Multiclass_Classification_UCC_Dataset_TJK.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "8tKJjKWX923F"
      },
      "source": [
        "# **Multiclass-Classification**\n",
        "# Using PyTorch and [Huggingface](https://huggingface.co/)\n",
        "# On [UCC Unhealthy Comments Dataset](https://github.com/conversationai/unhealthy-conversations)\n",
        "\n",
        "## By: Tahsin Jahin Khalid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4zRgLHh-hgO"
      },
      "source": [
        "### Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "IwN-i_yu914P"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install transformers\n",
        "!pip install pytorch-lightning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-Q7d42mAGUN",
        "outputId": "3287e395-4313-4f00-e1a6-f1a0a9b47e00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Oct  7 14:57:12 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GykJggQZ-kjb"
      },
      "source": [
        "### Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "tOMVR7jm91E_"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "# data processing\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "# for visualisation\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset\n",
        "import torch\n",
        "from transformers import AutoTokenizer\n",
        "import pytorch_lightning as pl\n",
        "from torch.utils.data import DataLoader\n",
        "# for model selection and training\n",
        "from transformers import AutoModel, get_cosine_schedule_with_warmup\n",
        "from torch import optim\n",
        "import torch.nn as nn\n",
        "import math\n",
        "from torchmetrics.functional.classification import auroc\n",
        "import torch.nn.functional as F\n",
        "# for evaluation\n",
        "from sklearn import metrics\n",
        "# for drive access\n",
        "# from google.colab import drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "tBC-juPTW6Qp"
      },
      "outputs": [],
      "source": [
        "train_csv = \"https://raw.githubusercontent.com/conversationai/unhealthy-conversations/main/corpus/train.csv\"\n",
        "valid_csv = \"https://raw.githubusercontent.com/conversationai/unhealthy-conversations/main/corpus/val.csv\"\n",
        "test_csv = \"https://raw.githubusercontent.com/conversationai/unhealthy-conversations/main/corpus/test.csv\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bVkdha3-sPz"
      },
      "source": [
        "### Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "SMc-fUaZ-uve"
      },
      "outputs": [],
      "source": [
        "train_data = pd.read_csv(train_csv)"
      ]
    },
    {
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      "execution_count": 6,
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      "outputs": [
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              "        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-fad864bd-7ef1-4782-b24c-8b717e42210f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "7YWp5tWDXxjo",
        "outputId": "d887375a-a96f-49c0-afe3-5d049d14d744"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# we make a new variable called \"unhealthy\"\n",
        "# which is 0 if healthy and 1 if otherwise\n",
        "train_data[\"unhealthy\"] = np.where(train_data[\"healthy\"] == 1, 0, 1)\n",
        "\n",
        "attributes = [\"antagonize\", \"condescending\",\n",
        "              \"dismissive\", \"generalisation\",\n",
        "              \"generalisation_unfair\", \"hostile\",\n",
        "              \"sarcastic\", \"unhealthy\"]\n",
        "\n",
        "train_data[attributes].sum().plot.bar()\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.xlabel(\"Category\")\n",
        "plt.title(\"Frequency of Different Classes/Categories\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBIvtWIP-vTS"
      },
      "source": [
        "### Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "C7oBpdVx-yhX"
      },
      "outputs": [],
      "source": [
        "# make a custom class for the dataset\n",
        "class UCC_Dataset(Dataset):\n",
        "\n",
        "  def __init__(self, data_path, tokenizer, attributes,\n",
        "               max_token_len: int = 128, sample = 5000):\n",
        "    self.data_path = data_path\n",
        "    self.tokenizer = tokenizer\n",
        "    self.attributes = attributes\n",
        "    self.max_token_len = max_token_len\n",
        "    self.sample = sample\n",
        "    self._prepare_data()\n",
        "\n",
        "\n",
        "  def _prepare_data(self):\n",
        "    data = pd.read_csv(self.data_path)\n",
        "    data['unhealthy'] = np.where(data['healthy'] == 1, 0, 1)\n",
        "    if self.sample is not None:\n",
        "      unhealthy = data.loc[data[attributes].sum(axis=1) > 0]\n",
        "      clean = data.loc[data[attributes].sum(axis=1) == 0]\n",
        "      self.data = pd.concat(\n",
        "          [unhealthy, clean.sample(self.sample, random_state=7)])\n",
        "    else:\n",
        "      self.data = data\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    item = self.data.iloc[index]\n",
        "    comment = str(item.comment)\n",
        "    attributes = torch.FloatTensor(item[self.attributes])\n",
        "    tokens = self.tokenizer.encode_plus(comment,\n",
        "                                        add_special_tokens=True,\n",
        "                                        return_tensors='pt',\n",
        "                                        truncation=True,\n",
        "                                        padding='max_length',\n",
        "                                        max_length=self.max_token_len,\n",
        "                                        return_attention_mask = True)\n",
        "    return {'input_ids': tokens.input_ids.flatten(),\n",
        "            'attention_mask': tokens.attention_mask.flatten(),\n",
        "            'labels': attributes}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcwJSpCj-zMR"
      },
      "source": [
        "### Data Module"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "eQsSJ83t-28Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "d26fa0460f3d4a218bcb538703b88db3",
            "2817abc89cb843b6aa36fb4ad9e9ad1b",
            "7186868757be44249a6bec86077f357c",
            "74ba7d37bd0d47cf9b4f4cb31c07577b",
            "2b447d9c0d10471e8e327631bca5967b",
            "9439e0059c57432a8141666b789ed6a1",
            "83d5cbd47ef74275b428d5c8b0ae1504",
            "5ada9adec64641d9b76af54549bd5b88",
            "5637a88c705049d49bf8d5d844ef9a66",
            "c6198ce82b2f4588b09b6c52fc38f15e",
            "1ae9ea07a89a45e4a5ed85df4ec2d906",
            "0f2a91f244d7484aaa54d6da603580d0",
            "f40a5af0773146d0ae1d66060bd62695",
            "d25506f41f43468cbe9bd4522c2f99f7",
            "c971b99cc9fa4d439e17b28a8d150519",
            "d7c7bb26541742888faf8923890f57ee",
            "705153ffad9c469f9a000c3ff4037fa6",
            "be155b74139f4eb4a6cc0ee0880d5d59",
            "a5ca6e768ad14c71ba5f6f2e7b4a78ba",
            "835e68edc4d447dd904b5def7878807c",
            "cf9e31d3c5674856ab6f95deeac0ba0e",
            "03d548a1f79b41189a299229a7184896",
            "8bfcb252ff934849a491a391debb5fb6",
            "1ddf3fe19e9d4345b30a7faa56feab7a",
            "d88a017c4f2a4f668cae443b73be7078",
            "b90ccbaf7e3c4565a5c8013d59237729",
            "d5a0ae0f29a440e19162333981c5be7e",
            "6392a75b17f748fd84414303103f62d1",
            "6e33271f623b4a00aa34802ed3dc4638",
            "8378af9c55d742d68fcf5915f00ae4b4",
            "aa3f6d464152457d935e5453d10428d0",
            "65b9aea0313a435ebbb576a54ef7559b",
            "0ee569a82e984d4e8f97f94e28836fbf",
            "069f3c31ba164ca7810635ee7df75622",
            "996f368e960346f1a7aefa7b78fc5e7f",
            "1ab1ef874e2f48388defa2e5fa463ffa",
            "16afb5f1b89544019bc9fac1b95e15d5",
            "35b1ff79490548a2b1dac6889b2ea50d",
            "cee1f5dc81224c88a0f72f755a04476d",
            "b699624ecde449b389db15e6dacef7f2",
            "71305795152e43ff8924ebb8875734be",
            "56360640f91048aca2e07f3472d34fc5",
            "e38dce8ae51f445da3e2ec941f2a955a",
            "7543ea5ed2e44e69be7f97dfdc73f50a"
          ]
        },
        "outputId": "9810c155-ae07-4142-d0c7-71d2842ee920"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d26fa0460f3d4a218bcb538703b88db3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f2a91f244d7484aaa54d6da603580d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bfcb252ff934849a491a391debb5fb6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "069f3c31ba164ca7810635ee7df75622"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model_name = 'roberta-base'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "ucc_ds = UCC_Dataset(train_csv, tokenizer, attributes=attributes)\n",
        "ucc_ds_val = UCC_Dataset(valid_csv, tokenizer, attributes=attributes, sample=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EX8ZznrZyLq",
        "outputId": "2aa2f5c9-21bd-4d7c-e711-b369dd4b417e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Labels:         torch.Size([8]),\n",
            "Input_IDs:      torch.Size([128]),\n",
            "Attention Mask: torch.Size([128])\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(f\"\"\"\n",
        "Labels:         {ucc_ds.__getitem__(0)['labels'].shape},\n",
        "Input_IDs:      {ucc_ds.__getitem__(0)['input_ids'].shape},\n",
        "Attention Mask: {ucc_ds.__getitem__(0)['attention_mask'].shape}\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbqwK9r8aJZj",
        "outputId": "40624ce7-a573-48c4-fb69-db3211b88324"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of Rows in Training Set:   9960\n",
            "Number of Rows in Validation Set: 4427\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(f\"\"\"\n",
        "Number of Rows in Training Set:   {len(ucc_ds)}\n",
        "Number of Rows in Validation Set: {len(ucc_ds_val)}\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "DHUSZY3jaila"
      },
      "outputs": [],
      "source": [
        "class UCC_Data_Module(pl.LightningDataModule):\n",
        "\n",
        "  def __init__(self, train_path, val_path,\n",
        "               attributes, batch_size: int = 16,\n",
        "               max_token_length: int = 128,\n",
        "               model_name='roberta-base'):\n",
        "    super().__init__()\n",
        "    self.train_path = train_path\n",
        "    self.val_path = val_path\n",
        "    self.attributes = attributes\n",
        "    self.batch_size = batch_size\n",
        "    self.max_token_length = max_token_length\n",
        "    self.model_name = model_name\n",
        "    self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "  def setup(self, stage = None):\n",
        "    if stage in (None, \"fit\"):\n",
        "      self.train_dataset = UCC_Dataset(self.train_path,\n",
        "                                       attributes=self.attributes,\n",
        "                                       tokenizer=self.tokenizer)\n",
        "      self.val_dataset = UCC_Dataset(self.val_path,\n",
        "                                     attributes=self.attributes,\n",
        "                                     tokenizer=self.tokenizer,\n",
        "                                     sample=None)\n",
        "    if stage == 'predict':\n",
        "      self.val_dataset = UCC_Dataset(self.val_path,\n",
        "                                     attributes=self.attributes,\n",
        "                                     tokenizer=self.tokenizer,\n",
        "                                     sample=None)\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    return DataLoader(self.train_dataset,\n",
        "                      batch_size = self.batch_size,\n",
        "                      num_workers=4,\n",
        "                      shuffle=True)\n",
        "\n",
        "  def val_dataloader(self):\n",
        "    return DataLoader(self.val_dataset,\n",
        "                      batch_size = self.batch_size,\n",
        "                      num_workers=4,\n",
        "                      shuffle=False)\n",
        "\n",
        "  def predict_dataloader(self):\n",
        "    return DataLoader(self.val_dataset,\n",
        "                      batch_size = self.batch_size,\n",
        "                      num_workers=4,\n",
        "                      shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "1LdWOGtYakDw"
      },
      "outputs": [],
      "source": [
        "ucc_data_module = UCC_Data_Module(train_csv,\n",
        "                                  valid_csv,\n",
        "                                  attributes=attributes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "_Kp0MMpxa_a2"
      },
      "outputs": [],
      "source": [
        "ucc_data_module.setup()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ry-bQ_F7bCEr",
        "outputId": "0ea3594d-1e55-4514-9e5c-69210161e861"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x78feb6bb6a70>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "ucc_data_module.train_dataloader()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7AplZQdxbFPU",
        "outputId": "1de676d6-3231-426a-b81c-ef556c72f2cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "623"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "len(ucc_data_module.train_dataloader())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN5RRM62-3Yx"
      },
      "source": [
        "### Model Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "c_gpAC4y-5ej"
      },
      "outputs": [],
      "source": [
        "class UCC_Comment_Classifier(pl.LightningModule):\n",
        "\n",
        "  def __init__(self, config: dict):\n",
        "    super().__init__()\n",
        "    self.config = config\n",
        "    self.pretrained_model = AutoModel.from_pretrained(\n",
        "        config['model_name'],\n",
        "        return_dict = True)\n",
        "    self.hidden = torch.nn.Linear(\n",
        "        self.pretrained_model.config.hidden_size,\n",
        "        self.pretrained_model.config.hidden_size)\n",
        "    self.classifier = torch.nn.Linear(\n",
        "        self.pretrained_model.config.hidden_size,\n",
        "        self.config['n_labels'])\n",
        "    torch.nn.init.xavier_uniform_(self.classifier.weight)\n",
        "    self.loss_func = nn.BCEWithLogitsLoss(reduction='mean')\n",
        "    self.dropout = nn.Dropout()\n",
        "\n",
        "  def forward(self, input_ids, attention_mask, labels=None):\n",
        "    # roberta layer\n",
        "    output = self.pretrained_model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask)\n",
        "    pooled_output = torch.mean(\n",
        "        output.last_hidden_state, 1)\n",
        "    # final logits\n",
        "    pooled_output = self.dropout(pooled_output)\n",
        "    pooled_output = self.hidden(pooled_output)\n",
        "    pooled_output = F.relu(pooled_output)\n",
        "    pooled_output = self.dropout(pooled_output)\n",
        "    logits = self.classifier(pooled_output)\n",
        "    # calculate loss\n",
        "    loss = 0\n",
        "    if labels is not None:\n",
        "      loss = self.loss_func(\n",
        "          logits.view(-1, self.config['n_labels']),\n",
        "          labels.view(-1, self.config['n_labels']))\n",
        "    return loss, logits\n",
        "\n",
        "  def training_step(self, batch, batch_index):\n",
        "    loss, outputs = self(**batch)\n",
        "    self.log(\"train loss \", loss,\n",
        "             prog_bar = True, logger=True)\n",
        "    return {\"loss\":loss,\n",
        "            \"predictions\":outputs,\n",
        "            \"labels\": batch[\"labels\"]}\n",
        "\n",
        "  def validation_step(self, batch, batch_index):\n",
        "    loss, outputs = self(**batch)\n",
        "    self.log(\"validation loss \", loss,\n",
        "             prog_bar = True, logger=True)\n",
        "    return {\"val_loss\": loss, \"predictions\":outputs,\n",
        "            \"labels\": batch[\"labels\"]}\n",
        "\n",
        "  def predict_step(self, batch, batch_index):\n",
        "    loss, outputs = self(**batch)\n",
        "    return outputs\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "    optimizer = optim.AdamW(self.parameters(),\n",
        "                            lr=self.config['lr'],\n",
        "                            weight_decay=self.config['weight_decay'])\n",
        "    total_steps = self.config['train_size']/self.config['batch_size']\n",
        "    warmup_steps = math.floor(total_steps * self.config['warmup'])\n",
        "    warmup_steps = math.floor(total_steps * self.config['warmup'])\n",
        "    scheduler = get_cosine_schedule_with_warmup(optimizer,\n",
        "                                                warmup_steps,\n",
        "                                                total_steps)\n",
        "    return [optimizer],[scheduler]\n",
        "\n",
        "  # def validation_epoch_end(self, outputs):\n",
        "  #   losses = []\n",
        "  #   for output in outputs:\n",
        "  #     loss = output['val_loss'].detach().cpu()\n",
        "  #     losses.append(loss)\n",
        "  #   avg_loss = torch.mean(torch.stack(losses))\n",
        "  #   self.log(\"avg_val_loss\", avg_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "vR-UgGEib0EL"
      },
      "outputs": [],
      "source": [
        "config = {\n",
        "    'model_name': 'distilroberta-base',\n",
        "    'n_labels': len(attributes),\n",
        "    'batch_size': 128,\n",
        "    'lr': 1.5e-6,\n",
        "    'warmup': 0.2,\n",
        "    'train_size': len(ucc_data_module.train_dataloader()),\n",
        "    'weight_decay': 0.001,\n",
        "    # 'n_epochs': 100\n",
        "    # reduce to 20 epoch since it took like\n",
        "    # 3.5 hours or so before colab timed out\n",
        "    'n_epochs': 20\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "H9wbrdsOb1PK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "outputId": "e00830d8-a64c-436d-9596-00ef593167a3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/480 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6398bbf26c1044f1a60fd0961570fd95"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/331M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b83f26e3f3134619a32625813f785a19"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = UCC_Comment_Classifier(config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abW2JHYvb4z6",
        "outputId": "fef9eef2-0c97-40b2-bca7-dd4a135c690c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([8]) torch.Size([1, 8]) tensor([[ 0.1903,  0.0589,  0.3509, -0.2044,  0.0438, -0.3039,  0.2462, -0.3153]],\n",
            "       grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ],
      "source": [
        "idx=0\n",
        "input_ids = ucc_ds.__getitem__(idx)['input_ids']\n",
        "attention_mask = ucc_ds.__getitem__(idx)['attention_mask']\n",
        "labels = ucc_ds.__getitem__(idx)['labels']\n",
        "model.cpu()\n",
        "loss, output = model(\n",
        "    input_ids.unsqueeze(dim=0),\n",
        "    attention_mask.unsqueeze(dim=0),\n",
        "    labels.unsqueeze(dim=0))\n",
        "print(labels.shape, output.shape, output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tntw_7LK-5-L"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451,
          "referenced_widgets": [
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        "outputId": "dd6a8bbf-8dd8-4218-eb34-92da98d2a77b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name             | Type              | Params\n",
            "-------------------------------------------------------\n",
            "0 | pretrained_model | RobertaModel      | 82.1 M\n",
            "1 | hidden           | Linear            | 590 K \n",
            "2 | classifier       | Linear            | 6.2 K \n",
            "3 | loss_func        | BCEWithLogitsLoss | 0     \n",
            "4 | dropout          | Dropout           | 0     \n",
            "-------------------------------------------------------\n",
            "82.7 M    Trainable params\n",
            "0         Non-trainable params\n",
            "82.7 M    Total params\n",
            "330.861   Total estimated model params size (MB)\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e57cf63d25184a42891206689e54a078"
            }
          },
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
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            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=20` reached.\n"
          ]
        }
      ],
      "source": [
        "# datamodule\n",
        "ucc_data_module = UCC_Data_Module(train_csv,\n",
        "                                  valid_csv,\n",
        "                                  attributes=attributes,\n",
        "                                  batch_size=config['batch_size'])\n",
        "ucc_data_module.setup()\n",
        "\n",
        "# model\n",
        "model = UCC_Comment_Classifier(config)\n",
        "\n",
        "# trainer and fit\n",
        "trainer = pl.Trainer(\n",
        "    max_epochs=config['n_epochs'],\n",
        "    devices=1,\n",
        "    accelerator=\"gpu\",\n",
        "    num_sanity_val_steps=50)\n",
        "trainer.fit(model, ucc_data_module)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4snsjFR-9ib"
      },
      "source": [
        "### Model Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "eXwDBniK--wT"
      },
      "outputs": [],
      "source": [
        "# method to convert list of comments into predictions for each comment\n",
        "def classify_raw_comments(model, dm):\n",
        "  predictions = trainer.predict(\n",
        "      model,\n",
        "      datamodule=dm)\n",
        "  flattened_predictions = np.stack(\n",
        "      [torch.sigmoid(torch.Tensor(p))\n",
        "       for batch in predictions\n",
        "       for p in batch])\n",
        "  return flattened_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "62vXN8p8d131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
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            "c1c79f0ff5b241248bb744ef305f637d",
            "01e789e1a44c4a618303cc63ff9f76c2",
            "c2dde17f28b94ebebb7b093509472256",
            "94a51174a6144add99a4dadca2b2aad1"
          ]
        },
        "outputId": "6c84e6b5-fe50-440a-c3ba-0528ad1f8407"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5abbc10494ec49fcbe20c640714a4ce4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictions = classify_raw_comments(model, ucc_data_module)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "xdOFyMNkdtQ5"
      },
      "outputs": [],
      "source": [
        "val_data = pd.read_csv(valid_csv)\n",
        "val_data['unhealthy'] = np.where(val_data['healthy'] == 1, 0, 1)\n",
        "true_labels = np.array(val_data[attributes])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-2DBvWNeLlh"
      },
      "source": [
        "### Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "cRY8ofZVd70C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "429eb9ce-0095-4ec7-b349-49c5d3febaa1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 8))\n",
        "for i, attribute in enumerate(attributes):\n",
        "  fpr, tpr, _ = metrics.roc_curve(\n",
        "      true_labels[:,i].astype(int), predictions[:, i])\n",
        "  auc = metrics.roc_auc_score(\n",
        "      true_labels[:,i].astype(int), predictions[:, i])\n",
        "  plt.plot(fpr, tpr, label='%s %g' % (attribute, auc))\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('RoBERTa Trained on UCC Datatset - AUC ROC')\n",
        "plt.show()"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
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      "display_name": "Python 3",
      "name": "python3"
    },
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      "name": "python"
    },
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