Alpert.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-KXX2roXE8AN"
   },
   "source": [
    "# Get The Data Ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "rTCYOwqItUcn"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import DataLoader, RandomSampler, TensorDataset\n",
    "from tqdm.notebook import tqdm\n",
    "from transformers import (\n",
    "    AdamW,\n",
    "    AlbertForSequenceClassification,\n",
    "    AlbertTokenizer,\n",
    "    get_linear_schedule_with_warmup,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8FLWEGmAFIrB"
   },
   "source": [
    "train validarion split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "xOPJ7J7MFGN-"
   },
   "outputs": [],
   "source": [
    "X_train , X_val , y_train , y_val = train_test_split(\n",
    "    df.index.values,\n",
    "    df.label.values,\n",
    "    test_size = 0.15 , \n",
    "    random_state = 17,\n",
    "    stratify = df.label.values\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "KsKaz1sth_Jl"
   },
   "outputs": [],
   "source": [
    "df['data_type'] = ['not_set'] *df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "b1hwm59CiZCp"
   },
   "outputs": [],
   "source": [
    "df.loc[X_train, 'data_type'] = 'train'\n",
    "df.loc[X_val, 'data_type'] = 'val'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "fBwJnp55FlSG",
    "outputId": "90ffde12-ca6d-42fd-966d-88aeb776e14c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>text</th>\n",
       "      <th>tokens</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>polarity</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th>data_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bullying</th>\n",
       "      <th>train</th>\n",
       "      <td>8131</td>\n",
       "      <td>8131</td>\n",
       "      <td>8131</td>\n",
       "      <td>8131</td>\n",
       "      <td>8131</td>\n",
       "      <td>8131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val</th>\n",
       "      <td>1436</td>\n",
       "      <td>1436</td>\n",
       "      <td>1436</td>\n",
       "      <td>1436</td>\n",
       "      <td>1436</td>\n",
       "      <td>1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">nonbullying</th>\n",
       "      <th>train</th>\n",
       "      <td>7446</td>\n",
       "      <td>7446</td>\n",
       "      <td>7446</td>\n",
       "      <td>7446</td>\n",
       "      <td>7446</td>\n",
       "      <td>7446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val</th>\n",
       "      <td>1314</td>\n",
       "      <td>1314</td>\n",
       "      <td>1314</td>\n",
       "      <td>1314</td>\n",
       "      <td>1314</td>\n",
       "      <td>1314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Unnamed: 0  text  tokens  subjectivity  polarity  \\\n",
       "label       data_type                                                     \n",
       "bullying    train            8131  8131    8131          8131      8131   \n",
       "            val              1436  1436    1436          1436      1436   \n",
       "nonbullying train            7446  7446    7446          7446      7446   \n",
       "            val              1314  1314    1314          1314      1314   \n",
       "\n",
       "                       Analysis  \n",
       "label       data_type            \n",
       "bullying    train          8131  \n",
       "            val            1436  \n",
       "nonbullying train          7446  \n",
       "            val            1314  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([  'label' , 'data_type']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "b33vtxB_OiNA"
   },
   "outputs": [],
   "source": [
    "labels = [ 'nonbullying','bullying']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "EhvgSB17VAcM"
   },
   "outputs": [],
   "source": [
    "label_dic = {}\n",
    "for index,label in enumerate(labels):\n",
    "    label_dic[label] = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0BAAkK1bvZyP",
    "outputId": "4e0e41cd-36a2-4168-8dc9-f94590e6cb98"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nonbullying': 0, 'bullying': 1}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "TgAXLpxwv8KL"
   },
   "outputs": [],
   "source": [
    "df['label'] = df.label.replace(label_dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zbmYlM2fFoQY"
   },
   "source": [
    "# Encoding Our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
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    "outputId": "e5b64996-4fdc-4e29-b42e-a4f746618205"
   },
   "outputs": [],
   "source": [
    "tokenizer =AlbertTokenizer.from_pretrained('albert-base-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o7pwDG7Bj8bs",
    "outputId": "42c6963e-ef10-4880-8756-3ed7edc577bd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "/home/mohres/Desktop/tweet-cyperbullying-detecting/.venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "encoded_data_train = tokenizer.batch_encode_plus(\n",
    "    df[df.data_type == 'train'].text.values,\n",
    "    add_special_tokens = True ,\n",
    "    return_attention_mask = True,\n",
    "    pad_to_max_length = True,\n",
    "    max_length = 256,\n",
    "    return_tensors = 'pt'\n",
    "\n",
    ")\n",
    "encoded_data_val = tokenizer.batch_encode_plus(\n",
    "    df[df.data_type == 'val'].text.values,\n",
    "    add_special_tokens = True ,\n",
    "    return_attention_mask = True,\n",
    "    pad_to_max_length = True,\n",
    "    max_length = 256,\n",
    "    return_tensors = 'pt'\n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "input_ids_train  = encoded_data_train['input_ids']\n",
    "attention_masks_train = encoded_data_train['attention_mask']\n",
    "labels_train = torch.tensor(df[df.data_type == 'train'].label.values)\n",
    "\n",
    "input_ids_val = encoded_data_val['input_ids']\n",
    "attention_masks_val = encoded_data_val['attention_mask']\n",
    "labels_val = torch.tensor(df[df.data_type == 'val'].label.values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aI14YQKvF_ys"
   },
   "source": [
    "Pytorch Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "ykxH0ZQSkL7W"
   },
   "outputs": [],
   "source": [
    "dataset_train = TensorDataset(input_ids_train,\n",
    "                              attention_masks_train,\n",
    "                              labels_train)\n",
    "dataset_val = TensorDataset(input_ids_val,\n",
    "                              attention_masks_val,\n",
    "                              labels_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_by0lCGOIfSW"
   },
   "source": [
    "# **Setting** up the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156,
     "referenced_widgets": [
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    "id": "AaXTam_WkAZg",
    "outputId": "6aa72a7c-53cf-4f9e-b74c-98b5b7a9200d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForSequenceClassification: ['predictions.dense.weight', 'predictions.dense.bias', 'predictions.LayerNorm.weight', 'predictions.bias', 'predictions.LayerNorm.bias', 'predictions.decoder.bias']\n",
      "- This IS expected if you are initializing AlbertForSequenceClassification 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 AlbertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AlbertForSequenceClassification.from_pretrained('albert-base-v2', num_labels=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "01X4nxhRGKkK"
   },
   "source": [
    "## Creating Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "0Ls6YAfUoqBP"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "dataloader_train = DataLoader  ( \n",
    "    dataset_train ,\n",
    "    sampler = RandomSampler(dataset_train),\n",
    "    batch_size = batch_size\n",
    "\n",
    ")\n",
    "\n",
    "dataloader_val = DataLoader(\n",
    "    dataset_val,\n",
    "    sampler=RandomSampler(dataset_val),\n",
    "    batch_size = 32\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5dLkiIyGGUAm"
   },
   "source": [
    "# Setting Up Optimizer and Scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lvN3dMVgpJ-2",
    "outputId": "206eac39-b993-4e22-c72c-981bc76d13bc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mohres/Desktop/tweet-cyperbullying-detecting/.venv/lib/python3.10/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "optimizer = AdamW(\n",
    "    model.parameters(),\n",
    "    lr=5e-5, #2e-5 > 5e-5\n",
    "    eps=1e-8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "U5rBb0YqpMm1"
   },
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    num_warmup_steps = 0,\n",
    "    num_training_steps = len(dataloader_train)*epochs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "FCK1IpcRr88P"
   },
   "outputs": [],
   "source": [
    "def accuracy_per_class(preds, labels):\n",
    "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    acc = 0\n",
    "\n",
    "    for label in np.unique(labels_flat):\n",
    "        y_preds = preds_flat[labels_flat == label]\n",
    "        y_true = labels_flat[labels_flat == label]\n",
    "        acc += len(y_preds[y_preds == label]) / len(y_true)\n",
    "    return acc_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UHgwGLfvHkx3"
   },
   "source": [
    "# Creating our Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "SXvttJxWpcF0"
   },
   "outputs": [],
   "source": [
    "seed_val = 17\n",
    "random.seed(seed_val)\n",
    "np.random.seed(seed_val)\n",
    "torch.manual_seed(seed_val)\n",
    "torch.cuda.manual_seed_all(seed_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "2ZOzqB6LpgG8"
   },
   "outputs": [],
   "source": [
    "def evaluate(dataloader_val):\n",
    "\n",
    "    model.eval()\n",
    "    \n",
    "    loss_val_total = 0\n",
    "    predictions, true_vals = [], []\n",
    "    \n",
    "    for batch in dataloader_val:\n",
    "        \n",
    "        batch = tuple(b.to(device) for b in batch)\n",
    "        \n",
    "        inputs = {'input_ids':      batch[0],\n",
    "                  'attention_mask': batch[1],\n",
    "                  'labels':         batch[2],\n",
    "                 }\n",
    "\n",
    "        with torch.no_grad():        \n",
    "            outputs = model(**inputs)\n",
    "            \n",
    "        loss = outputs[0]\n",
    "        logits = outputs[1]\n",
    "        loss_val_total += loss.item()\n",
    "\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        label_ids = inputs['labels'].cpu().numpy()\n",
    "        predictions.append(logits)\n",
    "        true_vals.append(label_ids)\n",
    "    \n",
    "    loss_val_avg = loss_val_total/len(dataloader_val) \n",
    "    \n",
    "    predictions = np.concatenate(predictions, axis=0)\n",
    "    true_vals = np.concatenate(true_vals, axis=0)\n",
    "\n",
    "            \n",
    "    return loss_val_avg, predictions, true_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C-_Y_UyBH5sD",
    "outputId": "e1cb32c4-5271-4661-f469-35f32051cb66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
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      },
      "text/plain": [
       "Epoch1:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1\n",
      "Validation loss: 0.47132582423534797\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.95      0.87       717\n",
      "           1       0.95      0.79      0.86       783\n",
      "\n",
      "    accuracy                           0.87      1500\n",
      "   macro avg       0.88      0.87      0.87      1500\n",
      "weighted avg       0.88      0.87      0.87      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[682  35]\n",
      " [161 622]]\n",
      "Class: nonbullying\n",
      "Accuracy: 682/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 622/783\n",
      "\n"
     ]
    },
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      },
      "text/plain": [
       "Epoch2:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2\n",
      "Validation loss: 0.58982530172835\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.68      0.70       717\n",
      "           1       0.72      0.76      0.74       783\n",
      "\n",
      "    accuracy                           0.72      1500\n",
      "   macro avg       0.72      0.72      0.72      1500\n",
      "weighted avg       0.72      0.72      0.72      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[487 230]\n",
      " [185 598]]\n",
      "Class: nonbullying\n",
      "Accuracy: 487/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 598/783\n",
      "\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Epoch3:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3\n",
      "Validation loss: 0.4199385528868817\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.99      0.83       717\n",
      "           1       0.99      0.64      0.78       783\n",
      "\n",
      "    accuracy                           0.81      1500\n",
      "   macro avg       0.85      0.82      0.80      1500\n",
      "weighted avg       0.86      0.81      0.80      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[713   4]\n",
      " [284 499]]\n",
      "Class: nonbullying\n",
      "Accuracy: 713/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 499/783\n",
      "\n"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch4:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4\n",
      "Validation loss: 0.48527206797549066\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.97      0.85       717\n",
      "           1       0.96      0.71      0.81       783\n",
      "\n",
      "    accuracy                           0.83      1500\n",
      "   macro avg       0.86      0.84      0.83      1500\n",
      "weighted avg       0.86      0.83      0.83      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[695  22]\n",
      " [230 553]]\n",
      "Class: nonbullying\n",
      "Accuracy: 695/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 553/783\n",
      "\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch5:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 5\n",
      "Validation loss: 0.6932871303659804\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       717\n",
      "           1       0.52      1.00      0.69       783\n",
      "\n",
      "    accuracy                           0.52      1500\n",
      "   macro avg       0.26      0.50      0.34      1500\n",
      "weighted avg       0.27      0.52      0.36      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[  0 717]\n",
      " [  0 783]]\n",
      "Class: nonbullying\n",
      "Accuracy: 0/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 783/783\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch6:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 6\n",
      "Validation loss: 0.42502035581051034\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.95      0.87       717\n",
      "           1       0.95      0.80      0.86       783\n",
      "\n",
      "    accuracy                           0.87      1500\n",
      "   macro avg       0.88      0.87      0.87      1500\n",
      "weighted avg       0.88      0.87      0.87      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[682  35]\n",
      " [160 623]]\n",
      "Class: nonbullying\n",
      "Accuracy: 682/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 623/783\n",
      "\n"
     ]
    },
    {
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       "model_id": "b41c5bc19b9146f29a12b2c58b47a5a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch7:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 7\n",
      "Validation loss: 0.47106983607753794\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.95      0.87       717\n",
      "           1       0.95      0.79      0.86       783\n",
      "\n",
      "    accuracy                           0.87      1500\n",
      "   macro avg       0.88      0.87      0.87      1500\n",
      "weighted avg       0.88      0.87      0.87      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[684  33]\n",
      " [165 618]]\n",
      "Class: nonbullying\n",
      "Accuracy: 684/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 618/783\n",
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "ab94a5312a9247fc93fc0a786aa6918b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch8:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 8\n",
      "Validation loss: 0.4125560886682348\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.96      0.88       717\n",
      "           1       0.96      0.78      0.86       783\n",
      "\n",
      "    accuracy                           0.87      1500\n",
      "   macro avg       0.88      0.87      0.87      1500\n",
      "weighted avg       0.88      0.87      0.87      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[691  26]\n",
      " [171 612]]\n",
      "Class: nonbullying\n",
      "Accuracy: 691/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 612/783\n",
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "e26bb00733bf40269a1fbb4e193bd08e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch9:   0%|          | 0/2125 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 9\n",
      "Validation loss: 0.4549135620051876\n",
      "Validation metrics:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.96      0.88       717\n",
      "           1       0.96      0.79      0.86       783\n",
      "\n",
      "    accuracy                           0.87      1500\n",
      "   macro avg       0.88      0.87      0.87      1500\n",
      "weighted avg       0.88      0.87      0.87      1500\n",
      "\n",
      "Confusion matrix:\n",
      "[[690  27]\n",
      " [167 616]]\n",
      "Class: nonbullying\n",
      "Accuracy: 690/717\n",
      "\n",
      "Class: bullying\n",
      "Accuracy: 616/783\n",
      "\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-c23372d8ca8a>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Confusion matrix:\\n{confusion}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_per_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mtrue_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     hist[epoch+1] = {\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0;34m'epochIndex'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;34m'loss'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list assignment index out of range"
     ]
    }
   ],
   "source": [
    "loss_values = []\n",
    "val_loss_values = []\n",
    "training_times = []\n",
    "testing_times = []\n",
    "hist = [] \n",
    "\n",
    "for epoch in tqdm(range(1, epochs+1)):\n",
    "    model.train()\n",
    "    loss_train_total = 0 \n",
    "    progress_bar = tqdm(dataloader_train \n",
    "                        , desc='Epoch{:1d}'.format(epoch)\n",
    "                        , leave=False,\n",
    "                        disable = False)\n",
    "    start_time = time.time()\n",
    "\n",
    "\n",
    "    for batch in progress_bar:\n",
    "      model.zero_grad()\n",
    "\n",
    "      batch = tuple(b.to(device) for b in batch)\n",
    "\n",
    "      inputs = {\n",
    "          'input_ids'  : batch[0],\n",
    "          'attention_mask' : batch[1],\n",
    "          'labels'    : batch[2]\n",
    "      }\n",
    "      outputs = model(**inputs)\n",
    "      loss = outputs[0]\n",
    "      loss_train_total += loss.item()\n",
    "      \n",
    "     \n",
    "      loss.backward()\n",
    "      loss_values.append(loss.item()/len(batch))\n",
    "\n",
    "      torch.nn.utils.clip_grad_norm_(model.parameters() , 1.0)\n",
    "\n",
    "      optimizer.step()\n",
    "      scheduler.step()\n",
    "\n",
    "      progress_bar.set_postfix({'training_loss' : '{:.3f}'.format(loss.item()/len(batch)\n",
    "      )})\n",
    "    end_time = time.time()\n",
    "    train_time = end_time - start_time\n",
    "    training_times.append(train_time)\n",
    "\n",
    "\n",
    "   # torch.save(model.state_dict() , f'/content/drive/MyDrive/models/Roberta{epoch}.model')\n",
    "    tqdm.write(f'\\nEpoch {epoch}')\n",
    "    val_loss , predictions , true_vals = evaluate(dataloader_val)\n",
    "    val_loss_values.append(val_loss)\n",
    "\n",
    "    print(f'Validation loss: {val_loss}')\n",
    "  \n",
    "    # Calculate validation metrics\n",
    "    class_predictions = np.argmax(predictions, axis=1)\n",
    "    confusion = confusion_matrix(true_vals, class_predictions)\n",
    "\n",
    "    report = classification_report(true_vals, class_predictions)  \n",
    "    print(f'train_time:\\n{train_time}')\n",
    "    print(f'Validation metrics:\\n{report}')\n",
    "\n",
    "    tqdm.write(f'Confusion matrix:\\n{confusion}')\n",
    "    end_time = time.time()\n",
    "    test_time = end_time - start_time\n",
    "    testing_times.append(test_time)\n",
    "    print(f'test_time:\\n{test_time}')\n",
    "\n",
    "    accuracy = accuracy_per_class(predictions , true_vals)\n",
    "    hist.append({\n",
    "        'epochIndex': epoch,\n",
    "        'loss': loss.item(),\n",
    "        'val_loss': val_loss,\n",
    "        'classification_report': report,\n",
    "        'conf_matrix': confusion,\n",
    "        'accuracy': accuracy\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "obpKSd3kiGnS"
   },
   "outputs": [],
   "source": [
    "# Loss GRAPH\n",
    "train_losses = [hist[i]['loss'] for i in range(epochs)]\n",
    "val_losses = [hist[i]['val_loss'] for i in range(epochs)]\n",
    "\n",
    "# Accuracy değerlerini alın\n",
    "train_accuracies = [hist[i]['accuracy'] for i in range(epochs)]\n",
    "\n",
    "# Loss vs Epoch Size grafiğini çizin\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(range(1, epochs+1), train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), val_losses, label='Validation Loss')\n",
    "plt.xlabel('Epoch Size')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss vs Epoch Size')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mzT4meBjgXHE"
   },
   "outputs": [],
   "source": [
    "avg_train_time = sum(training_times) / len(training_times)\n",
    "avg_test_time = sum(testing_times) / len(testing_times)\n",
    "print(f'Average Training Time: {avg_train_time:.2f} seconds')\n",
    "print(f'Average Testing Time: {avg_test_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4iy6tilbgZKn"
   },
   "outputs": [],
   "source": [
    "val_loss , predictions , true_vals = evaluate(dataloader_val)\n",
    "class_predictions = np.argmax(predictions, axis=1)\n",
    "\n",
    "report = classification_report(true_vals, class_predictions)\n",
    "\n",
    "\n",
    "# Print the classification report\n",
    "print(\"Classification Report:\")\n",
    "print(report) \n",
    "    "
   ]
  }
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