3. Text classification using BERT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39b356a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import time \n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "from argparse import Namespace\n",
    "\n",
    "import torch\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.utils.data import TensorDataset, DataLoader, RandomSampler\n",
    "from torch.optim import AdamW\n",
    "from transformers import BertTokenizerFast, BertForSequenceClassification, \\\n",
    "    get_linear_schedule_with_warmup, BertConfig\n",
    "\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a29ed7bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "args = Namespace(\n",
    "    dataset_dir = \".data\",\n",
    "    dataset_prefix = \"faq_with_splits_\",\n",
    "    dataset = \"lemmatized_filtered\",\n",
    "    models = [\"SZTAKI-HLT/hubert-base-cc\", \"bert-base-multilingual-uncased\"],\n",
    "    model_save_dir = \".model_storage/BERT\",\n",
    "    model_state_file = \"model\",\n",
    "    seed = 1234,\n",
    "    num_epochs = 3,\n",
    "    learning_rate = 5e-5,\n",
    "    hidden_size = 100,\n",
    "    batch_size = 16,\n",
    "    cuda = True,\n",
    "    train_column = ['short_question', 'long_question']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1192120",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if args.cuda & torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fbe683b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "args.model_state_file = os.path.join(args.model_save_dir, args.model_state_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c586f7b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".model_storage/BERT directory already exists\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(args.model_save_dir):\n",
    "    os.makedirs(args.model_save_dir)\n",
    "    print(f\"Created directory {args.model_save_dir}\")\n",
    "else:\n",
    "    print(f\"{args.model_save_dir} directory already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87116c39",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".data\\faq_with_splits_lemmatized_filtered\n"
     ]
    }
   ],
   "source": [
    "args.dataset = os.path.join(args.dataset_dir, args.dataset_prefix + args.dataset)\n",
    "print(args.dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f090921",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "random.seed(args.seed)\n",
    "np.random.seed(args.seed)\n",
    "torch.manual_seed(args.seed)\n",
    "torch.cuda.manual_seed_all(args.seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8cc17295",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tokenizers = {}\n",
    "\n",
    "for model in args.models:\n",
    "    tokenizers[model] = BertTokenizerFast.from_pretrained(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20860c51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(args.dataset + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e01281c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short_question</th>\n",
       "      <th>long_question</th>\n",
       "      <th>answer</th>\n",
       "      <th>main_category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vállsérülés gyógytorna</td>\n",
       "      <td>vállsérülés gyógytorna az kérdés rossz váll sz...</td>\n",
       "      <td>edzeni kénealapjába véve mindent váll, hát, tr...</td>\n",
       "      <td>Egészség</td>\n",
       "      <td>Sérülések, balesetek</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>heti hányszor dehidtratálja szervezet citromos...</td>\n",
       "      <td>heti hányszor dehidtratálja szervezet citromos...</td>\n",
       "      <td>Nem értem miért akarod magad dehidratálni, nem...</td>\n",
       "      <td>Egészség</td>\n",
       "      <td>Bio életmód</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a doctor who ban vörös hajú lány</td>\n",
       "      <td>a doctor who ban vörös hajú lány</td>\n",
       "      <td>Vagy Amy Pond vagy Donna Noble</td>\n",
       "      <td>Szórakozás</td>\n",
       "      <td>Filmek, sorozatok</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...</td>\n",
       "      <td>vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...</td>\n",
       "      <td>Egy törpenyuszihoz nagyon sok türelem kell. Pr...</td>\n",
       "      <td>Állatok</td>\n",
       "      <td>Kisemlősök</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fityma szűkület</td>\n",
       "      <td>fityma szűkület 15 éves fiú nyugalmi állapot p...</td>\n",
       "      <td>Valószínű. Esetleg még a fitymaféked lehet röv...</td>\n",
       "      <td>Egészség</td>\n",
       "      <td>Férfiak egészsége</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      short_question  \\\n",
       "0                             vállsérülés gyógytorna   \n",
       "1  heti hányszor dehidtratálja szervezet citromos...   \n",
       "2                   a doctor who ban vörös hajú lány   \n",
       "3  vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...   \n",
       "4                                    fityma szűkület   \n",
       "\n",
       "                                       long_question  \\\n",
       "0  vállsérülés gyógytorna az kérdés rossz váll sz...   \n",
       "1  heti hányszor dehidtratálja szervezet citromos...   \n",
       "2                   a doctor who ban vörös hajú lány   \n",
       "3  vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...   \n",
       "4  fityma szűkület 15 éves fiú nyugalmi állapot p...   \n",
       "\n",
       "                                              answer main_category  \\\n",
       "0  edzeni kénealapjába véve mindent váll, hát, tr...      Egészség   \n",
       "1  Nem értem miért akarod magad dehidratálni, nem...      Egészség   \n",
       "2                     Vagy Amy Pond vagy Donna Noble    Szórakozás   \n",
       "3  Egy törpenyuszihoz nagyon sok türelem kell. Pr...       Állatok   \n",
       "4  Valószínű. Esetleg még a fitymaféked lehet röv...      Egészség   \n",
       "\n",
       "           sub_category  split  \n",
       "0  Sérülések, balesetek  train  \n",
       "1           Bio életmód  train  \n",
       "2     Filmek, sorozatok  train  \n",
       "3            Kisemlősök  train  \n",
       "4     Férfiak egészsége  train  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79052258",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = df[['main_category', *args.train_column, 'split']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6eb30534",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .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>main_category</th>\n",
       "      <th>short_question</th>\n",
       "      <th>long_question</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Egészség</td>\n",
       "      <td>vállsérülés gyógytorna</td>\n",
       "      <td>vállsérülés gyógytorna az kérdés rossz váll sz...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Egészség</td>\n",
       "      <td>heti hányszor dehidtratálja szervezet citromos...</td>\n",
       "      <td>heti hányszor dehidtratálja szervezet citromos...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Szórakozás</td>\n",
       "      <td>a doctor who ban vörös hajú lány</td>\n",
       "      <td>a doctor who ban vörös hajú lány</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Állatok</td>\n",
       "      <td>vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...</td>\n",
       "      <td>vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Egészség</td>\n",
       "      <td>fityma szűkület</td>\n",
       "      <td>fityma szűkület 15 éves fiú nyugalmi állapot p...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  main_category                                     short_question  \\\n",
       "0      Egészség                             vállsérülés gyógytorna   \n",
       "1      Egészség  heti hányszor dehidtratálja szervezet citromos...   \n",
       "2    Szórakozás                   a doctor who ban vörös hajú lány   \n",
       "3       Állatok  vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...   \n",
       "4      Egészség                                    fityma szűkület   \n",
       "\n",
       "                                       long_question  split  \n",
       "0  vállsérülés gyógytorna az kérdés rossz váll sz...  train  \n",
       "1  heti hányszor dehidtratálja szervezet citromos...  train  \n",
       "2                   a doctor who ban vörös hajú lány  train  \n",
       "3  vesz 2 törpenyuszi szob 4 5 honap gyávák ők sz...  train  \n",
       "4  fityma szűkület 15 éves fiú nyugalmi állapot p...  train  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f2408dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Egészség', 'Szórakozás', 'Állatok', 'Számítástechnika']\n",
      "{'Egészség': 0, 'Szórakozás': 1, 'Állatok': 2, 'Számítástechnika': 3}\n"
     ]
    }
   ],
   "source": [
    "target_names = df.main_category.unique().tolist()\n",
    "target_dict = {k: v for v, k in enumerate(target_names)}\n",
    "\n",
    "print(target_names)\n",
    "print(target_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b78206a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.main_category = df.main_category.apply(lambda x: target_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b6250b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "max_seq_len = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d7474632",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def tokenize(df, tokenizer, train_column):\n",
    "    return tokenizer.batch_encode_plus(\n",
    "        df[train_column].tolist(),\n",
    "        max_length = max_seq_len,\n",
    "        padding='max_length',\n",
    "        truncation=True,\n",
    "        return_token_type_ids=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f159cadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = {}\n",
    "\n",
    "for split in ['train', 'val', 'test']:\n",
    "    dfs[split] = df[df.split == split]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88e10fd5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding data for model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'train'\n",
      "Encoding data for model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'val'\n",
      "Encoding data for model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'test'\n",
      "Encoding data for model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'train'\n",
      "Encoding data for model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'val'\n",
      "Encoding data for model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'test'\n",
      "Encoding data for model 'bert-base-multilingual-uncased' column 'short_question' split 'train'\n",
      "Encoding data for model 'bert-base-multilingual-uncased' column 'short_question' split 'val'\n",
      "Encoding data for model 'bert-base-multilingual-uncased' column 'short_question' split 'test'\n",
      "Encoding data for model 'bert-base-multilingual-uncased' column 'long_question' split 'train'\n",
      "Encoding data for model 'bert-base-multilingual-uncased' column 'long_question' split 'val'\n",
      "Encoding data for model 'bert-base-multilingual-uncased' column 'long_question' split 'test'\n"
     ]
    }
   ],
   "source": [
    "encoded_data = {}\n",
    "\n",
    "for model in args.models:\n",
    "    tokenizer = tokenizers[model]\n",
    "\n",
    "    encoded_data[model] = {}\n",
    "    \n",
    "    for train_column in args.train_column:\n",
    "        encoded_data[model][train_column] = {}\n",
    "        \n",
    "        for split in ['train', 'val', 'test']:\n",
    "            print(f\"Encoding data for model '{model}' column '{train_column}' split '{split}'\")\n",
    "            encoded_data[model][train_column][split] = tokenize(dfs[split], tokenizer, train_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e531a41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "input_ids = {}\n",
    "attention_masks = {}\n",
    "\n",
    "for model in args.models:\n",
    "    input_ids[model] = {}\n",
    "    attention_masks[model] = {}\n",
    "    \n",
    "    for train_column in args.train_column:\n",
    "        input_ids[model][train_column] = {}\n",
    "        attention_masks[model][train_column] = {}\n",
    "    \n",
    "        for split in ['train', 'val', 'test']:\n",
    "            input_ids[model][train_column][split] = encoded_data[model][train_column][split]['input_ids']\n",
    "            attention_masks[model][train_column][split] = encoded_data[model][train_column][split]['attention_mask']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b553bd26",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "labels = {}\n",
    "\n",
    "for split in ['train', 'val', 'test']:\n",
    "    labels[split] = torch.tensor(dfs[split].main_category.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6292889f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset for model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'train'\n",
      "Creating dataset for model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'val'\n",
      "Creating dataset for model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'test'\n",
      "Creating dataset for model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'train'\n",
      "Creating dataset for model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'val'\n",
      "Creating dataset for model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'test'\n",
      "Creating dataset for model 'bert-base-multilingual-uncased' column 'short_question' split 'train'\n",
      "Creating dataset for model 'bert-base-multilingual-uncased' column 'short_question' split 'val'\n",
      "Creating dataset for model 'bert-base-multilingual-uncased' column 'short_question' split 'test'\n",
      "Creating dataset for model 'bert-base-multilingual-uncased' column 'long_question' split 'train'\n",
      "Creating dataset for model 'bert-base-multilingual-uncased' column 'long_question' split 'val'\n",
      "Creating dataset for model 'bert-base-multilingual-uncased' column 'long_question' split 'test'\n"
     ]
    }
   ],
   "source": [
    "datasets = {}\n",
    "\n",
    "for model in args.models:\n",
    "    datasets[model] = {}\n",
    "    \n",
    "    for train_column in args.train_column:\n",
    "        datasets[model][train_column] = {}\n",
    "\n",
    "        for split in ['train', 'val', 'test']:\n",
    "            print(f\"Creating dataset for model '{model}' column '{train_column}' split '{split}'\")\n",
    "            datasets[model][train_column][split] = TensorDataset(\n",
    "                            torch.tensor(input_ids[model][train_column][split]),\n",
    "                            torch.tensor(attention_masks[model][train_column][split]),\n",
    "                            labels[split]\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3d248a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating loader fro model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'train'\n",
      "Creating loader fro model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'val'\n",
      "Creating loader fro model 'SZTAKI-HLT/hubert-base-cc' column 'short_question' split 'test'\n",
      "Creating loader fro model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'train'\n",
      "Creating loader fro model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'val'\n",
      "Creating loader fro model 'SZTAKI-HLT/hubert-base-cc' column 'long_question' split 'test'\n",
      "Creating loader fro model 'bert-base-multilingual-uncased' column 'short_question' split 'train'\n",
      "Creating loader fro model 'bert-base-multilingual-uncased' column 'short_question' split 'val'\n",
      "Creating loader fro model 'bert-base-multilingual-uncased' column 'short_question' split 'test'\n",
      "Creating loader fro model 'bert-base-multilingual-uncased' column 'long_question' split 'train'\n",
      "Creating loader fro model 'bert-base-multilingual-uncased' column 'long_question' split 'val'\n",
      "Creating loader fro model 'bert-base-multilingual-uncased' column 'long_question' split 'test'\n"
     ]
    }
   ],
   "source": [
    "dataloaders = {}\n",
    "\n",
    "for model in args.models:\n",
    "    dataloaders[model] = {}\n",
    "    \n",
    "    for train_column in args.train_column:\n",
    "        dataloaders[model][train_column] = {}\n",
    "\n",
    "        for split in ['train', 'val', 'test']:\n",
    "            print(f\"Creating loader fro model '{model}' column '{train_column}' split '{split}'\")\n",
    "            dataloaders[model][train_column][split] = DataLoader(\n",
    "                datasets[model][train_column][split],\n",
    "                sampler=RandomSampler(datasets[model][train_column][split]),\n",
    "                batch_size=args.batch_size\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "29adf744",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7529209  1.05188476 0.82005859 1.99306534]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass classes=[0 1 2 3], y=0         0\n",
      "1         0\n",
      "2         1\n",
      "3         2\n",
      "4         0\n",
      "         ..\n",
      "153550    1\n",
      "153551    0\n",
      "153552    0\n",
      "153553    2\n",
      "153555    3\n",
      "Name: main_category, Length: 107490, dtype: int64 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    }
   ],
   "source": [
    "class_wts = compute_class_weight('balanced', np.unique(dfs['train'].main_category), \\\n",
    "                                 dfs['train'].main_category)\n",
    "\n",
    "print(class_wts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57b0c133",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(class_wts, dtype=torch.float)\n",
    "weights = weights.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fbbad3dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def class_accuracy(preds, labels):\n",
    "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    return accuracy_score(y_true=labels_flat, y_pred=preds_flat)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "717b56da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def show_data_to_model(model, optimizer, scheduler, criterion, bar, train):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "       \n",
    "    total_predicted = []\n",
    "    true_vals = []\n",
    "    idx = 0\n",
    "\n",
    "    for batch in bar:\n",
    "        idx += 1\n",
    "        if train:\n",
    "            optimizer.zero_grad()\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",
    "        \n",
    "        outputs = model(**inputs)\n",
    "        \n",
    "        logits = outputs[1]\n",
    "        label_ids = inputs['labels'].cpu().numpy()\n",
    "\n",
    "        loss = criterion(logits, batch[2])\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        acc = class_accuracy(logits, label_ids)\n",
    "\n",
    "        total_predicted += np.argmax(logits, axis=1).flatten().tolist()\n",
    "        true_vals += label_ids.flatten().tolist()\n",
    "\n",
    "        if train:\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "\n",
    "        del batch\n",
    "            \n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "            \n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "       \n",
    "        bar.set_postfix(loss=(epoch_loss / idx), acc=(epoch_acc / idx))\n",
    "\n",
    "    return epoch_loss / idx , epoch_acc / idx, total_predicted, true_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37e409b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_model(model, optimizer, scheduler, criterion, bar):\n",
    "    model.train()\n",
    "    loss, acc, _, _ = show_data_to_model(model, optimizer, scheduler, criterion, bar, True)\n",
    "    \n",
    "    return loss, acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8f9bf8d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def evaluate_model(model, optimizer, scheduler, criterion, bar):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        loss, acc, pred, true_vals = show_data_to_model(model, optimizer, scheduler, criterion, bar, False)\n",
    "            \n",
    "    return loss, acc, pred, true_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8bd882bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from transformers import logging\n",
    "logging.set_verbosity_error()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fe93b5de",
   "metadata": {
    "scrolled": true
   },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "train_losses = {}\n",
    "train_accuracies = {}\n",
    "\n",
    "valid_losses = {}\n",
    "valid_accuracies = {}\n",
    "\n",
    "models = {}\n",
    "\n",
    "for model_ in args.models:\n",
    "    train_losses[model_] = {}\n",
    "    train_accuracies[model_] = {}\n",
    "\n",
    "    valid_losses[model_] = {}\n",
    "    valid_accuracies[model_] = {}\n",
    "    \n",
    "    models[model_] = {} \n",
    "    \n",
    "    for train_column in args.train_column:\n",
    "        train_losses[model_][train_column] = []\n",
    "        train_accuracies[model_][train_column] = []\n",
    "\n",
    "        valid_losses[model_][train_column] = []\n",
    "        valid_accuracies[model_][train_column] = []\n",
    "        \n",
    "        best_valid_loss = float('inf')\n",
    "        \n",
    "        train_dl = dataloaders[model_][train_column]['train']\n",
    "        valid_dl = dataloaders[model_][train_column]['val']    \n",
    "        \n",
    "        model = BertForSequenceClassification.from_pretrained(\n",
    "            model_,\n",
    "            num_labels = len(target_names),\n",
    "            output_attentions=False,\n",
    "            output_hidden_states=False   \n",
    "        )\n",
    "        \n",
    "        optimizer = AdamW(\n",
    "            model.parameters(),\n",
    "            args.learning_rate,\n",
    "            eps=1e-8\n",
    "        )\n",
    "    \n",
    "        scheduler = get_linear_schedule_with_warmup(\n",
    "            optimizer,\n",
    "            num_warmup_steps=0,\n",
    "            num_training_steps=len(train_dl) * args.num_epochs\n",
    "        )\n",
    "    \n",
    "        criterion = CrossEntropyLoss(weight=weights)\n",
    "    \n",
    "        model = model.to(device)\n",
    "        criterion = criterion.to(device)\n",
    "   \n",
    "        epoch_bar = tqdm(desc=f\"'{model_}' '{train_column}' epoch\", total=args.num_epochs, position=0, leave=True)\n",
    "        \n",
    "        for epoch in range(args.num_epochs):\n",
    "            train_bar = tqdm(train_dl, desc=f\"'{model_}' '{train_column}' train\", leave=False)\n",
    "            valid_bar = tqdm(valid_dl, desc=f\"'{model_}' '{train_column}' valid\", leave=False)\n",
    "\n",
    "            train_loss, train_acc = train_model(model, optimizer, scheduler, criterion, train_bar)\n",
    "            valid_loss, valid_acc, _, _ = evaluate_model(model, optimizer, scheduler, criterion, valid_bar)\n",
    "\n",
    "            train_losses[model_][train_column].append(train_loss)\n",
    "            train_accuracies[model_][train_column].append(train_acc)\n",
    "\n",
    "            valid_losses[model_][train_column].append(valid_loss)\n",
    "            valid_accuracies[model_][train_column].append(valid_acc)\n",
    "    \n",
    "            if valid_loss < best_valid_loss:\n",
    "                best_valid_loss = valid_loss\n",
    "\n",
    "                torch.save(model.state_dict(), args.model_state_file + \\\n",
    "                           \"_\" + model_.replace('/', '_') + \\\n",
    "                           '_' + train_column + \".pth\")\n",
    " \n",
    "            epoch_bar.update()\n",
    "        models[model_][train_column] = model.to(torch.device('cpu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "38fdd737",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = len(args.models) * len(args.train_column)\n",
    "\n",
    "fig, axs = plt.subplots(rows, 2, figsize=(10 * 2, 10 * rows))\n",
    "\n",
    "for i, col in enumerate(args.train_column):\n",
    "    for j, model_ in enumerate(args.models):\n",
    "        idx = i * len(args.train_column) + j\n",
    "        \n",
    "        axs[idx, 0].set_title(f\"Loss values for '{model_}' '{col}'\")\n",
    "        axs[idx, 0].plot([*range(args.num_epochs)], train_losses[model_][col], color = 'b', label='Train loss')\n",
    "        axs[idx, 0].plot([*range(args.num_epochs)], valid_losses[model_][col], color = 'r', label='Valid loss')\n",
    "        axs[idx, 0].legend(loc=\"upper center\", bbox_to_anchor=(0.0, 1.1), ncol=1)\n",
    "\n",
    "        axs[idx, 1].set_title(f\"Accuracy values for '{model_}' '{col}'\")\n",
    "        axs[idx, 1].axis(ymin = 0, ymax = 1)\n",
    "        axs[idx, 1].plot([*range(args.num_epochs)], train_accuracies[model_][col], color = 'b', label='Train accuracy')\n",
    "        axs[idx, 1].plot([*range(args.num_epochs)], valid_accuracies[model_][col], color = 'r', label='Valid accuracy')\n",
    "        axs[idx, 1].legend(loc=\"upper center\", bbox_to_anchor=(0.0, 1.1), ncol=1)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "af6893c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "'SZTAKI-HLT/hubert-base-cc' test:   0%|          | 0/1440 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "'SZTAKI-HLT/hubert-base-cc' test:   0%|          | 0/1440 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "'bert-base-multilingual-uncased' test:   0%|          | 0/1440 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "'bert-base-multilingual-uncased' test:   0%|          | 0/1440 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = {}\n",
    "test_target = {}\n",
    "\n",
    "for model_ in args.models:\n",
    "    test_target[model_] = {}\n",
    "    predictions[model_] = {}\n",
    "\n",
    "    config = BertConfig.from_pretrained(model_)\n",
    "    config.num_labels = len(target_names)\n",
    "    config.output_attentions = False\n",
    "    config.output_hidden_states = False\n",
    "    \n",
    "    for train_column in args.train_column:\n",
    "\n",
    "        \n",
    "        test_dl = dataloaders[model_][train_column]['test']\n",
    "    \n",
    "        model = models[model_][train_column]\n",
    "        model.load_state_dict(torch.load(args.model_state_file + \\\n",
    "                                         \"_\" + model_.replace('/', '_') + \\\n",
    "                                         '_' + train_column + \".pth\"))\n",
    "\n",
    "        criterion = CrossEntropyLoss(weight=weights)\n",
    "    \n",
    "        model = model.to(device)\n",
    "        criterion = criterion.to(device)\n",
    "    \n",
    "        test_bar = tqdm(test_dl, desc=f\"'{model_} {train_column}' test\", leave=False)\n",
    "\n",
    "        _, _, predictions[model_][train_column], test_target[model_][train_column] = \\\n",
    "                    evaluate_model(model, None, None, criterion, test_bar)\n",
    "\n",
    "        del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "832cf33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = {}\n",
    "\n",
    "for tc in args.train_column:\n",
    "\n",
    "    accuracy[tc] = {}\n",
    "\n",
    "    for model_ in args.models:\n",
    "        prediction = predictions[model_][tc]\n",
    "        target = test_target[model_][tc]\n",
    "        \n",
    "        accuracy[tc][model_] = round(accuracy_score(target, prediction), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c378974e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(11, 5))\n",
    "\n",
    "acc_df = pd.DataFrame({f\"{tc}\": accuracy[tc].values() for tc in args.train_column}, index=args.models)        \n",
    "acc_df.plot(kind='bar', ax=ax)\n",
    "\n",
    "ax.axis(ymin = 0.85, ymax = 1)\n",
    "\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(0)\n",
    "\n",
    "for label in ax.containers:\n",
    "    ax.bar_label(label)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c4a44c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(len(args.models), len(args.train_column), figsize=(8 * len(args.train_column), 8 * len(args.models)))\n",
    "\n",
    "for i, model_ in enumerate(args.models):\n",
    "    for j, train_column in enumerate(args.train_column):\n",
    "        axs[i, j].set_title(f\"Confusion matrix for '{model_}', '{train_column}'\")\n",
    "    \n",
    "        prediction = predictions[model_][train_column]\n",
    "        target = test_target[model_][train_column]\n",
    "        \n",
    "        cm = confusion_matrix(target, prediction)\n",
    "        cm_df = pd.DataFrame(cm, index=target_names, columns=target_names)\n",
    "    \n",
    "        heatmap = sn.heatmap(cm_df, annot=True, ax=axs[i, j], cmap='Reds', fmt='g', annot_kws={\"size\": 15}, cbar=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "870c0926",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for 'SZTAKI-HLT/hubert-base-cc' 'short_question':\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "        Egészség       0.94      0.95      0.95      7648\n",
      "      Szórakozás       0.93      0.90      0.91      5474\n",
      "         Állatok       0.95      0.93      0.94      7022\n",
      "Számítástechnika       0.85      0.91      0.88      2889\n",
      "\n",
      "        accuracy                           0.93     23033\n",
      "       macro avg       0.92      0.92      0.92     23033\n",
      "    weighted avg       0.93      0.93      0.93     23033\n",
      "\n",
      "============================================================\n",
      "Classification report for 'bert-base-multilingual-uncased' 'short_question':\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "        Egészség       0.93      0.94      0.94      7648\n",
      "      Szórakozás       0.92      0.90      0.91      5474\n",
      "         Állatok       0.94      0.93      0.93      7022\n",
      "Számítástechnika       0.86      0.89      0.87      2889\n",
      "\n",
      "        accuracy                           0.92     23033\n",
      "       macro avg       0.91      0.91      0.91     23033\n",
      "    weighted avg       0.92      0.92      0.92     23033\n",
      "\n",
      "============================================================\n",
      "Classification report for 'SZTAKI-HLT/hubert-base-cc' 'long_question':\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "        Egészség       0.97      0.97      0.97      7648\n",
      "      Szórakozás       0.94      0.93      0.93      5474\n",
      "         Állatok       0.98      0.98      0.98      7022\n",
      "Számítástechnika       0.91      0.93      0.92      2889\n",
      "\n",
      "        accuracy                           0.96     23033\n",
      "       macro avg       0.95      0.95      0.95     23033\n",
      "    weighted avg       0.96      0.96      0.96     23033\n",
      "\n",
      "============================================================\n",
      "Classification report for 'bert-base-multilingual-uncased' 'long_question':\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "        Egészség       0.97      0.96      0.97      7648\n",
      "      Szórakozás       0.93      0.92      0.92      5474\n",
      "         Állatok       0.98      0.96      0.97      7022\n",
      "Számítástechnika       0.88      0.94      0.91      2889\n",
      "\n",
      "        accuracy                           0.95     23033\n",
      "       macro avg       0.94      0.95      0.94     23033\n",
      "    weighted avg       0.95      0.95      0.95     23033\n",
      "\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "for train_column in args.train_column:\n",
    "    for model_ in args.models:\n",
    "        prediction = predictions[model_][train_column]\n",
    "        target = test_target[model_][train_column]\n",
    "        \n",
    "        report = classification_report(target, prediction, target_names=target_names)\n",
    "        print(f\"Classification report for '{model_}' '{train_column}':\\n{report}\\n{'=' * 60}\")"
   ]
  }
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