b2c  == External Links == \\n Hello everyone, how ar...   \n",
              "1    69f37ba4ab7d93aa  \" \\n : Let me phrase this properly: this artic...   \n",
              "2    f05dd87a0c3e68a3  Because archives are generally much bigger, in...   \n",
              "3    6184c8d272f0321b                 ]] for use by California schools}}   \n",
              "4    d83955b945f03172                              I will add more later   \n",
              "..                ...                                                ...   \n",
              "195  8467292ea30cbbff  grab your left ear with your right hand and yo...   \n",
              "196  f861fc7e6a69fff6  == Please help! == \\n Some idiot decided to mo...   \n",
              "197  0bdd3e5962caf581  ::Arguing with a conpiracy theorist is like tr...   \n",
              "198  08e7dd4506fa68ac               You are a big hairy ape like mamith.   \n",
              "199  912c06ca4d96a46e  , 27 January 2010 (UTC) \\n ::::::H C P what I'...   \n",
              "\n",
              "     label  phi3  \n",
              "0        0     0  \n",
              "1        0     1  \n",
              "2        0     1  \n",
              "3        0     1  \n",
              "4        0     0  \n",
              "..     ...   ...  \n",
              "195      1     1  \n",
              "196      1     1  \n",
              "197      1     1  \n",
              "198      1     1  \n",
              "199      1     1  \n",
              "\n",
              "[200 rows x 4 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "3nrx7NT64BeR",
        "outputId": "835edad0-588f-4384-97b5-c6e8294ebb80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.83      0.85       175\n",
            "           1       0.84      0.88      0.86       175\n",
            "\n",
            "    accuracy                           0.86       350\n",
            "   macro avg       0.86      0.86      0.86       350\n",
            "weighted avg       0.86      0.86      0.86       350\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "cm = confusion_matrix(df_['label'], df_['phi3'])\n",
        "\n",
        "# Plot the confusion matrix\n",
        "print(classification_report(df_['label'], df_['phi3']))\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title(r'Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "6b7d1f9164fc4ea9a10f0cb1eeea9c9d",
            "54f8f2e3151745ed906b5b3e14f82b1f",
            "cd650fbac1494318938531cbf4df70c1",
            "16d1b03bbb5842b1acbe67efb85a3734",
            "d6ae31403ab542168a6a3cc158ae3178",
            "451c8322962c40dd9ab9d7e8494d8185",
            "fe95f18873fa4e2ebc39e78f236f6455",
            "f8527fd1663e4889b3b093dc94d0a473",
            "e7a3c91b78ad48ac95ece61f7f0e9d56",
            "17ee44b6100441d69605d89195888ce8",
            "c96a305866d847bba13540f4871c9104",
            "5ad0780b1e3240c5a6ef0d89607ca133",
            "a659945a144f48c18c742b2ac5511766",
            "e67cf394310a4956bd053021d5baf2a7",
            "6e67a02041f54c44ba5f9cbe66d5e877",
            "ac4b80a4dd964b80b3b9b62e3618b14a",
            "34068818369447258c79c45bcbf4fb1c",
            "0b9328b77bf948fa9d284c9357347e6f",
            "ec6f8878d95e4f1ba2593b2972c14984",
            "205ad0473302498e87fe1244d7015929",
            "2addcbde1ed941c0b4bc24e64c303617",
            "fba85f14baad4951b7329f7e55e2bb07",
            "f86a03739b6f4b9b849ea668dfd36dd3",
            "87e91c547a1c41c29c53329d3e9a4499",
            "dbe3b2fbfcac4e6483f213e70c694eaf",
            "625fcb7dba0045728c45878ecadc53f2",
            "d529b88aaeac42c49d0696392aeae5af",
            "4b5c429330a44e1e8fd2465e544b8ab7",
            "009c1a78e13644d69f100e52324fe886"
          ]
        },
        "id": "6YG15aGSktP2",
        "outputId": "87c1124a-83e1-4ff8-a4f0-edea13a12e0f"
      },
      "execution_count": 20,
      "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": "6b7d1f9164fc4ea9a10f0cb1eeea9c9d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JPBXEfJAfDFc"
      },
      "outputs": [],
      "source": [
        "merged_model.push_to_hub('Jlonge4/phi3-toxicity-judge-merge')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "61a1e969ca044f9d906edf586e615e12",
            "1cfa594469324d5f987b5d45a32fe5de",
            "7f90354bdc024ee3afcce31750f7baad",
            "2f5e688149aa4d519aeeb1ee3fc3c579",
            "87e3a6e5e50f463f939087324227c5c1",
            "9adacbd921914fb8960b533a811730fa",
            "42fe7b4e929a4ec397d7b3a7ede6377f",
            "f10bbacfd0e7471c83f242263eac24da",
            "1fbc362491384a3b84a5f872359d574b",
            "db06edfb3ddd4c50a50640c7a672e707",
            "daddc50fc8f44cd79ac850fca14d2795"
          ]
        },
        "id": "IjcEyPxqTX6t",
        "outputId": "6541e850-f585-45dd-dfb4-d08eaf4a6a02"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/1.26k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61a1e969ca044f9d906edf586e615e12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Jlonge4/phi3-toxicity-judge/commit/3c8c1eb2ee070bae4f00cec54b6a29212056b331', commit_message='Upload model', commit_description='', oid='3c8c1eb2ee070bae4f00cec54b6a29212056b331', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "model_peft.push_to_hub('Jlonge4/phi3-toxicity-judge')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IlCG4v0g4CEU"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "machine_shape": "hm",
      "provenance": [],
      "mount_file_id": "1sht3SYkRF74y3_RpmQl0Swrqk477cZ6x",
      "authorship_tag": "ABX9TyPL5yljRjXNqntPJD/0DBnm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "513b5f35ad9d41aeb92439b065af9585": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
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