Applying_Fine_Tuned_Bert.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pUxShfTh9xOC"
   },
   "outputs": [],
   "source": [
    "import re \n",
    "import nltk \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import recall_score, precision_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "64N0Lc2Q_24d"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "DRUs91yU-rMT"
   },
   "outputs": [],
   "source": [
    "#Reading the csv file using pandas\n",
    "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/processed_flipkart_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "PCLGqmnIFRgK"
   },
   "outputs": [],
   "source": [
    "df['description'] = df['description'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bzXzniGLFgqj",
    "outputId": "de1c0a16-d3c2-43f6-df45-6e440363cccd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Automotive': 9,\n",
       " 'Baby Care': 12,\n",
       " 'Bags, Wallets & Belts': 7,\n",
       " 'Beauty and Personal Care': 6,\n",
       " 'Cameras & Accessories': 20,\n",
       " 'Clothing': 0,\n",
       " 'Computers': 19,\n",
       " 'Eyewear': 26,\n",
       " 'Footwear': 2,\n",
       " 'Furniture': 1,\n",
       " 'Gaming': 22,\n",
       " 'Health & Personal Care Appliances': 21,\n",
       " 'Home & Kitchen': 18,\n",
       " 'Home Decor & Festive Needs': 8,\n",
       " 'Home Entertainment': 25,\n",
       " 'Home Furnishing': 11,\n",
       " 'Home Improvement': 23,\n",
       " 'Jewellery': 16,\n",
       " 'Kitchen & Dining': 17,\n",
       " 'Mobiles & Accessories': 13,\n",
       " 'Pens & Stationery': 4,\n",
       " 'Pet Supplies': 3,\n",
       " 'Sports & Fitness': 5,\n",
       " 'Sunglasses': 24,\n",
       " 'Tools & Hardware': 10,\n",
       " 'Toys & School Supplies': 15,\n",
       " 'Watches': 14,\n",
       " 'eBooks': 27}"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "label_dict = {}\n",
    "for index, possible_label in enumerate(df.product_category_tree.unique()):\n",
    "    label_dict[possible_label] = index\n",
    "label_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GRq_qS1wAGJT",
    "outputId": "7cf5663e-5eb5-4338-840f-a5954b9db801"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/81/91/61d69d58a1af1bd81d9ca9d62c90a6de3ab80d77f27c5df65d9a2c1f5626/transformers-4.5.0-py3-none-any.whl (2.1MB)\n",
      "\u001b[K     |████████████████████████████████| 2.2MB 4.3MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
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      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
      "Collecting tokenizers<0.11,>=0.10.1\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
      "\u001b[K     |████████████████████████████████| 3.3MB 34.8MB/s \n",
      "\u001b[?25hCollecting sacremoses\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/cd/342e584ee544d044fb573ae697404ce22ede086c9e87ce5960772084cad0/sacremoses-0.0.44.tar.gz (862kB)\n",
      "\u001b[K     |████████████████████████████████| 870kB 34.9MB/s \n",
      "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
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      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
      "Building wheels for collected packages: sacremoses\n",
      "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for sacremoses: filename=sacremoses-0.0.44-cp37-none-any.whl size=886084 sha256=a54af47be453c7dbc0958ec57d723afee7f2aa27280fb3f5c6bc1189ed71a49a\n",
      "  Stored in directory: /root/.cache/pip/wheels/3e/fb/c0/13ab4d63d537658f448366744654323077c4d90069b6512f3c\n",
      "Successfully built sacremoses\n",
      "Installing collected packages: tokenizers, sacremoses, transformers\n",
      "Successfully installed sacremoses-0.0.44 tokenizers-0.10.2 transformers-4.5.0\n"
     ]
    }
   ],
   "source": [
    "!pip3 install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "VYVMRJ56-stC"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from transformers import BertTokenizer\n",
    "from torch.utils.data import TensorDataset\n",
    "\n",
    "from transformers import BertForSequenceClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "pJ1_fC2XAlD0"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "frwdg-_4-uiC"
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(df.index.values,\n",
    "                                                  df.label.values, \n",
    "                                                  test_size=0.25, \n",
    "                                                  random_state=42, \n",
    "                                                  stratify=df.label.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "HITkEEp5_eJ7"
   },
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', \n",
    "                                          do_lower_case=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "BcDnC3ku_e7D"
   },
   "outputs": [],
   "source": [
    "df['data_type'] = ['not_set']*df.shape[0]\n",
    "\n",
    "df.loc[X_train, 'data_type'] = 'train'\n",
    "df.loc[X_val, 'data_type'] = 'val'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XPiW5FZLDPNn",
    "outputId": "07287e78-235c-46a1-92b1-786fb5ea2195"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['description'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hIMWAReWAsWh",
    "outputId": "34ada946-3ed1-4d0c-b6d2-00a0449cc670"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: 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",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "encoded_data_train = tokenizer.batch_encode_plus(\n",
    "    df[df.data_type=='train'].description.values, \n",
    "    add_special_tokens=True, \n",
    "    return_attention_mask=True, \n",
    "    pad_to_max_length=True, \n",
    "    max_length=128, \n",
    "    return_tensors='pt',\n",
    "    truncation = True\n",
    ")\n",
    "\n",
    "encoded_data_val = tokenizer.batch_encode_plus(\n",
    "    df[df.data_type=='val'].description.values, \n",
    "    add_special_tokens=True, \n",
    "    return_attention_mask=True, \n",
    "    pad_to_max_length=True, \n",
    "    max_length=128, \n",
    "    return_tensors='pt',\n",
    "    truncation = True\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "hT92M5H_AyQG"
   },
   "outputs": [],
   "source": [
    "\n",
    "dataset_train = TensorDataset(input_ids_train, attention_masks_train, labels_train)\n",
    "dataset_val = TensorDataset(input_ids_val, attention_masks_val, labels_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QUHGT4o1ERmh",
    "outputId": "46803604-3f8d-4fdb-a84f-6b53908c42aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14747, 4916)"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset_train), len(dataset_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228,
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    "outputId": "b7e3862d-9f8e-442d-ef39-c11c641cd8c2"
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   "outputs": [
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "78d9eb7ad9f3420eb182cfb20b9d465a",
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    {
     "name": "stdout",
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     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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 = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
    "                                                      num_labels=len(df.label.unique()),\n",
    "                                                      output_attentions=False,\n",
    "                                                      output_hidden_states=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "a3ALFf7UF4CI"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
    "\n",
    "batch_size = 3\n",
    "\n",
    "dataloader_train = DataLoader(dataset_train, \n",
    "                              sampler=RandomSampler(dataset_train), \n",
    "                              batch_size=batch_size)\n",
    "\n",
    "dataloader_validation = DataLoader(dataset_val, \n",
    "                                   sampler=SequentialSampler(dataset_val), \n",
    "                                   batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "XvWKSOvXGITV"
   },
   "outputs": [],
   "source": [
    "from transformers import AdamW, get_linear_schedule_with_warmup\n",
    "\n",
    "optimizer = AdamW(model.parameters(),\n",
    "                  lr=1e-5, \n",
    "                  eps=1e-8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "n-0W06X9GKur"
   },
   "outputs": [],
   "source": [
    "epochs = 5\n",
    "\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
    "                                            num_warmup_steps=0,\n",
    "                                            num_training_steps=len(dataloader_train)*epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "mAFaf_QlGM8B"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "def f1_score_func(preds, labels):\n",
    "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    return f1_score(labels_flat, preds_flat, average='weighted')\n",
    "\n",
    "def accuracy_per_class(preds, labels):\n",
    "    label_dict_inverse = {v: k for k, v in label_dict.items()}\n",
    "    \n",
    "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\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",
    "        print(f'Class: {label_dict_inverse[label]}')\n",
    "        print(f'Accuracy: {len(y_preds[y_preds==label])}/{len(y_true)}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "LWhuL6X5GPI2"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "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": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NJu4Wqw1GRVC",
    "outputId": "ad37bade-e6e6-4987-cae4-c8bc949fb2b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "IQXHBiDhGbXh"
   },
   "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",
    "    return loss_val_avg, predictions, true_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522,
     "referenced_widgets": [
      "839f7cda4ed4463cbdd9aaa6010a1128",
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    },
    "id": "1YzYneAjGerA",
    "outputId": "e2d03a84-f57a-4e21-e8ef-656f13a179f3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "839f7cda4ed4463cbdd9aaa6010a1128",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7b6f76b5fe74bccadfed26bc959f117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 1', max=4916.0, style=ProgressStyle(description_wid…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\r\n",
      "Epoch 1\n",
      "Training loss: 0.15058754250590764\n",
      "Validation loss: 0.15042813984087697\n",
      "F1 Score (Weighted): 0.9719002349557728\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1e8d96e89c54631adcd5a92b4e24967",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 2', max=4916.0, style=ProgressStyle(description_wid…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\r\n",
      "Epoch 2\n",
      "Training loss: 0.08823945847886318\n",
      "Validation loss: 0.13428537265497384\n",
      "F1 Score (Weighted): 0.9760727932229382\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90044433877c4290aa48e6ed2562970b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 3', max=4916.0, style=ProgressStyle(description_wid…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\r\n",
      "Epoch 3\n",
      "Training loss: 0.05333085670582552\n",
      "Validation loss: 0.13184611081840705\n",
      "F1 Score (Weighted): 0.9768779168500414\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef9ec3c2f6ed4e8caa735f7d999e7bf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 4', max=4916.0, style=ProgressStyle(description_wid…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\r\n",
      "Epoch 4\n",
      "Training loss: 0.02978689303113061\n",
      "Validation loss: 0.12152416960941113\n",
      "F1 Score (Weighted): 0.9806107952935434\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b108dfa1d53646648f2e02dbf039d7a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 5', max=4916.0, style=ProgressStyle(description_wid…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\r\n",
      "Epoch 5\n",
      "Training loss: 0.021603909590160668\n",
      "Validation loss: 0.12152416960941113\n",
      "F1 Score (Weighted): 0.9806107952935434\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in tqdm(range(1, epochs+1)):\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    loss_train_total = 0\n",
    "\n",
    "    progress_bar = tqdm(dataloader_train, desc='Epoch {:1d}'.format(epoch), leave=False, disable=False)\n",
    "    for batch in progress_bar:\n",
    "\n",
    "        model.zero_grad()\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",
    "        outputs = model(**inputs)\n",
    "        \n",
    "        loss = outputs[0]\n",
    "        loss_train_total += loss.item()\n",
    "        loss.backward()\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",
    "        \n",
    "    torch.save(model.state_dict(), f'/content/drive/MyDrive/Colab Notebooks/save_mid/finetuned_BERT_epoch_{epoch}.model')\n",
    "        \n",
    "    tqdm.write(f'\\nEpoch {epoch}')\n",
    "    \n",
    "    loss_train_avg = loss_train_total/len(dataloader_train)            \n",
    "    tqdm.write(f'Training loss: {loss_train_avg}')\n",
    "    \n",
    "    val_loss, predictions, true_vals = evaluate(dataloader_validation)\n",
    "    val_f1 = f1_score_func(predictions, true_vals)\n",
    "    tqdm.write(f'Validation loss: {val_loss}')\n",
    "    tqdm.write(f'F1 Score (Weighted): {val_f1}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q3si0y3YGpxZ",
    "outputId": "27697a8a-a582-45c5-eb38-aa300efeaf5f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=28, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
    "                                                      num_labels=len(df.label.unique()),\n",
    "                                                      output_attentions=False,\n",
    "                                                      output_hidden_states=False)\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "woZ1Zv5bxNoc",
    "outputId": "10f88507-5c7c-4882-9e91-26a51b71fad5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('/content/drive/MyDrive/Colab Notebooks/save_mid/finetuned_BERT_epoch_1.model', map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "PODBMdy3pmhB"
   },
   "outputs": [],
   "source": [
    "_, predictions, true_vals = evaluate(dataloader_validation)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ia9rX4F2p8Pi",
    "outputId": "f9a43e13-a83a-4330-d96b-b6f5404c5243"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class: Clothing\n",
      "Accuracy: 252/253\n",
      "\n",
      "Class: Furniture\n",
      "Accuracy: 109/121\n",
      "\n",
      "Class: Footwear\n",
      "Accuracy: 64/66\n",
      "\n",
      "Class: Pet Supplies\n",
      "Accuracy: 177/178\n",
      "\n",
      "Class: Pens & Stationery\n",
      "Accuracy: 20/20\n",
      "\n",
      "Class: Sports & Fitness\n",
      "Accuracy: 1540/1550\n",
      "\n",
      "Class: Beauty and Personal Care\n",
      "Accuracy: 139/145\n",
      "\n",
      "Class: Bags, Wallets & Belts\n",
      "Accuracy: 0/2\n",
      "\n",
      "Class: Home Decor & Festive Needs\n",
      "Accuracy: 307/307\n",
      "\n",
      "Class: Automotive\n",
      "Accuracy: 40/45\n",
      "\n",
      "Class: Tools & Hardware\n",
      "Accuracy: 6/9\n",
      "\n",
      "Class: Home Furnishing\n",
      "Accuracy: 9/11\n",
      "\n",
      "Class: Baby Care\n",
      "Accuracy: 0/6\n",
      "\n",
      "Class: Mobiles & Accessories\n",
      "Accuracy: 227/232\n",
      "\n",
      "Class: Watches\n",
      "Accuracy: 0/5\n",
      "\n",
      "Class: Toys & School Supplies\n",
      "Accuracy: 173/175\n",
      "\n",
      "Class: Jewellery\n",
      "Accuracy: 17/20\n",
      "\n",
      "Class: Kitchen & Dining\n",
      "Accuracy: 882/883\n",
      "\n",
      "Class: Home & Kitchen\n",
      "Accuracy: 158/162\n",
      "\n",
      "Class: Computers\n",
      "Accuracy: 266/275\n",
      "\n",
      "Class: Cameras & Accessories\n",
      "Accuracy: 45/78\n",
      "\n",
      "Class: Health & Personal Care Appliances\n",
      "Accuracy: 7/7\n",
      "\n",
      "Class: Gaming\n",
      "Accuracy: 36/41\n",
      "\n",
      "Class: Home Improvement\n",
      "Accuracy: 9/9\n",
      "\n",
      "Class: Sunglasses\n",
      "Accuracy: 94/98\n",
      "\n",
      "Class: Home Entertainment\n",
      "Accuracy: 78/82\n",
      "\n",
      "Class: Eyewear\n",
      "Accuracy: 131/132\n",
      "\n",
      "Class: eBooks\n",
      "Accuracy: 1/4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "accuracy_per_class(predictions, true_vals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JD1NEIIjqgWf"
   },
   "outputs": [],
   "source": []
  }
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