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        "id": "cYJxJ6jb3xTJ",
        "outputId": "d01a4787-6aea-4026-c6c1-bcbac8ed48ec"
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      "source": [
        "# For use with Google Colab only to install transformers library\n",
        "# !pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RqKR2tUd8W2G"
      },
      "outputs": [],
      "source": [
        "# importing necessary libraries\n",
        "import torch \n",
        "from torch.utils.data import Dataset\n",
        "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n",
        "from transformers import AdamW\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "LdZaCOwM8nT4",
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              "                 id                                       comment_text  toxic  \\\n",
              "0  0000997932d777bf  Explanation\\r\\nWhy the edits made under my use...      0   \n",
              "1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0   \n",
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              "\n",
              "   severe_toxic  obscene  threat  insult  identity_hate  \n",
              "0             0        0       0       0              0  \n",
              "1             0        0       0       0              0  \n",
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            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# define the model name\n",
        "model_name = \"distilbert-base-uncased\"\n",
        "\n",
        "# reading in the data and splitting into features and labels\n",
        "df = pd.read_csv(\"train.csv\")\n",
        "train_texts = df[\"comment_text\"].values\n",
        "train_labels = df[df.columns[2:]].values\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2oqpgybJ9PQZ"
      },
      "outputs": [],
      "source": [
        "# splitting up the data into training and validation sets\n",
        "train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# getting the tokenizer\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained(model_name, max_length=1024)\n",
        "\n",
        "# creating a custom dataset for training\n",
        "class ToxicDataset(Dataset):\n",
        "  def __init__(self, texts, labels):\n",
        "    self.texts = texts\n",
        "    self.labels = labels\n",
        "  \n",
        "  def __getitem__(self, index):\n",
        "    encodings = tokenizer(self.texts[index], truncation=True, padding='max_length')\n",
        "    item = {key: torch.tensor(val) for key, val in encodings.items()}\n",
        "    item['labels'] = torch.tensor(self.labels[index], dtype=torch.float32)\n",
        "    del encodings #\n",
        "    return item\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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      "outputs": [],
      "source": [
        "# creating a dataloader for training and custom dataset\n",
        "# device is set in order to use GPU for training, adjust code accordingly if GPU is not available\n",
        "device = torch.device('cuda')\n",
        "\n",
        "# download model and prepare it for training\n",
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=6, problem_type=\"multi_label_classification\")\n",
        "model.to(device)\n",
        "model.train()\n",
        "\n",
        "# defining the dataset and dataloader\n",
        "train_dataset = ToxicDataset(train_texts, train_labels)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PASPUG053GHV",
        "outputId": "d07607e9-ac33-451f-c95d-c3b068d68070"
      },
      "outputs": [],
      "source": [
        "# getting the optimizer and setting the number of epochs\n",
        "optim = AdamW(model.parameters(), lr=5e-5)\n",
        "num_train_epochs = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "i3X2d_SaRXsm"
      },
      "outputs": [],
      "source": [
        "# training the model\n",
        "for epoch in range(num_train_epochs):\n",
        "  for batch in train_dataloader:\n",
        "    optim.zero_grad()\n",
        "    input_ids = batch['input_ids'].to(device)\n",
        "    attention_mask = batch['attention_mask'].to(device)\n",
        "    labels = batch['labels'].to(device)\n",
        "\n",
        "    outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "\n",
        "    loss = outputs[0]\n",
        "    loss.backward()\n",
        "    optim.step()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ySTMOMGj-giU"
      },
      "outputs": [],
      "source": [
        "# setting the model to evaluation mode\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-O3TOe49MXar",
        "outputId": "ed8d6f79-9d6a-4353-a05d-17d27d782865"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[99.9134, 47.9581, 99.0946,  0.6099, 91.4176,  1.0425]],\n",
            "       device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "# testing a predication on a single example from the training set\n",
        "X_train = [\"COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK\"]\n",
        "batch = tokenizer(X_train, truncation=True, padding='max_length', return_tensors=\"pt\").to(device)\n",
        "\n",
        "with torch.no_grad():\n",
        "  outputs = model(**batch)\n",
        "  predictions = torch.sigmoid(outputs.logits)*100\n",
        "  print(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "XxIRAUvwM9uS"
      },
      "outputs": [],
      "source": [
        "# saving the model and its tokenizer\n",
        "model.save_pretrained(\"pretrained_model\")\n",
        "tokenizer.save_pretrained(\"model_tokenizer\")"
      ]
    }
  ],
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