Bert_QNA_fine_tune.ipynb

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    "# Fine-Tuning BERT for Sentence-Pair Classification\n",
    "\n",
    "This notebook demonstrates how to fine-tune BERT-based models for the sentence-pair classification task. Utilizing the Hugging Face transformers and datasets libraries, this TensorFlow implementation enables seamless research experiments and evaluation metrics.\n",
    "\n",
    "The sentence-pair classification task is a vital component of the semantic textual similarity/duplicate problem. It involves two pairs of questions, and the goal is to model the textual interaction between them.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "The dataset used in this notebook is the Quora Question Pair (QQP) dataset, which is a part of the GLUE benchmark. The dataset consists of pairs of questions, and the objective is to predict whether one sentence is the paraphrase of the other. The evaluation metrics for this task are F1 score and accuracy.\n",
    "\n",
    "## Main Features\n",
    "\n",
    "This tutorial offers the following main features:\n",
    "\n",
    "1. End-to-end ML implementation covering training, validation, prediction, and evaluation.\n",
    "2. Capability to utilize custom datasets for fine-tuning.\n",
    "3. Facilitation of quick experiments with other BERT-based models such as BERT, ALBERT, and more.\n",
    "4. A comparison between the fine-tuned model and the model without fine-tuning."
   ]
  },
  {
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     "text": [
      "2023-07-19 08:06:21.151579: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:21.169468: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:21.169534: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:21.775059: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:21.775127: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:21.775134: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-07-19 08:06:21.775167: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:21.775195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /device:GPU:0 with 5402 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/device:GPU:0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking tenserflow gpu\n",
    "import tensorflow as tf\n",
    "tf.test.gpu_device_name()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "dca6ad44",
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     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking pytorch gpu\n",
    "import torch\n",
    "torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
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   "source": [
    "# Importing libraries\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, DataCollatorWithPadding\n",
    "from datasets import load_dataset\n",
    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from datasets import Dataset, DatasetDict\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, recall_score, precision_score\n",
    "plt.style.use('ggplot')\n",
    "# os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\""
   ]
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       "(404290, 6)"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# Reading the data\n",
    "train_df = pd.read_csv(\"train.csv.zip\")\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0679431",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
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       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
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       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# Printing the first 5 rows\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404290 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            404290 non-null  int64 \n",
      " 1   qid1          404290 non-null  int64 \n",
      " 2   qid2          404290 non-null  int64 \n",
      " 3   question1     404289 non-null  object\n",
      " 4   question2     404288 non-null  object\n",
      " 5   is_duplicate  404290 non-null  int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Information about dataset\n",
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1de50cce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              0\n",
       "qid1            0\n",
       "qid2            0\n",
       "question1       1\n",
       "question2       2\n",
       "is_duplicate    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# checking for null values\n",
    "train_df.isnull().sum()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "cbb9acce",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 404287 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            404287 non-null  int64 \n",
      " 1   qid1          404287 non-null  int64 \n",
      " 2   qid2          404287 non-null  int64 \n",
      " 3   question1     404287 non-null  object\n",
      " 4   question2     404287 non-null  object\n",
      " 5   is_duplicate  404287 non-null  int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 21.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Removing null values\n",
    "train_df.dropna(inplace=True)\n",
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39f26fc1",
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       "      <th></th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88485</th>\n",
       "      <td>For the CAT preparation, which is the best ins...</td>\n",
       "      <td>What is the best coaching institute for CAT pr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111700</th>\n",
       "      <td>How do I start up a new cafe?</td>\n",
       "      <td>What's the best strategy to start up a cafe?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126774</th>\n",
       "      <td>Why do we use abbreviations?</td>\n",
       "      <td>Why do we like to use abbreviations so much?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194997</th>\n",
       "      <td>Is Hillary Clinton crooked?</td>\n",
       "      <td>Why is Hillary Clinton so corrupt?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152810</th>\n",
       "      <td>How many times in a day a women need sex?</td>\n",
       "      <td>How many times a woman have sex in a day?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                question1  \\\n",
       "88485   For the CAT preparation, which is the best ins...   \n",
       "111700                      How do I start up a new cafe?   \n",
       "126774                       Why do we use abbreviations?   \n",
       "194997                        Is Hillary Clinton crooked?   \n",
       "152810          How many times in a day a women need sex?   \n",
       "\n",
       "                                                question2  is_duplicate  \n",
       "88485   What is the best coaching institute for CAT pr...             1  \n",
       "111700       What's the best strategy to start up a cafe?             1  \n",
       "126774       Why do we like to use abbreviations so much?             1  \n",
       "194997                 Why is Hillary Clinton so corrupt?             1  \n",
       "152810          How many times a woman have sex in a day?             1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing unnecessary columns\n",
    "train_df.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)\n",
    "train_df.sample(5)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "### Converting data to HuggingFace Dataset"
   ]
  },
  {
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   "execution_count": 10,
   "id": "f0c251f6",
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   "source": [
    "# Splitting the data into train and validation set\n",
    "train_data, test_val_data = train_test_split(train_df, test_size=0.4, random_state=100, shuffle=True)\n",
    "val_data, test_data = train_test_split(test_val_data, test_size=0.5, random_state=100, shuffle=True)\n",
    "train_data.to_csv('train_clean.csv',index=False)\n",
    "val_data.to_csv('val_clean.csv',index=False)\n",
    "test_data.to_csv('test_clean.csv',index=False)"
   ]
  },
  {
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   "execution_count": 11,
   "id": "ad8a8083",
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-ee7343318a037f09/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d...\n"
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    },
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      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
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      "text/plain": [
       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
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    {
     "data": {
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
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      "text/plain": [
       "Generating valid split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-ee7343318a037f09/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9a2da5991764c05b0e43ea275a70d74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['question1', 'question2', 'is_duplicate'],\n",
       "        num_rows: 242572\n",
       "    })\n",
       "    valid: Dataset({\n",
       "        features: ['question1', 'question2', 'is_duplicate'],\n",
       "        num_rows: 80857\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the data into Hugging Face Dataset format\n",
    "dataset = load_dataset('csv', data_files={'train': 'train_clean.csv','valid':'val_clean.csv',})\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "128cdbe3",
   "metadata": {
    "papermill": {
     "duration": 0.02403,
     "end_time": "2023-03-22T12:40:15.771271",
     "exception": false,
     "start_time": "2023-03-22T12:40:15.747241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Model set and batch size\n",
    "model_checkpoint = \"distilbert-base-uncased\"\n",
    "batch_size = 56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b73f2a56",
   "metadata": {
    "papermill": {
     "duration": 4.374326,
     "end_time": "2023-03-22T12:40:20.161547",
     "exception": false,
     "start_time": "2023-03-22T12:40:15.787221",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initializing the tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85997c74",
   "metadata": {
    "papermill": {
     "duration": 0.016371,
     "end_time": "2023-03-22T12:40:20.195111",
     "exception": false,
     "start_time": "2023-03-22T12:40:20.178740",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Testing Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74e9a086",
   "metadata": {
    "papermill": {
     "duration": 2.469942,
     "end_time": "2023-03-22T12:40:22.681624",
     "exception": false,
     "start_time": "2023-03-22T12:40:20.211682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-19 08:06:27.600734: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:27.600840: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:27.600876: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:27.601141: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:27.601174: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:27.601195: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:28.369232: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:28.369258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-07-19 08:06:28.369298: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:967] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-19 08:06:28.369320: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5402 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input_ids': <tf.Tensor: shape=(1, 128), dtype=int32, numpy=\n",
       "array([[  101,  2054,  2003,  1996,  3357,  2011,  3357,  5009,  2000,\n",
       "        15697,  1999,  3745,  3006,  1999,  2634,  1029,   102,  2054,\n",
       "         2003,  1996,  3357,  2011,  3357,  5009,  2000, 15697,  1999,\n",
       "         3745,  3006,  1029,   102,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "            0,     0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(1, 128), dtype=int32, numpy=\n",
       "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
       "      dtype=int32)>}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running tokenizer on sample data\n",
    "tokenizer(train_df.question1[0], train_df.question2[0],padding='max_length', truncation=True, max_length=128, return_tensors='tf')  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b420b578",
   "metadata": {
    "papermill": {
     "duration": 0.016387,
     "end_time": "2023-03-22T12:40:22.714710",
     "exception": false,
     "start_time": "2023-03-22T12:40:22.698323",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Tokenizer for data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ae6346f",
   "metadata": {
    "papermill": {
     "duration": 0.024342,
     "end_time": "2023-03-22T12:40:22.755453",
     "exception": false,
     "start_time": "2023-03-22T12:40:22.731111",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# function to tokenize the data\n",
    "def preprocess_function(records):\n",
    "    return tokenizer(records['question1'], records['question2'], truncation=True, max_length = 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a1a3006",
   "metadata": {
    "papermill": {
     "duration": 47.368265,
     "end_time": "2023-03-22T12:41:10.226360",
     "exception": false,
     "start_time": "2023-03-22T12:40:22.858095",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/242572 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/80857 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Running tokenizer on dataset\n",
    "encoded_dataset = dataset.map(preprocess_function, batched=True, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ea30858",
   "metadata": {
    "papermill": {
     "duration": 0.025921,
     "end_time": "2023-03-22T12:41:10.269590",
     "exception": false,
     "start_time": "2023-03-22T12:41:10.243669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['question1', 'question2', 'is_duplicate', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 242572\n",
       "    })\n",
       "    valid: Dataset({\n",
       "        features: ['question1', 'question2', 'is_duplicate', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 80857\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocessed dataset\n",
    "encoded_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6faf7c5a",
   "metadata": {
    "papermill": {
     "duration": 0.025354,
     "end_time": "2023-03-22T12:41:10.311685",
     "exception": false,
     "start_time": "2023-03-22T12:41:10.286331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['input_ids', 'attention_mask']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns added by tokenizer\n",
    "col_tokenizer = encoded_dataset.column_names['train'][3:]\n",
    "col_tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87cac0e3",
   "metadata": {
    "papermill": {
     "duration": 0.016706,
     "end_time": "2023-03-22T12:41:10.345273",
     "exception": false,
     "start_time": "2023-03-22T12:41:10.328567",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Data padding and batching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbb8e5ad",
   "metadata": {
    "papermill": {
     "duration": 2.315657,
     "end_time": "2023-03-22T12:41:12.677847",
     "exception": false,
     "start_time": "2023-03-22T12:41:10.362190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adding Padding to the dataset\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c49253b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:385: FutureWarning: The output of `to_tf_dataset` will change when a passing single element list for `labels` or `columns` in the next datasets version. To return a tuple structure rather than dict, pass a single string.\n",
      "Old behaviour: columns=['a'], labels=['labels'] -> (tf.Tensor, tf.Tensor)  \n",
      "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor)  \n",
      "New behaviour: columns=['a'],labels=['labels'] -> ({'a': tf.Tensor}, {'labels': tf.Tensor})  \n",
      "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n",
      "  warnings.warn(\n",
      "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    }
   ],
   "source": [
    "# Making the dataset batched and shuffled\n",
    "tf_train_dataset = encoded_dataset[\"train\"].to_tf_dataset(\n",
    "    columns=col_tokenizer,\n",
    "    label_cols=[\"is_duplicate\"],\n",
    "    shuffle=True,\n",
    "    batch_size=batch_size,\n",
    "    collate_fn=data_collator,\n",
    ")\n",
    "tf_validation_dataset = encoded_dataset[\"valid\"].to_tf_dataset(\n",
    "    columns=col_tokenizer,\n",
    "    label_cols=[\"is_duplicate\"],\n",
    "    shuffle=True,\n",
    "    batch_size=batch_size,\n",
    "    collate_fn=data_collator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e7a43a5",
   "metadata": {
    "papermill": {
     "duration": 0.495229,
     "end_time": "2023-03-22T12:41:13.190489",
     "exception": false,
     "start_time": "2023-03-22T12:41:12.695260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'input_ids': <tf.Tensor: shape=(56, 83), dtype=int64, numpy=\n",
       "  array([[ 101, 2129, 2116, ...,    0,    0,    0],\n",
       "         [ 101, 2054, 2003, ...,    0,    0,    0],\n",
       "         [ 101, 2429, 2000, ...,    0,    0,    0],\n",
       "         ...,\n",
       "         [ 101, 2040, 2003, ...,    0,    0,    0],\n",
       "         [ 101, 2054, 4084, ...,    0,    0,    0],\n",
       "         [ 101, 2054, 1005, ...,    0,    0,    0]])>,\n",
       "  'attention_mask': <tf.Tensor: shape=(56, 83), dtype=int64, numpy=\n",
       "  array([[1, 1, 1, ..., 0, 0, 0],\n",
       "         [1, 1, 1, ..., 0, 0, 0],\n",
       "         [1, 1, 1, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [1, 1, 1, ..., 0, 0, 0],\n",
       "         [1, 1, 1, ..., 0, 0, 0],\n",
       "         [1, 1, 1, ..., 0, 0, 0]])>},\n",
       " <tf.Tensor: shape=(56,), dtype=int64, numpy=\n",
       " array([1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "        0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "        0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0])>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample train batch\n",
    "sample_batch=next(iter(tf_train_dataset))\n",
    "sample_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "db573a18",
   "metadata": {
    "papermill": {
     "duration": 2.395507,
     "end_time": "2023-03-22T12:41:15.606024",
     "exception": false,
     "start_time": "2023-03-22T12:41:13.210517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[CLS] how many businesses does warren buffett own? [SEP] what companies does warren buffett own? [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample decoded string\n",
    "tokenizer.decode(sample_batch[0]['input_ids'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78009df5",
   "metadata": {
    "papermill": {
     "duration": 0.030939,
     "end_time": "2023-03-22T12:41:15.654563",
     "exception": false,
     "start_time": "2023-03-22T12:41:15.623624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    63.08\n",
       "1    36.92\n",
       "Name: is_duplicate, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of classes in dataset\n",
    "num_classes = len(train_df.is_duplicate.unique())\n",
    "round(train_df.is_duplicate.value_counts(normalize=True)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0203d5b0",
   "metadata": {
    "papermill": {
     "duration": 0.017139,
     "end_time": "2023-03-22T12:41:15.688845",
     "exception": false,
     "start_time": "2023-03-22T12:41:15.671706",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Loading the model checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5670fe5a",
   "metadata": {
    "papermill": {
     "duration": 5.830236,
     "end_time": "2023-03-22T12:41:21.536138",
     "exception": false,
     "start_time": "2023-03-22T12:41:15.705902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-19 08:06:37.542752: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:630] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_transform.weight']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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": [
    "# Downloading the model\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels = num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb91d448",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-22T07:35:05.056559Z",
     "iopub.status.busy": "2023-03-22T07:35:05.056154Z",
     "iopub.status.idle": "2023-03-22T07:35:05.063517Z",
     "shell.execute_reply": "2023-03-22T07:35:05.061678Z",
     "shell.execute_reply.started": "2023-03-22T07:35:05.056514Z"
    },
    "papermill": {
     "duration": 0.017122,
     "end_time": "2023-03-22T12:41:21.571011",
     "exception": false,
     "start_time": "2023-03-22T12:41:21.553889",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Defining the parameters, optimizer and loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0fbbfab7",
   "metadata": {
    "papermill": {
     "duration": 0.042511,
     "end_time": "2023-03-22T12:41:21.630799",
     "exception": false,
     "start_time": "2023-03-22T12:41:21.588288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setting epochs and learning rate\n",
    "num_epochs = 3\n",
    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
    "lr_scheduler = PolynomialDecay(\n",
    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps, power = 2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4a03cdcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizer and loss function\n",
    "opt = Adam(learning_rate=lr_scheduler)\n",
    "loss = SparseCategoricalCrossentropy(from_logits=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad78cdb",
   "metadata": {
    "papermill": {
     "duration": 0.017141,
     "end_time": "2023-03-22T12:41:21.665534",
     "exception": false,
     "start_time": "2023-03-22T12:41:21.648393",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Plotting the learning rate decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "05399aee",
   "metadata": {
    "papermill": {
     "duration": 0.272147,
     "end_time": "2023-03-22T12:41:21.954985",
     "exception": false,
     "start_time": "2023-03-22T12:41:21.682838",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the learning rate\n",
    "plt.plot([lr_scheduler(step).numpy() for step in range(num_train_steps)])\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Learning rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce75871f",
   "metadata": {
    "papermill": {
     "duration": 0.037225,
     "end_time": "2023-03-22T12:41:22.010347",
     "exception": false,
     "start_time": "2023-03-22T12:41:21.973122",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "81648e60",
   "metadata": {
    "papermill": {
     "duration": 9035.410545,
     "end_time": "2023-03-22T15:11:57.438962",
     "exception": false,
     "start_time": "2023-03-22T12:41:22.028417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "WARNING:tensorflow:From /usr/lib/python3/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-19 08:07:07.225876: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x555bbfdab390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-07-19 08:07:07.225935: I tensorflow/compiler/xla/service/service.cc:181]   StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n",
      "2023-07-19 08:07:07.233988: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-07-19 08:07:07.334465: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4332/4332 [==============================] - 896s 204ms/step - loss: 0.3277 - accuracy: 0.8497 - val_loss: 0.2642 - val_accuracy: 0.8849\n",
      "Epoch 2/3\n",
      "4332/4332 [==============================] - 877s 202ms/step - loss: 0.2016 - accuracy: 0.9165 - val_loss: 0.2549 - val_accuracy: 0.8929\n",
      "Epoch 3/3\n",
      "4332/4332 [==============================] - 881s 203ms/step - loss: 0.1388 - accuracy: 0.9456 - val_loss: 0.2843 - val_accuracy: 0.8938\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "history=model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9e0d29ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the accuracy, training and validation loss \n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy/Loss\")\n",
    "plt.legend(['train_acc', 'val_acc', 'train_loss', 'val_loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73640b80",
   "metadata": {
    "papermill": {
     "duration": 3.278189,
     "end_time": "2023-03-22T15:12:02.366449",
     "exception": false,
     "start_time": "2023-03-22T15:11:59.088260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Saving the model\n",
    "model.save_pretrained(f\"saved_model_epoch{num_epochs}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cad42ce0",
   "metadata": {
    "papermill": {
     "duration": 2.963285,
     "end_time": "2023-03-22T15:12:07.041072",
     "exception": false,
     "start_time": "2023-03-22T15:12:04.077787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some layers from the model checkpoint at saved_model_epoch3 were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at saved_model_epoch3 and are newly initialized: ['dropout_39']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# Loading the Last saved model\n",
    "trained_model = TFAutoModelForSequenceClassification.from_pretrained(f'saved_model_epoch{num_epochs}',num_labels = num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fc6a5828",
   "metadata": {
    "papermill": {
     "duration": 1.726847,
     "end_time": "2023-03-22T15:12:10.667052",
     "exception": false,
     "start_time": "2023-03-22T15:12:08.940205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Function to check similarity\n",
    "def check_similarity(question1, question2, debug = 0):\n",
    "  tokenizer_output = tokenizer(question1, question2, truncation=True, max_length = 128, return_tensors = 'tf')\n",
    "  logits = trained_model(**tokenizer_output)[\"logits\"]\n",
    "  predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
    "  if predicted_class_id == 1:\n",
    "    if(debug):\n",
    "        print(\"Both questions mean the same\")\n",
    "    return 1\n",
    "  else:\n",
    "    if(debug):\n",
    "        print(\"Both the questions are different.\")\n",
    "    return 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ffa243fd",
   "metadata": {
    "papermill": {
     "duration": 1.876603,
     "end_time": "2023-03-22T15:12:14.168552",
     "exception": false,
     "start_time": "2023-03-22T15:12:12.291949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both the questions are different.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking similarity between two questions\n",
    "check_similarity(\"Why are people so obsessed with cricket?\", \"Why are people so obsessed with football?\", 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f53e8bd",
   "metadata": {
    "papermill": {
     "duration": 1.912185,
     "end_time": "2023-03-22T15:12:17.807095",
     "exception": false,
     "start_time": "2023-03-22T15:12:15.894910",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both questions mean the same\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking similarity between two questions\n",
    "check_similarity(\"Why are people so obsessed with cricket?\", \"Why do people like cricket?\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c79450",
   "metadata": {},
   "source": [
    "### Testing the model on validation set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "99e46077-fac9-4d56-9c72-9911e9930075",
   "metadata": {},
   "outputs": [],
   "source": [
    "import swifter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d9a342cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15f2348184e9421d990574678bcc17c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pandas Apply:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking accuracy on test data\n",
    "val_df = pd.read_csv(\"val_clean.csv\").sample(1000)\n",
    "val_df['predicted'] = val_df.swifter.apply(lambda row: check_similarity(row['question1'], row['question2']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c58bf94e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Validation data is  88.9\n",
      "Precision on Validation data is  84.72\n",
      "Recall on Validation data is  85.41\n",
      "F1 score on Validation data is  85.06\n",
      "ROC AUC score on Validation data is  88.18\n"
     ]
    }
   ],
   "source": [
    "# checking metrics on test data\n",
    "val_df['predicted'] = val_df['predicted'].astype(int)\n",
    "val_df['is_duplicate'] = val_df['is_duplicate'].astype(int)\n",
    "print(\"Accuracy on Validation data is \",round(accuracy_score(val_df['is_duplicate'],val_df['predicted'])*100,2))\n",
    "print(\"Precision on Validation data is \",round(precision_score(val_df['is_duplicate'],val_df['predicted'])*100,2))\n",
    "print(\"Recall on Validation data is \",round(recall_score(val_df['is_duplicate'],val_df['predicted'])*100,2))\n",
    "print(\"F1 score on Validation data is \",round(f1_score(val_df['is_duplicate'],val_df['predicted'])*100,2))\n",
    "print(\"ROC AUC score on Validation data is \",round(roc_auc_score(val_df['is_duplicate'],val_df['predicted'])*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "95b7e011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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   "source": [
    "# checking accuracy on test data\n",
    "test_df = pd.read_csv(\"test_clean.csv\").sample(1000)\n",
    "test_df['predicted'] = test_df.swifter.apply(lambda row: check_similarity(row['question1'], row['question2']), axis=1)"
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  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f3df3487",
   "metadata": {},
   "outputs": [
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     "text": [
      "Accuracy on test data is  89.2\n",
      "Precision on test data is  84.56\n",
      "Recall on test data is  87.66\n",
      "F1 score on test data is  86.08\n",
      "ROC AUC score on test data is  88.9\n"
     ]
    }
   ],
   "source": [
    "# checking metrics on test data\n",
    "test_df['predicted'] = test_df['predicted'].astype(int)\n",
    "test_df['is_duplicate'] = test_df['is_duplicate'].astype(int)\n",
    "print(\"Accuracy on test data is \",round(accuracy_score(test_df['is_duplicate'],test_df['predicted'])*100,2))\n",
    "print(\"Precision on test data is \",round(precision_score(test_df['is_duplicate'],test_df['predicted'])*100,2))\n",
    "print(\"Recall on test data is \",round(recall_score(test_df['is_duplicate'],test_df['predicted'])*100,2))\n",
    "print(\"F1 score on test data is \",round(f1_score(test_df['is_duplicate'],test_df['predicted'])*100,2))\n",
    "print(\"ROC AUC score on test data is \",round(roc_auc_score(test_df['is_duplicate'],test_df['predicted'])*100,2))"
   ]
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