contextualizedModels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4P4rY_M0Nl-T"
   },
   "source": [
    "# **Installing the packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 28865,
     "status": "ok",
     "timestamp": 1710257503925,
     "user": {
      "displayName": "Vageesh Saxena",
      "userId": "08383190130410030928"
     },
     "user_tz": -60
    },
    "id": "gdq--glAM2d1",
    "outputId": "6eed776f-d37e-479b-c664-949718ce2153"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Collecting git+https://github.com/osainz59/t5-encoder\n",
      "  Cloning https://github.com/osainz59/t5-encoder to /tmp/pip-req-build-r72er44w\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/osainz59/t5-encoder /tmp/pip-req-build-r72er44w\n",
      "  Resolved https://github.com/osainz59/t5-encoder to commit f7443943fb65426a34948254a8e43bde6b700982\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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     ]
    }
   ],
   "source": [
    "! pip install tqdm pandas numpy plotly scikit-learn matplotlib torch lightning transformers wandb datasets sentence-transformers\n",
    "! python -m pip install git+https://github.com/osainz59/t5-encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UkggqSr8Nvu0"
   },
   "source": [
    "# **Importing the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 57237,
     "status": "ok",
     "timestamp": 1710257561155,
     "user": {
      "displayName": "Vageesh Saxena",
      "userId": "08383190130410030928"
     },
     "user_tz": -60
    },
    "id": "fBIG2hmqNkpb",
    "outputId": "506be9e1-7dc4-4a40-a5a7-b5b2aa05990e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
       "            function loadScript(url) {\n",
       "            return new Promise(function(resolve, reject) {\n",
       "                let newScript = document.createElement(\"script\");\n",
       "                newScript.onerror = reject;\n",
       "                newScript.onload = resolve;\n",
       "                document.body.appendChild(newScript);\n",
       "                newScript.src = url;\n",
       "            });\n",
       "            }\n",
       "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
       "            const iframe = document.createElement('iframe')\n",
       "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
       "            document.body.appendChild(iframe)\n",
       "            const handshake = new Postmate({\n",
       "                container: iframe,\n",
       "                url: 'https://wandb.ai/authorize'\n",
       "            });\n",
       "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
       "            handshake.then(function(child) {\n",
       "                child.on('authorize', data => {\n",
       "                    clearTimeout(timeout)\n",
       "                    resolve(data)\n",
       "                });\n",
       "            });\n",
       "            })\n",
       "        });\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ··········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    }
   ],
   "source": [
    "# Mount the drive if not mounted\n",
    "from google.colab import drive\n",
    "drive.mount(\"/content/drive/\")\n",
    "\n",
    "import os\n",
    "import random\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import f1_score, balanced_accuracy_score, classification_report\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader, RandomSampler, random_split, TensorDataset\n",
    "\n",
    "from datasets import load_dataset\n",
    "\n",
    "from pytorch_lightning.loggers import WandbLogger\n",
    "from pytorch_lightning.utilities.deepspeed import convert_zero_checkpoint_to_fp32_state_dict\n",
    "\n",
    "import lightning as L\n",
    "import lightning.pytorch as pl\n",
    "from lightning.pytorch import Trainer, LightningModule, LightningDataModule, seed_everything\n",
    "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
    "from lightning.pytorch.callbacks import TQDMProgressBar\n",
    "# from lightning.pytorch.strategies import DeepSpeedStrategy\n",
    "from lightning.pytorch.plugins.precision import DeepSpeedPrecisionPlugin\n",
    "\n",
    "from transformers import AdamW, get_linear_schedule_with_warmup\n",
    "from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification\n",
    "\n",
    "# from deepspeed.ops.adam import DeepSpeedCPUAdam\n",
    "\n",
    "import t5_encoder\n",
    "\n",
    "import wandb\n",
    "wandb.login(relogin=True)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fHrvf3OYQmF9"
   },
   "source": [
    "# **Setting seed value for reproducibility**    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1710257561157,
     "user": {
      "displayName": "Vageesh Saxena",
      "userId": "08383190130410030928"
     },
     "user_tz": -60
    },
    "id": "DcFqU-iEQomT",
    "outputId": "707411c0-98d3-4991-88d9-72a3088023da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Seed set to 111\n",
      "INFO:lightning.fabric.utilities.seed:Seed set to 111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 111\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False\n",
    "np.random.seed(seed)\n",
    "random.seed(seed)\n",
    "os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n",
    "seed_everything(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e-cZbZg4QuAD"
   },
   "source": [
    "# **Creating DataModule**\n",
    "\n",
    "In PyTorch, a Dataset is a handy tool that lets us organize our data in an easy-to-use format. When we create a Dataset, we basically tell PyTorch how to get data and its corresponding label. This involves creating a class and defining two key methods: len and getitem. The len method tells PyTorch how many data samples we have, while the getitem method tells PyTorch how to get the n-th data sample.\n",
    "\n",
    "The DataModule provides a high-level abstraction over the data pipeline and allows us to encapsulate all the complex data procedures from data preparation, splitting, and processing, to creating PyTorch Dataset and DataLoader objects into a single class that can be easily shared, reused, and tested. This ensures that our data pipeline is robust and that our machine learning model can focus on learning from the data, not managing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GfEfMTC8PZhg"
   },
   "outputs": [],
   "source": [
    "class contextualizedClassifierDataModule(pl.LightningDataModule):\n",
    "    def __init__(self, tokenizer_name_or_path, batch_size=32):\n",
    "        super().__init__()\n",
    "\n",
    "        self.tokenizer_name_or_path = tokenizer_name_or_path\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        # Handling the padding token in distilgpt2 by substituting it with eos_token_id\n",
    "        if self.tokenizer_name_or_path == \"distilgpt2\":\n",
    "            self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name_or_path, use_fast=True)\n",
    "            self.tokenizer.pad_token = self.tokenizer.eos_token\n",
    "        else:\n",
    "            self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name_or_path, use_fast=True)\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        # Load the dataset into a pandas dataframe.\n",
    "        # Load the data from a CSV file\n",
    "        # Pre-processing data\n",
    "        df = pd.read_csv(\"/content/drive/MyDrive/AA-Tutorial/data/Agora.csv\", encoding='ISO-8859-1')\n",
    "        # Renaming all the features of the dataframe\n",
    "        df = df.rename(str.strip, axis='columns')\n",
    "        # Merging the Item and Item Description using a [SEP] token\n",
    "        separator = ' [SEP] '\n",
    "        df['TEXT'] = df.apply(lambda row: f\"{row['Item']}{separator}{row['Item Description']}\", axis=1)\n",
    "        # dropping Unncessary columns\n",
    "        df.drop(columns=[\"Item\", \"Item Description\", \"Category\", \"Price\", \"Origin\", \"Destination\", \"Rating\", \"Remarks\"], inplace=True)\n",
    "        # Assuming that vendors Amsterdam100 and amsterdam100 are the same vendors\n",
    "        df.Vendor = df.Vendor.apply(lambda x: x.lower())\n",
    "\n",
    "        # Due to the extensive time required to train on over 100K+ samples, we have decided to limit our analysis to a subset of 5K samples.\n",
    "        # To get these samples, we look into vendors that have 5+ advertisements and then allocate all the vendors that have less than 5 ads into a new class, \"others\".\n",
    "        df = df.iloc[:5000]\n",
    "        # Assigning a vendor ID to \"others\" class\n",
    "        # vendors_dict[\"others\"] = len(vendors_dict) + 1\n",
    "        # Calculate advertisement frequency for each vendor\n",
    "        ad_freq = df['Vendor'].value_counts()\n",
    "        # Filter vendors with ad frequency less than 5\n",
    "        vendors_to_replace = ad_freq[ad_freq < 5].index\n",
    "        # Update DataFrame: Replace vendor names with 'others' where ad frequency is less than 5\n",
    "        df['Vendor'] = df['Vendor'].apply(lambda x: 'others' if x in vendors_to_replace else x)\n",
    "\n",
    "        # Assigning vendor IDs to vendor handles using a dictionary comprehension.\n",
    "        # This approach eliminates the need for checking if a vendor already exists in the dictionary,\n",
    "        # as each unique vendor will be processed once. The enumerate function provides a counter (idx),\n",
    "        # which is used to assign IDs, starting from 1 for the first vendor.\n",
    "        vendors_dict = {vendor: idx for idx, vendor in enumerate(df.Vendor.unique())}\n",
    "\n",
    "        # Updating the 'Vendor' column in the DataFrame to reflect the vendor IDs.\n",
    "        # The 'map' function is used to replace each vendor handle with its corresponding vendor ID\n",
    "        # based on the 'vendor_to_idx_dict'. This operation is vectorized and efficient.\n",
    "        df['Vendor'] = df['Vendor'].map(vendors_dict)\n",
    "\n",
    "        text = df.TEXT.values.tolist()\n",
    "        vendors = df.Vendor.values.tolist()\n",
    "\n",
    "        # Tokenizing the data with padding and truncation\n",
    "        encodings = self.tokenizer(text, add_special_tokens=True, max_length=512, padding='max_length', return_token_type_ids=False, truncation=True,\n",
    "                                   return_attention_mask=True, return_tensors='pt')\n",
    "\n",
    "        # Convert the lists into tensors.\n",
    "        input_ids = encodings['input_ids']\n",
    "        attention_mask = encodings['attention_mask']\n",
    "        labels = torch.tensor(vendors)\n",
    "\n",
    "        # Combine the inputs into a TensorDataset.\n",
    "        dataset = TensorDataset(input_ids, attention_mask, labels)\n",
    "\n",
    "        # Getting an 0.75-0.05-0.20 split for training-val-test dataset\n",
    "        train_dataset, test_dataset = random_split(dataset, [0.8, 0.2], generator=torch.Generator().manual_seed(1111))\n",
    "        train_dataset, val_dataset = random_split(train_dataset, [0.95, 0.05], generator=torch.Generator().manual_seed(1111))\n",
    "\n",
    "        self.train_dataset = train_dataset\n",
    "        self.test_dataset = test_dataset\n",
    "        self.val_dataset = val_dataset\n",
    "\n",
    "    # Returning the pytorch-lightning default training DataLoader\n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(self.train_dataset, sampler=RandomSampler(self.train_dataset), batch_size=self.batch_size)\n",
    "\n",
    "    # Returning the pytorch-lightning default val DataLoader\n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(self.val_dataset, batch_size=self.batch_size)\n",
    "\n",
    "    # Returning the pytorch-lightning default test DataLoader\n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(self.test_dataset, batch_size=self.batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P2V1ogrAZZXH"
   },
   "source": [
    "# **Initializing Lightning Model Module**\n",
    "\n",
    "Once we’ve defined our datamodule, the next step is to set up the training process. This involves defining how the data is fed to the model, calculating the loss, updating the model’s weights, and then evaluating the model’s performance.\n",
    "\n",
    "While all these steps can be done using raw PyTorch, it can lead to repetition or even impose logical errors, especially when dealing with complex models and workflows. That is where PyTorch Lightning steps in, by providing the LightningModule class that encapsulates all aspects of the training logic into a single class similar to LightningDataModule. This class enables us to define the methods for the training step, validation step, test step, and configuring the optimizer while also abstracting away repetitive steps such as backward propagation and weight updates thus making our code cleaner and easier to understand. Furthermore, it makes it incredibly straightforward to experiment with different models and tune hyperparameters, thus enhancing the overall machine learning experimentation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YbOk3IftZZ0p"
   },
   "outputs": [],
   "source": [
    "class ClassifierModel(pl.LightningModule):\n",
    "    def __init__(self, learning_rate, adam_epsilon, weight_decay, model_name_or_path, num_classes, num_training_steps, warmup_steps):\n",
    "        super().__init__()\n",
    "\n",
    "        self.save_hyperparameters()\n",
    "        self.hparams.learning_rate = learning_rate\n",
    "        self.hparams.eps = adam_epsilon\n",
    "        self.hparams.weight_decay = weight_decay\n",
    "        self.hparams.model_name_or_path = model_name_or_path\n",
    "        self.hparams.num_classes = num_classes\n",
    "        self.hparams.num_training_steps = num_training_steps\n",
    "        self.hparams.warmup_steps = warmup_steps\n",
    "\n",
    "        # freeze\n",
    "        self._frozen = False\n",
    "\n",
    "        # Handling the padding token in distilgpt2 by substituting it with eos_token_id\n",
    "        if self.hparams.model_name_or_path == \"distilgpt2\":\n",
    "            config = AutoConfig.from_pretrained(self.hparams.model_name_or_path, num_labels=self.hparams.num_classes, output_attentions=False, output_hidden_states=True)\n",
    "            self.model = AutoModelForSequenceClassification.from_pretrained(self.hparams.model_name_or_path, config=config)\n",
    "            self.model.config.pad_token_id = self.model.config.eos_token_id\n",
    "        else:\n",
    "            config = AutoConfig.from_pretrained(self.hparams.model_name_or_path, num_labels=self.hparams.num_classes, output_attentions=False, output_hidden_states=True)\n",
    "            self.model = AutoModelForSequenceClassification.from_pretrained(self.hparams.model_name_or_path, config=config)\n",
    "\n",
    "    def forward(self, batch):\n",
    "        # The batch contains the input_ids, the input_put_mask and the labels (for training)\n",
    "        input_ids = batch[0]\n",
    "        input_mask = batch[1]\n",
    "        labels = batch[2]\n",
    "\n",
    "        outputs = self.model(input_ids, attention_mask=input_mask, labels=labels)\n",
    "        loss = outputs[0]\n",
    "        logits = outputs[1]\n",
    "\n",
    "        return loss, logits\n",
    "\n",
    "    def training_step(self, batch, batch_nb):\n",
    "        # the training step is a (virtual) method,specified in the interface, that the pl.LightningModule\n",
    "        # class stipulates you to overwrite. This we do here, by virtue of this definition\n",
    "        outputs = self(batch)  # self refers to the model, which in turn acceses the forward method\n",
    "        train_loss = outputs[0]\n",
    "        self.log_dict({\"train_loss\": train_loss}, on_step=False, on_epoch=True, prog_bar=True, logger=True)\n",
    "        return train_loss\n",
    "        # the training_step method expects a dictionary, which should at least contain the loss\n",
    "\n",
    "    def validation_step(self, batch, batch_nb):\n",
    "        # the training step is a (virtual) method,specified in the interface, that the pl.LightningModule\n",
    "        # class  wants you to overwrite, in case you want to do validation. This we do here, by virtue of this definition.\n",
    "\n",
    "        outputs = self(batch)\n",
    "        # self refers to the model, which in turn accesses the forward method\n",
    "\n",
    "        # Apart from the validation loss, we also want to track validation accuracy  to get an idea, what the\n",
    "        # model training has achieved \"in real terms\".\n",
    "        val_loss = outputs[0]\n",
    "        logits = outputs[1]\n",
    "        labels = batch[2]\n",
    "\n",
    "        # Evaluating the performance\n",
    "        predictions = torch.argmax(logits, dim=1)\n",
    "        balanced_accuracy = balanced_accuracy_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), adjusted=True)\n",
    "        macro_accuracy = f1_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), average='macro')\n",
    "        micro_accuracy = f1_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), average='micro')\n",
    "        weighted_accuracy = f1_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), average='weighted')\n",
    "\n",
    "        self.log_dict({\"val_loss\": val_loss, 'accuracy': balanced_accuracy, 'macro-F1': macro_accuracy, 'micro-F1': micro_accuracy, 'weighted-F1':weighted_accuracy},\n",
    "                      on_step=False, on_epoch=True, prog_bar=True, logger=True)\n",
    "        return val_loss\n",
    "\n",
    "    def test_step(self, batch, batch_nb):\n",
    "        # the training step is a (virtual) method,specified in the interface, that the pl.LightningModule\n",
    "        # class  wants you to overwrite, in case you want to do test. This we do here, by virtue of this definition.\n",
    "\n",
    "        outputs = self(batch)\n",
    "        # self refers to the model, which in turn accesses the forward method\n",
    "\n",
    "        # Apart from the validation loss, we also want to track validation accuracy  to get an idea, what the\n",
    "        # model training has achieved \"in real terms\".\n",
    "        test_loss = outputs[0]\n",
    "        logits = outputs[1]\n",
    "        labels = batch[2]\n",
    "\n",
    "        # Evaluating the performance\n",
    "        predictions = torch.argmax(logits, dim=1)\n",
    "        balanced_accuracy = balanced_accuracy_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), adjusted=True)\n",
    "        macro_accuracy = f1_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), average='macro')\n",
    "        micro_accuracy = f1_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), average='micro')\n",
    "        weighted_accuracy = f1_score(labels.detach().cpu().numpy(), predictions.detach().cpu().numpy(), average='weighted')\n",
    "\n",
    "        self.log_dict({\"test_loss\": test_loss, 'accuracy': balanced_accuracy, 'macro-F1': macro_accuracy, 'micro-F1': micro_accuracy, 'weighted-F1':weighted_accuracy},\n",
    "                      on_step=True, on_epoch=True, prog_bar=True, logger=True)\n",
    "\n",
    "    def predict_step(self, batch, batch_nb):\n",
    "        # the training step is a (virtual) method,specified in the interface, that the pl.LightningModule\n",
    "        # class  wants you to overwrite, in case you want to do validation. This we do here, by virtue of this definition.\n",
    "\n",
    "        outputs = self(batch)\n",
    "        # self refers to the model, which in turn accesses the forward method\n",
    "\n",
    "        # Apart from the validation loss, we also want to track validation accuracy  to get an idea, what the\n",
    "        # model training has achieved \"in real terms\".\n",
    "        val_loss = outputs[0]\n",
    "        logits = outputs[1]\n",
    "        labels = batch[2]\n",
    "\n",
    "        predictions = torch.argmax(logits, dim=1)\n",
    "        return predictions.detach().cpu().numpy()\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        # The configure_optimizers is a (virtual) method, specified in the interface, that the\n",
    "        # pl.LightningModule class wants you to overwrite.\n",
    "\n",
    "        # In this case we define that some parameters are optimized in a different way than others. In\n",
    "        # particular we single out parameters that have 'bias', 'LayerNorm.weight' in their names. For those\n",
    "        # we do not use an optimization technique called weight decay.\n",
    "\n",
    "        no_decay = ['bias', 'LayerNorm.weight']\n",
    "\n",
    "        optimizer_grouped_parameters = [{'params': [p for n, p in self.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay':self.hparams.weight_decay},\n",
    "                                        {'params': [p for n, p in self.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}]\n",
    "        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.eps)\n",
    "        # optimizer = DeepSpeedCPUAdam(optimizer_grouped_parameters, adamw_mode=True, lr=self.hparams.learning_rate, betas=(0.9, 0.999), eps=self.hparams.eps)\n",
    "\n",
    "        # We also use a scheduler that is supplied by transformers.\n",
    "        scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.hparams.num_training_steps)\n",
    "        scheduler = {\"scheduler\": scheduler, \"interval\": \"step\", \"frequency\": 1}\n",
    "\n",
    "        return [optimizer], [scheduler]\n",
    "\n",
    "    def freeze(self) -> None:\n",
    "        # freeze all layers, except the final classifier layers\n",
    "        for name, param in self.model.named_parameters():\n",
    "            if 'classifier' not in name:  # classifier layer\n",
    "                param.requires_grad = False\n",
    "\n",
    "        self._frozen = True\n",
    "\n",
    "    def unfreeze(self) -> None:\n",
    "        if self._frozen:\n",
    "            for name, param in self.model.named_parameters():\n",
    "                if 'classifier' not in name:  # classifier layer\n",
    "                    param.requires_grad = True\n",
    "\n",
    "        self._frozen = False\n",
    "\n",
    "    def train_epoch_start(self):\n",
    "        \"\"\"pytorch lightning hook\"\"\"\n",
    "        if self.current_epoch < self.hparams.nr_frozen_epochs:\n",
    "            self.freeze()\n",
    "\n",
    "        if self.current_epoch >= self.hparams.nr_frozen_epochs:\n",
    "            self.unfreeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d-9jmeG6gQNL"
   },
   "source": [
    "# **Helper functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0SiZDms6gv1D"
   },
   "outputs": [],
   "source": [
    "def train_model(tokenizer_name, model_name):\n",
    "  # Loading the datamodule\n",
    "  dm = contextualizedClassifierDataModule(tokenizer_name_or_path=tokenizer_name)\n",
    "  dm.setup()\n",
    "\n",
    "  # Change the number of classes as you increase the size of the dataset\n",
    "  num_classes = 153\n",
    "  nb_epochs = 10\n",
    "\n",
    "  num_training_steps = len(dm.train_dataloader()) * nb_epochs\n",
    "  # Setting the warmup steps to 1/10th the size of training data\n",
    "  warmup_steps = int(len(dm.train_dataloader()) * 10/100)\n",
    "\n",
    "  # Loading the model\n",
    "  model = ClassifierModel(learning_rate=0.0001, adam_epsilon=float(1e-6), weight_decay=0.01, model_name_or_path=model_name, num_classes=num_classes,\n",
    "                            num_training_steps=num_training_steps, warmup_steps=warmup_steps)\n",
    "\n",
    "  early_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=0.01, patience=5, verbose=False, mode=\"min\")\n",
    "  wandb_logger = WandbLogger(save_dir=\"logs\", name=model_name, project=\"AA-Tutorials\")\n",
    "\n",
    "  # %% Setting up the trainer\n",
    "  # Unfortunately the lr_finder functionality doesn't support DeepSpeedStrategy yet, therefore we will set up our trainer twice. Once to find the suitable\n",
    "  # learning rate and secondly to train our model.\n",
    "  trainer = L.Trainer(max_epochs=nb_epochs,\n",
    "                      accelerator=\"gpu\",\n",
    "                      devices=1 if torch.cuda.is_available() else None,\n",
    "                      fast_dev_run=False,\n",
    "                      accumulate_grad_batches = 1, # To run the backward step after n batches, helps to increase the batch size\n",
    "                      benchmark = True, # Fastens the training process\n",
    "                      deterministic=True, # Ensures reproducibility\n",
    "                      limit_train_batches=1.0, # trains on 10% of the data,\n",
    "                      check_val_every_n_epoch = 1, # run val loop every 1 training epochs\n",
    "                      callbacks=[early_stop_callback], # Enables model checkpoint and early stopping\n",
    "                      logger = wandb_logger,\n",
    "                      precision='16-mixed') # Mixed Precision system\n",
    "\n",
    "  # Training model\n",
    "  trainer.fit(model, dm)\n",
    "  # Evaluating model\n",
    "  trainer.test(model=model, dataloaders=dm.test_dataloader())\n",
    "\n",
    "  return trainer, model, dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Juu15XQERb6c"
   },
   "source": [
    "# **Loading the Results Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FRTrY2kcRg_P"
   },
   "outputs": [],
   "source": [
    "results_df = pd.read_csv(\"/content/drive/MyDrive/AA-Tutorial/data/results.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g9So-zo3Qwl2"
   },
   "source": [
    "# [all-MiniLM-L6-v2](https://arxiv.org/abs/2002.10957)\n",
    "\n",
    "---\n",
    "\n",
    "### Key Features\n",
    "- **Efficient Architecture:** Built on a lightweight MiniLM architecture with 6 layers, optimized for both speed and performance, capable of encoding text into 384-dimensional vectors.\n",
    "- **Versatile Applications:** Designed for a wide range of natural language processing tasks including semantic search, clustering, and sentence similarity assessments.\n",
    "- **Bidirectional Contextual Understanding:** By leveraging a bidirectional approach, it captures the context of each word in a sentence more effectively, resulting in richer semantic representations.\n",
    "\n",
    "### Training and Development\n",
    "- **Contrastive Learning Objective:** Fine-tuned on over 1 billion sentence pairs using a self-supervised contrastive learning objective, aiming to distinguish between paired sentences among randomly sampled ones.\n",
    "- **Comprehensive Data Sources:** Trained across a diverse range of datasets including Reddit comments, S2ORC citation pairs, Stack Exchange content, and more, ensuring robustness and versatility across various domains.\n",
    "- **Optimized Training Procedure:** Leveraged TPU v3-8 infrastructure for efficient training, with a focus on hyperparameters like batch size, learning rate warm-up, and sequence length to optimize performance.\n",
    "\n",
    "### Intended Use\n",
    "- **Sentence and Paragraph Encoding:** Primarily intended as an encoder for text, converting input into semantically rich, high-dimensional vectors for further processing or analysis.\n",
    "- **Broad Applicability:** Its embeddings are suitable for tasks that require understanding of textual similarity or categorization, such as information retrieval and document clustering.\n",
    "\n",
    "### Training Achievements\n",
    "- **Extensive Training Data:** Utilizes a massive corpus of over 1 billion sentence pairs, ensuring the model's generalizability across a wide range of text types and domains.\n",
    "- **Innovative Training Strategy:** The application of contrastive learning and fine-tuning on such a large dataset represents a significant advancement in training sentence embedding models."
   ]
  },
  {
   "cell_type": "code",
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      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at sentence-transformers/all-MiniLM-L6-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
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      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mvageesh\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      accuracy_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.647554337978363     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      macro-F1_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.5526297688484192     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      micro-F1_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.703000009059906     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      test_loss_epoch      </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.4879542589187622     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">     weighted-F1_epoch     </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6820589303970337     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m     accuracy_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.647554337978363    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     macro-F1_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.5526297688484192    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     micro-F1_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.703000009059906    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     test_loss_epoch     \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.4879542589187622    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m    weighted-F1_epoch    \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6820589303970337    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _, _ = train_model(\"sentence-transformers/all-MiniLM-L6-v2\", \"sentence-transformers/all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "t_K243lKRR2d"
   },
   "outputs": [],
   "source": [
    "results_df[\"All-miniLM\"] = [0.6475543,0.6820589, 0.7030000, 0.552629]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r0-nm0j-Vb_y"
   },
   "source": [
    "[DistilBERT](https://arxiv.org/abs/1910.01108)\n",
    "\n",
    "---\n",
    "\n",
    "### Key Features\n",
    "- **Efficiency and Speed:** DistilBERT is designed to be smaller and faster than the original BERT model, making it more suitable for environments where computational resources are limited.\n",
    "- **Preserved Performance:** Despite its reduced size, it retains a significant proportion of BERT's performance on various natural language processing tasks.\n",
    "- **Uncased Processing:** This model does not differentiate between uppercase and lowercase English letters, allowing for simplified and more generalized text processing.\n",
    "\n",
    "### Training Objectives\n",
    "- **Distillation Loss:** Trained to replicate the probabilities of the BERT base model, enabling it to achieve similar performance with a smaller size.\n",
    "- **Masked Language Modeling (MLM):** Follows the MLM objective of BERT, where 15% of the words in each sentence are masked, and the model predicts these masked words, aiding in understanding context and language structure.\n",
    "- **Cosine Embedding Loss:** Aims to generate hidden states closely resembling those of the BERT base model, further aligning its understanding of language with BERT.\n",
    "\n",
    "### Training Details\n",
    "- **Data and Preprocessing:** Pretrained on the same corpus as BERT, including BookCorpus and English Wikipedia, using lowercasing and WordPiece tokenization.\n",
    "- **Optimization and Efficiency:** Achieves its efficiency through a distillation process, where the model is trained for 90 hours on 8 V100 GPUs, following a meticulously designed training procedure to ensure it captures the essence of the BERT model's language understanding capabilities.\n",
    "\n",
    "### Intended Uses and Limitations\n",
    "- **Flexible Application:** While primarily designed for masked language modeling and next sentence prediction, DistilBERT is also intended for fine-tuning on a wide range of downstream tasks, including sequence classification and question answering.\n",
    "- **Text Generation:** It's less suited for text generation tasks, for which models like GPT-2 are recommended due to their autoregressive nature.\n",
    "- **Inherent Biases:** Shares biases with its teacher model, BERT, and can exhibit biased predictions, which should be considered when deploying the model in real-world applications."
   ]
  },
  {
   "cell_type": "code",
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    },
    "executionInfo": {
     "elapsed": 678106,
     "status": "ok",
     "timestamp": 1710258971563,
     "user": {
      "displayName": "Vageesh Saxena",
      "userId": "08383190130410030928"
     },
     "user_tz": -60
    },
    "id": "pPLKctyNU-sh",
    "outputId": "27a7084d-2512-4615-f8c0-8af03927faca"
   },
   "outputs": [
    {
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       "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO: Using 16bit Automatic Mixed Precision (AMP)\n",
      "INFO:lightning.pytorch.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n",
      "INFO: GPU available: True (cuda), used: True\n",
      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
      "INFO: HPU available: False, using: 0 HPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO: `Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..\n",
      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO: \n",
      "  | Name  | Type                                | Params\n",
      "--------------------------------------------------------------\n",
      "0 | model | DistilBertForSequenceClassification | 67.1 M\n",
      "--------------------------------------------------------------\n",
      "67.1 M    Trainable params\n",
      "0         Non-trainable params\n",
      "67.1 M    Total params\n",
      "268.285   Total estimated model params size (MB)\n",
      "INFO:lightning.pytorch.callbacks.model_summary:\n",
      "  | Name  | Type                                | Params\n",
      "--------------------------------------------------------------\n",
      "0 | model | DistilBertForSequenceClassification | 67.1 M\n",
      "--------------------------------------------------------------\n",
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     "text": [
      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      accuracy_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7784318923950195     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      macro-F1_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7064704298973083     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      micro-F1_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8080000281333923     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      test_loss_epoch      </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9724990725517273     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">     weighted-F1_epoch     </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7948137521743774     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
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       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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       "│\u001b[36m \u001b[0m\u001b[36m     accuracy_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7784318923950195    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     macro-F1_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7064704298973083    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     micro-F1_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8080000281333923    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     test_loss_epoch     \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9724990725517273    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m    weighted-F1_epoch    \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7948137521743774    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
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   ],
   "source": [
    "_, _, _ = train_model(\"distilbert/distilbert-base-uncased\", \"distilbert/distilbert-base-uncased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pr50vvquVobn"
   },
   "outputs": [],
   "source": [
    "results_df[\"DistilBERT\"] = [0.77843189, 0.7948137, 0.8080000, 0.70647042]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Do-7uaTtY_PM"
   },
   "source": [
    "[DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base)\n",
    "\n",
    "--- \n",
    "\n",
    "### Model Details\n",
    "- **Efficient Design:** DistilRoBERTa is characterized by its distilled architecture from RoBERTa-base, featuring 6 layers, 768 dimensions, and 12 heads, totaling 82 million parameters—a reduction from RoBERTa-base's 125 million.\n",
    "- **Performance:** It maintains robust performance metrics while being twice as fast as its predecessor, facilitating more efficient processing without significant accuracy loss.\n",
    "- **Case Sensitivity:** Unlike some of its counterparts, this model is case-sensitive, distinguishing between capitalized and lowercase letters, which enhances its linguistic understanding.\n",
    "\n",
    "### Uses and Applications\n",
    "- **Masked Language Modeling:** Primarily designed for tasks like masked language modeling, it excels in understanding and predicting context within sentences.\n",
    "- **Fine-tuning for Downstream Tasks:** It's optimized for further fine-tuning across a spectrum of NLP tasks, including sequence classification, token classification, and question answering, with less suitability for text generation tasks.\n",
    "\n",
    "### Training and Evaluation\n",
    "- **Training Corpus:** DistilRoBERTa was trained on the OpenWebTextCorpus, offering a substantial, though smaller, dataset compared to RoBERTa's training materials, ensuring a diverse linguistic foundation.\n",
    "- **Achievements:** When fine-tuned, DistilRoBERTa demonstrates commendable performance across several benchmarks, showcasing its capability to rival more extensive models in accuracy and efficiency."
   ]
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at distilbert/distilroberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO: Using 16bit Automatic Mixed Precision (AMP)\n",
      "INFO:lightning.pytorch.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n",
      "INFO: GPU available: True (cuda), used: True\n",
      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
      "INFO: HPU available: False, using: 0 HPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO: `Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..\n",
      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO: \n",
      "  | Name  | Type                             | Params\n",
      "-----------------------------------------------------------\n",
      "0 | model | RobertaForSequenceClassification | 82.2 M\n",
      "-----------------------------------------------------------\n",
      "82.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "82.2 M    Total params\n",
      "328.944   Total estimated model params size (MB)\n",
      "INFO:lightning.pytorch.callbacks.model_summary:\n",
      "  | Name  | Type                             | Params\n",
      "-----------------------------------------------------------\n",
      "0 | model | RobertaForSequenceClassification | 82.2 M\n",
      "-----------------------------------------------------------\n",
      "82.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "82.2 M    Total params\n",
      "328.944   Total estimated model params size (MB)\n"
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       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
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      "text/plain": [
       "Testing: |          | 0/? [00:00<?, ?it/s]"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      accuracy_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.782052755355835     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      macro-F1_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7075056433677673     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      micro-F1_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8109999895095825     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      test_loss_epoch      </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9345627427101135     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">     weighted-F1_epoch     </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8015660643577576     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m     accuracy_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.782052755355835    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     macro-F1_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7075056433677673    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     micro-F1_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8109999895095825    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     test_loss_epoch     \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9345627427101135    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m    weighted-F1_epoch    \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8015660643577576    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _, _ = train_model(\"distilbert/distilroberta-base\", \"distilbert/distilroberta-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jzRotAuIZbFs"
   },
   "outputs": [],
   "source": [
    "results_df[\"DistilRoBERTa\"] = [0.7820527, 0.8015660, 0.81099999, 0.707505643]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1710260303921,
     "user": {
      "displayName": "Vageesh Saxena",
      "userId": "08383190130410030928"
     },
     "user_tz": -60
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    "id": "Rx00c5H9cvd9",
    "outputId": "37052570-802e-40a6-cabe-49b55f09077c"
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      "text/plain": [
       "       Metrics  MultinomialNB       SVC  RandomForest  LogisticRegression  \\\n",
       "0     Accuracy       0.331297  0.682322      0.412122            0.775756   \n",
       "1  Weighted-F1       0.524405  0.708481      0.344290            0.662284   \n",
       "2     Micro-F1       0.596000  0.700000      0.344000            0.668000   \n",
       "3     Macro-F1       0.337436  0.671945      0.283832            0.590027   \n",
       "\n",
       "        MLP  All-miniLM  DistilBERT  DistilRoBERTa  \n",
       "0  0.676682    0.647554    0.778432       0.782053  \n",
       "1  0.752288    0.682059    0.794814       0.801566  \n",
       "2  0.756000    0.703000    0.808000       0.811000  \n",
       "3  0.646615    0.552629    0.706470       0.707506  "
      ]
     },
     "execution_count": 19,
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