bert_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "So2KDTNqLXdY"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "SrOoFN43Lfjr"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "import numpy as np\n",
    "from transformers import BertTokenizer, BertModel\n",
    "from torch import nn\n",
    "from torch.optim import Adam\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
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    "id": "sbLRCgVGLksA",
    "outputId": "7bfdd4c6-f63d-41e3-e1c1-7587c0a6a95a"
   },
   "outputs": [
    {
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     "text": [
      "Saving training_nlp.csv to training_nlp.csv\n"
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    "from google.colab import files\n",
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       "          const element = document.querySelector('#df-d2be7d35-2fac-4962-8a96-e174d0d5c223');\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",
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       "  "
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      "text/plain": [
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       "\n",
       "                                  Text Transcription  \n",
       "0                                      Milk Milk.zip  \n",
       "1  ROSES ARE RED, VIOLETS ARE BLUE IF YOU DON'T S...  \n",
       "2  BREAKING NEWS: Russia releases photo of DONALD...  \n",
       "3                       MAN SEEKING WOMAN Ignad 18 O  \n",
       "4  Me explaining the deep lore of. J.R.R. Tolkein...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('training_nlp.csv', sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    },
    "id": "mP12zICdVAwH",
    "outputId": "950b0f23-c6c1-4bdb-cc1c-52eb57822c0b"
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      "text/plain": [
       "      file_name  misogynous  shaming  stereotype  objectification  violence  \\\n",
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       "1        10.jpg           1        0           0                0         1   \n",
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       "\n",
       "                                     Text Transcription  \\\n",
       "0                                         Milk Milk.zip   \n",
       "1     ROSES ARE RED, VIOLETS ARE BLUE IF YOU DON'T S...   \n",
       "2     BREAKING NEWS: Russia releases photo of DONALD...   \n",
       "3                          MAN SEEKING WOMAN Ignad 18 O   \n",
       "4     Me explaining the deep lore of. J.R.R. Tolkein...   \n",
       "...                                                 ...   \n",
       "9995      WAITING FOR THE END OF THE COVID  imgflip.com   \n",
       "9996                SMART WOMEN ARE AROUND  imgflip.com   \n",
       "9997      GOOD GIRLS ARE BEHIND THE CORNER  imgflip.com   \n",
       "9998                   COOKING FOR MY WIFE  imgflip.com   \n",
       "9999  LISTEN TOMORROW WILL BE MONDAY imgflip.com FRO...   \n",
       "\n",
       "                                      TextTranscription  \n",
       "0                                         Milk Milk.zip  \n",
       "1     ROSES ARE RED, VIOLETS ARE BLUE IF YOU DON'T S...  \n",
       "2     BREAKING NEWS: Russia releases photo of DONALD...  \n",
       "3                          MAN SEEKING WOMAN Ignad 18 O  \n",
       "4     Me explaining the deep lore of. J.R.R. Tolkein...  \n",
       "...                                                 ...  \n",
       "9995      WAITING FOR THE END OF THE COVID  imgflip.com  \n",
       "9996                SMART WOMEN ARE AROUND  imgflip.com  \n",
       "9997      GOOD GIRLS ARE BEHIND THE CORNER  imgflip.com  \n",
       "9998                   COOKING FOR MY WIFE  imgflip.com  \n",
       "9999  LISTEN TOMORROW WILL BE MONDAY imgflip.com FRO...  \n",
       "\n",
       "[10000 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df['TextTranscription'] = df['Text Transcription']\n",
    "df"
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  },
  {
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       "\n",
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       "          document.querySelector('#df-1f73859f-181e-44ff-bb12-ee3a70406f34 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-1f73859f-181e-44ff-bb12-ee3a70406f34');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      misogynous                                  TextTranscription\n",
       "0              0                                      Milk Milk.zip\n",
       "1              1  ROSES ARE RED, VIOLETS ARE BLUE IF YOU DON'T S...\n",
       "2              0  BREAKING NEWS: Russia releases photo of DONALD...\n",
       "3              0                       MAN SEEKING WOMAN Ignad 18 O\n",
       "4              0  Me explaining the deep lore of. J.R.R. Tolkein...\n",
       "...          ...                                                ...\n",
       "9995           0      WAITING FOR THE END OF THE COVID  imgflip.com\n",
       "9996           0                SMART WOMEN ARE AROUND  imgflip.com\n",
       "9997           0      GOOD GIRLS ARE BEHIND THE CORNER  imgflip.com\n",
       "9998           0                   COOKING FOR MY WIFE  imgflip.com\n",
       "9999           0  LISTEN TOMORROW WILL BE MONDAY imgflip.com FRO...\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.DataFrame()\n",
    "dataset['misogynous'] = df['misogynous']\n",
    "dataset['TextTranscription'] = df['TextTranscription']\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
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    },
    "id": "eOpRBLwlVjag",
    "outputId": "80d25a75-951b-4fa9-db3a-97858bcb77c3"
   },
   "outputs": [
    {
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       "                                                     [key], {});\n",
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       "\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",
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      "text/plain": [
       "      file_name  misogynous  shaming  stereotype  objectification  violence  \\\n",
       "0         1.jpg           0        0           0                0         0   \n",
       "1        10.jpg           1        0           0                0         1   \n",
       "2      1000.jpg           0        0           0                0         0   \n",
       "3     10000.jpg           0        0           0                0         0   \n",
       "4     10006.jpg           0        0           0                0         0   \n",
       "...         ...         ...      ...         ...              ...       ...   \n",
       "9995  15002.jpg           0        0           0                0         0   \n",
       "9996  15003.jpg           0        0           0                0         0   \n",
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       "9998  15005.jpg           0        0           0                0         0   \n",
       "9999  15006.jpg           0        0           0                0         0   \n",
       "\n",
       "                                      TextTranscription  \n",
       "0                                         Milk Milk.zip  \n",
       "1     ROSES ARE RED, VIOLETS ARE BLUE IF YOU DON'T S...  \n",
       "2     BREAKING NEWS: Russia releases photo of DONALD...  \n",
       "3                          MAN SEEKING WOMAN Ignad 18 O  \n",
       "4     Me explaining the deep lore of. J.R.R. Tolkein...  \n",
       "...                                                 ...  \n",
       "9995      WAITING FOR THE END OF THE COVID  imgflip.com  \n",
       "9996                SMART WOMEN ARE AROUND  imgflip.com  \n",
       "9997      GOOD GIRLS ARE BEHIND THE CORNER  imgflip.com  \n",
       "9998                   COOKING FOR MY WIFE  imgflip.com  \n",
       "9999  LISTEN TOMORROW WILL BE MONDAY imgflip.com FRO...  \n",
       "\n",
       "[10000 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['Text Transcription']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "EoDRfta5NPR9",
    "outputId": "7b0db1a0-4b73-45de-be0a-1f068f6bcd9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7aa4541750>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['misogynous']).size().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "4a8da086422240eebe79cf89d5e92be2",
      "2d811c00efbf409da5d3a83f5f1fc69e",
      "47c85cb16fed458b89ec757bd222adcb",
      "5df4293bdadd4457bb513792b73deadb",
      "e3eee95ef0ad4477871be4aa789e42c0",
      "45f9004368b74019b273e121c59229b3",
      "2150c7a643da463eb155604580882b3a",
      "5ca90e0285aa4d84ade8232a35413388",
      "0fa7278914f444beb34f82c5c71792af",
      "1d49e99fbff74b06bbb9bd64a37f303b",
      "7340c901bd1144529e589bcad8cfca94",
      "b47c79990c994b77aba6a3b47b61e3d7",
      "0545b2a3048c4faead291da8b60629a1",
      "1fcf503d5c364d9ab2b7cf81570e4de5",
      "ecc8c85a6a5f4e2091744fc4cd32df71",
      "24464a6feecb40158f9d02c158c0290b",
      "9701bbb675ef419fbf84935480419598",
      "81e3177c31bf4558ac8310efb138723c",
      "5786ef67facc4571802192cf034e64db",
      "982aa57d58b04f56b806011264997ffe",
      "bdc1d5b0d193434584c9ddbeccc74db2",
      "74d58ddf3bba4f688639b27b41ef2611",
      "56007e5d9f3249528d829a0ce8fc53d6",
      "0006cbfb08374627b1a7aa3497d6d3a9",
      "98ce101b1ad443d5a06542fbf2b76c97",
      "70a67780290b44929c239bc724551b1d",
      "08470989488b4d138591d02179b69e97",
      "534b5ebe2399496eb6fae492b33c931e",
      "6446b458631e41c8943ba7156d7a938f",
      "f2cec310a13c42519bb492b5ec19079f",
      "5e1be249882b4d56a50254b4707e2a08",
      "f62e7bb927284970be595313bbbbfeef",
      "7f758fc69f5b431b81035d79c30afe90"
     ]
    },
    "id": "m8OphndbNbXe",
    "outputId": "57f7426c-b1ef-494d-e791-b86fe0ee8b27"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a8da086422240eebe79cf89d5e92be2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/208k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b47c79990c994b77aba6a3b47b61e3d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56007e5d9f3249528d829a0ce8fc53d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
    "labels = {'0':0,\n",
    "          '1':1\n",
    "          }\n",
    "\n",
    "class Dataset(torch.utils.data.Dataset):\n",
    "\n",
    "    def __init__(self, df):\n",
    "\n",
    "        self.labels = [labels[str(label)] for label in df['misogynous']]\n",
    "        self.texts = [tokenizer(text, \n",
    "                               padding='max_length', max_length = 512, truncation=True,\n",
    "                                return_tensors=\"pt\") for text in df['TextTranscription']]\n",
    "\n",
    "    def classes(self):\n",
    "        return self.labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def get_batch_labels(self, idx):\n",
    "        # Fetch a batch of labels\n",
    "        return np.array(self.labels[idx])\n",
    "\n",
    "    def get_batch_texts(self, idx):\n",
    "        # Fetch a batch of inputs\n",
    "        return self.texts[idx]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "\n",
    "        batch_texts = self.get_batch_texts(idx)\n",
    "        batch_y = self.get_batch_labels(idx)\n",
    "\n",
    "        return batch_texts, batch_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "2on7FCMGNi5S"
   },
   "outputs": [],
   "source": [
    "class BertClassifier(nn.Module):\n",
    "\n",
    "    def __init__(self, dropout=0.5):\n",
    "\n",
    "        super(BertClassifier, self).__init__()\n",
    "\n",
    "        self.bert = BertModel.from_pretrained('bert-base-cased')\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.linear = nn.Linear(768, 5)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, input_id, mask):\n",
    "\n",
    "        _, pooled_output = self.bert(input_ids= input_id, attention_mask=mask,return_dict=False)\n",
    "        dropout_output = self.dropout(pooled_output)\n",
    "        linear_output = self.linear(dropout_output)\n",
    "        final_layer = self.relu(linear_output)\n",
    "\n",
    "        return final_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "UNGdiSmdNsQi"
   },
   "outputs": [],
   "source": [
    "def train(model, train_data, val_data, learning_rate, epochs):\n",
    "\n",
    "    train, val = Dataset(train_data), Dataset(val_data)\n",
    "\n",
    "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=2, shuffle=True)\n",
    "    val_dataloader = torch.utils.data.DataLoader(val, batch_size=2)\n",
    "\n",
    "    use_cuda = torch.cuda.is_available()\n",
    "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = Adam(model.parameters(), lr= learning_rate)\n",
    "\n",
    "    if use_cuda:\n",
    "\n",
    "            model = model.cuda()\n",
    "            criterion = criterion.cuda()\n",
    "\n",
    "    for epoch_num in range(epochs):\n",
    "\n",
    "            total_acc_train = 0\n",
    "            total_loss_train = 0\n",
    "\n",
    "            for train_input, train_label in tqdm(train_dataloader):\n",
    "\n",
    "                train_label = train_label.to(device)\n",
    "                mask = train_input['attention_mask'].to(device)\n",
    "                input_id = train_input['input_ids'].squeeze(1).to(device)\n",
    "\n",
    "                output = model(input_id, mask)\n",
    "                \n",
    "                batch_loss = criterion(output, train_label)\n",
    "                total_loss_train += batch_loss.item()\n",
    "                \n",
    "                acc = (output.argmax(dim=1) == train_label).sum().item()\n",
    "                total_acc_train += acc\n",
    "                \n",
    "                model.zero_grad()\n",
    "                batch_loss.backward()\n",
    "                optimizer.step()\n",
    "            \n",
    "            total_acc_val = 0\n",
    "            total_loss_val = 0\n",
    "\n",
    "            with torch.no_grad():\n",
    "\n",
    "                for val_input, val_label in val_dataloader:\n",
    "\n",
    "                    val_label = val_label.to(device)\n",
    "                    mask = val_input['attention_mask'].to(device)\n",
    "                    input_id = val_input['input_ids'].squeeze(1).to(device)\n",
    "\n",
    "                    output = model(input_id, mask)\n",
    "\n",
    "                    batch_loss = criterion(output, val_label)\n",
    "                    total_loss_val += batch_loss.item()\n",
    "                    \n",
    "                    acc = (output.argmax(dim=1) == val_label).sum().item()\n",
    "                    total_acc_val += acc\n",
    "            \n",
    "            print(\n",
    "                f'Epochs: {epoch_num + 1} | Train Loss: {total_loss_train / len(train_data): .3f} | Train Accuracy: {total_acc_train / len(train_data): .3f} | Val Loss: {total_loss_val / len(val_data): .3f} | Val Accuracy: {total_acc_val / len(val_data): .3f}')\n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "A7ITbusbNwqG"
   },
   "outputs": [],
   "source": [
    "def evaluate(model, test_data):\n",
    "\n",
    "    test = Dataset(test_data)\n",
    "\n",
    "    test_dataloader = torch.utils.data.DataLoader(test, batch_size=2)\n",
    "\n",
    "    use_cuda = torch.cuda.is_available()\n",
    "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "\n",
    "    if use_cuda:\n",
    "\n",
    "        model = model.cuda()\n",
    "\n",
    "    total_acc_test = 0\n",
    "    with torch.no_grad():\n",
    "\n",
    "        for test_input, test_label in test_dataloader:\n",
    "\n",
    "              test_label = test_label.to(device)\n",
    "              mask = test_input['attention_mask'].to(device)\n",
    "              input_id = test_input['input_ids'].squeeze(1).to(device)\n",
    "\n",
    "              output = model(input_id, mask)\n",
    "\n",
    "              acc = (output.argmax(dim=1) == test_label).sum().item()\n",
    "              total_acc_test += acc\n",
    "    \n",
    "    print(f'Test Accuracy: {total_acc_test / len(test_data): .3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "e6GvKIRngL22",
    "outputId": "0012ec96-e04e-4087-cdf1-55b53994ac94"
   },
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       "\n",
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       "        const buttonEl =\n",
       "          document.querySelector('#df-deb29917-b4fd-4af2-b3c7-321edfa5927f button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
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       "          const element = document.querySelector('#df-deb29917-b4fd-4af2-b3c7-321edfa5927f');\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",
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       "            + ' to learn more about interactive tables.';\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      file_name  misogynous  shaming  stereotype  objectification  violence  \\\n",
       "0         1.jpg           0        0           0                0         0   \n",
       "1        10.jpg           1        0           0                0         1   \n",
       "2      1000.jpg           0        0           0                0         0   \n",
       "3     10000.jpg           0        0           0                0         0   \n",
       "4     10006.jpg           0        0           0                0         0   \n",
       "...         ...         ...      ...         ...              ...       ...   \n",
       "9995  15002.jpg           0        0           0                0         0   \n",
       "9996  15003.jpg           0        0           0                0         0   \n",
       "9997  15004.jpg           0        0           0                0         0   \n",
       "9998  15005.jpg           0        0           0                0         0   \n",
       "9999  15006.jpg           0        0           0                0         0   \n",
       "\n",
       "                                      TextTranscription  \n",
       "0                                         milk milk.zip  \n",
       "1     roses are red, violets are blue if you don't s...  \n",
       "2     breaking news: russia releases photo of donald...  \n",
       "3                          man seeking woman ignad 18 o  \n",
       "4     me explaining the deep lore of. j.r.r. tolkein...  \n",
       "...                                                 ...  \n",
       "9995      waiting for the end of the covid  imgflip.com  \n",
       "9996                smart women are around  imgflip.com  \n",
       "9997      good girls are behind the corner  imgflip.com  \n",
       "9998                   cooking for my wife  imgflip.com  \n",
       "9999  listen tomorrow will be monday imgflip.com fro...  \n",
       "\n",
       "[10000 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TextTranscription'] = df['TextTranscription'].str.lower()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BUB728yRNz1N",
    "outputId": "fcc04f25-b626-44bc-f831-eb341d979a5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000 2000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(112)\n",
    "df_train, df_val = np.split(df.sample(frac=1, random_state=42), \n",
    "                                     [int(.8*len(df))])\n",
    "\n",
    "print(len(df_train),len(df_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "3Z9C2T_ZUweL",
    "outputId": "ac6b388d-f997-4eb2-bbfa-6764de78f80c"
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-041a1348-83c1-4273-a0c7-d72c92987011 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-041a1348-83c1-4273-a0c7-d72c92987011');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      file_name  misogynous  shaming  stereotype  objectification  violence  \\\n",
       "6252   5999.jpg           0        0           0                0         0   \n",
       "4684   4334.jpg           0        0           0                0         0   \n",
       "1731   1294.jpg           0        0           0                0         0   \n",
       "4742   4391.jpg           1        0           0                1         0   \n",
       "4521   4166.jpg           1        0           0                0         1   \n",
       "...         ...         ...      ...         ...              ...       ...   \n",
       "1638   1185.jpg           1        0           1                0         1   \n",
       "5891   5613.jpg           0        0           0                0         0   \n",
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       "6907   6704.jpg           1        0           1                0         0   \n",
       "\n",
       "                                      TextTranscription  \n",
       "6252                oh you're not gay? name every women  \n",
       "4684  when she buys a new toy: heinerle knatterwalze...  \n",
       "1731  cyberbullying? here's one quick solution memes...  \n",
       "4742  girlfriend catches you jerking off say \"thats ...  \n",
       "4521  chris brown's greatest hits memecenter.com mem...  \n",
       "...                                                 ...  \n",
       "1638  advanced photoshop lessons nâ°13 step 1 - open...  \n",
       "5891  social distancing in canada le www dry the whi...  \n",
       "7427  feminist when they're baking and not baqueen a...  \n",
       "608   hi hotel room cooking pt.7 quarantined chef ma...  \n",
       "6907  dappreciate ur concern but aren't u suppose to...  \n",
       "\n",
       "[8000 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
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    {
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       "Downloading:   0%|          | 0.00/416M [00:00<?, ?B/s]"
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    {
     "name": "stderr",
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     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "100%|██████████| 4000/4000 [30:01<00:00,  2.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: 1 | Train Loss:  0.502 | Train Accuracy:  0.600 | Val Loss:  0.289 | Val Accuracy:  0.747\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4000/4000 [30:00<00:00,  2.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: 2 | Train Loss:  0.243 | Train Accuracy:  0.795 | Val Loss:  0.245 | Val Accuracy:  0.781\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 61%|██████    | 2443/4000 [18:14<11:35,  2.24it/s]"
     ]
    }
   ],
   "source": [
    "EPOCHS = 3\n",
    "model = BertClassifier()\n",
    "LR = 1e-6\n",
    "              \n",
    "train(model, df_train, df_val, LR, EPOCHS)"
   ]
  },
  {
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   "execution_count": 2,
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    "outputId": "02c3484d-8079-4894-ba65-0a839133a8a7",
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   "source": [
    "#evaluate(model, df_test)"
   ]
  }
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