NLP_LLM_Transformer_baseline.ipynb

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    "\r\n",
    "\r\n",
    "# NLP Baseline on LLM using Transformers\r\n",
    "\r\n",
    "This notebook implements an end-to-end NLP workflow using transformer-based models, with a primary focus on fine-tuning a BERT-based architecture for text classification tasks. It provides a structured pipeline for training, validation, and evaluation using modern NLP libraries.\r\n",
    "\r\n",
    "## Key Features\r\n",
    "- **Model Architecture**: Utilizes `distilbert-base-uncased` as the base transformer model for text classification. The architecture can be expanded or adapted to other models.\r\n",
    "- **Custom Dataset Handling**: Implements a `CustomDataset` class that supports tokenization, encoding, and dynamic batching using PyTorch's `Dataset` and `DataLoader`.\r\n",
    "- **ModelTrainer Class**: Includes a comprehensive `ModelTrainer` class that:\r\n",
    "  - Loads and prepares datasets (e.g., IMDB dataset).\r\n",
    "  - Configures model training components like optimizer (`AdamW`), learning rate scheduler (`linear_schedule_with_warmup`), and gradient scaling (`GradScaler`).\r\n",
    "  - Supports multi-GPU training through `nn.DataParallel`.\r\n",
    "  - Provides functionality for model training, validation, metrics calculation (`f1_score`, `precision`, `recall`), and saving.\r\n",
    "- **Logging and Experiment Tracking**: Uses `wandb` (Weights & Biases) for experiment tracking, including automatic logging of metrics, losses, and configurations.\r\n",
    "- **Implementation Details**:\r\n",
    "  - Includes both the training (`train`) and validation (`validate`) methods to evaluate model performance.\r\n",
    "  - Flexible initialization parameters like learning rate, batch size, maximum seother datasets or models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1NnenHnBPVJL"
   },
   "outputs": [],
   "source": [
    "!pip install -q bertopic==0.15.0 datasets==2.14.4 transformers==4.24.0\n",
    "!pip install -q sentencepiece==0.1.99 sentence-transformers==2.2.2\n",
    "!pip install -q wandb"
   ]
  },
  {
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   "execution_count": null,
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1702338357107,
     "user": {
      "displayName": "Даниил Богданов",
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    "outputId": "692beb90-35d8-48bb-fff3-2a59f7bc6b1a"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mexsandebest\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
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    {
     "data": {
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       "True"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import logging\n",
    "import os\n",
    "import uuid\n",
    "\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import wandb\n",
    "from datasets import load_dataset\n",
    "from sklearn.metrics import (\n",
    "    classification_report,\n",
    "    f1_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    ")\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from tqdm.auto import tqdm\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "J_TKyJXAeqj6"
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   "outputs": [],
   "source": [
    "def get_logger(name: str = __name__) -> logging.Logger:\n",
    "    logging.basicConfig(\n",
    "        format=\"%(asctime)s:%(module)s:%(funcName)s:%(levelname)s: %(message)s\",\n",
    "        datefmt=\"%Y-%m-%d %H:%M:%S\",\n",
    "    )\n",
    "    logger = logging.getLogger(name)\n",
    "    logger.setLevel(logging.INFO)\n",
    "    return logger\n",
    "\n",
    "\n",
    "logger = get_logger(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZN37dmQYPeaX"
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   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, texts, labels, tokenizer, max_length: int, tokenize_at_init:bool = False):\n",
    "        \"\"\"\n",
    "        Initialize the custom dataset\n",
    "        :param texts: List of texts (documents or sentences)\n",
    "        :param labels: Corresponding labels for the texts\n",
    "        :param tokenizer: Tokenizer instance for text processing\n",
    "        :param max_length: Maximum length of the tokenized output\n",
    "        :param tokenize_at_init: Flag to perform tokenization at initialization\n",
    "        \"\"\"\n",
    "\n",
    "        self.texts = texts\n",
    "        self.labels = labels\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_length = max_length\n",
    "        self.tokenize_at_init = tokenize_at_init\n",
    "\n",
    "        if tokenize_at_init:\n",
    "            self.encoded_texts = self.tokenizer.batch_encode_plus(\n",
    "                texts,\n",
    "                add_special_tokens=True,\n",
    "                max_length=max_length,\n",
    "                truncation=True,\n",
    "                padding=\"max_length\",\n",
    "                return_attention_mask=True,\n",
    "                return_tensors=\"pt\",\n",
    "            )\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        if self.tokenize_at_init:\n",
    "            return {\n",
    "                \"input_ids\": self.encoded_texts[\"input_ids\"][idx].flatten(),\n",
    "                \"attention_mask\": self.encoded_texts[\"attention_mask\"][idx].flatten(),\n",
    "                \"label\": torch.tensor(self.labels[idx], dtype=torch.long),\n",
    "            }\n",
    "        else:\n",
    "            text = self.texts[idx]\n",
    "            label = self.labels[idx]\n",
    "\n",
    "            encoding = self.tokenizer.encode_plus(\n",
    "                text,\n",
    "                add_special_tokens=True,\n",
    "                max_length=self.max_length,\n",
    "                truncation=True,\n",
    "                padding=\"max_length\",\n",
    "                return_attention_mask=True,\n",
    "                return_tensors=\"pt\",\n",
    "            )\n",
    "\n",
    "            return {\n",
    "                \"input_ids\": encoding[\"input_ids\"].flatten(),\n",
    "                \"attention_mask\": encoding[\"attention_mask\"].flatten(),\n",
    "                \"label\": label,\n",
    "            }\n",
    "\n",
    "\n",
    "class ModelTrainer:\n",
    "    \"\"\"\n",
    "    This class implements logic run an experiemnt with a provided transformers classification model.\n",
    "    It incudes following components:\n",
    "    - load data\n",
    "    - load and configure a model and its artifacts\n",
    "    - train model\n",
    "    - validate model\n",
    "    - save model\n",
    "    - compue metrics\n",
    "    - run_experiment (as the man entrypoint to execute all flow)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        model_name: str = \"distilbert-base-uncased\",\n",
    "        num_labels: int = 2,\n",
    "        learning_rate: float = 2e-5,\n",
    "        max_length: int = 128,\n",
    "        num_workers: int = 4,\n",
    "        weight_decay: float = 0.01,\n",
    "        datasets_tokenize_at_init: bool = False,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Initialize the ModelTrainer class with a specified transformer model\n",
    "        :param model_name: Name of the transformer model to be used\n",
    "        :param num_labels: Number of labels for the classification task\n",
    "        :param learning_rate: Learning rate for the optimizer\n",
    "        :param max_length: Maximum length of the tokenized sequences\n",
    "        :param datasets_tokenize_at_init: Flag to perform tokenization at initialization of datasets\n",
    "        \"\"\"\n",
    "\n",
    "        logger.info(\"Initializing ModelTrainer\")\n",
    "\n",
    "        if model_name == \"distilbert-base-uncased\":\n",
    "            logger.info(f\"Using model {model_name}\")\n",
    "            from transformers import AutoTokenizer, AutoModel\n",
    "\n",
    "            self.model = AutoModel.from_pretrained(\n",
    "                model_name, num_labels=num_labels\n",
    "            )\n",
    "            self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "        else:\n",
    "            raise ValueError(f\"Model {model_name} is not supported in ModelTrainer\")\n",
    "\n",
    "        self.model_name = model_name\n",
    "        self.learning_rate = learning_rate\n",
    "        self.max_length = max_length\n",
    "        self.num_workers = num_workers\n",
    "        self.datasets_tokenize_at_init = datasets_tokenize_at_init\n",
    "        self.weight_decay = weight_decay\n",
    "\n",
    "        self.optimizer = None\n",
    "        self.scaler = None\n",
    "        self.scheduler = None\n",
    "\n",
    "        self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "        self.model.to(self.device)\n",
    "\n",
    "        logger.info(\"ModelTrainer successfully initialized!\")\n",
    "\n",
    "    def configure_optimizer(self, weight_decay: float = 0.01):\n",
    "        \"\"\"\n",
    "        Configure the optimizer for the model\n",
    "        :param weight_decay: Weight decay factor for regularization\n",
    "        \"\"\"\n",
    "\n",
    "        no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
    "        optimizer_grouped_parameters = [\n",
    "            {\n",
    "                \"params\": [\n",
    "                    p\n",
    "                    for n, p in self.model.named_parameters()\n",
    "                    if not any(nd in n for nd in no_decay)\n",
    "                ],\n",
    "                \"weight_decay\": weight_decay,\n",
    "            },\n",
    "            {\n",
    "                \"params\": [\n",
    "                    p\n",
    "                    for n, p in self.model.named_parameters()\n",
    "                    if any(nd in n for nd in no_decay)\n",
    "                ],\n",
    "                \"weight_decay\": 0.0,\n",
    "            },\n",
    "        ]\n",
    "        self.optimizer = optim.AdamW(\n",
    "            optimizer_grouped_parameters, lr=self.learning_rate\n",
    "        )\n",
    "\n",
    "        self.scaler = GradScaler()\n",
    "\n",
    "    def configure_scheduler(self, num_warmup_steps: int, num_training_steps: int) -> None:\n",
    "        \"\"\"\n",
    "        Set up the learning rate scheduler\n",
    "        :param num_warmup_steps: Number of warm-up steps for the scheduler\n",
    "        :param num_training_steps: Total number of training steps\n",
    "        \"\"\"\n",
    "\n",
    "        if not self.optimizer:\n",
    "            raise ValueError(\n",
    "                \"Optimizer not configured. Please configure optimizer before setting up the scheduler.\"\n",
    "            )\n",
    "\n",
    "        self.scheduler = get_linear_schedule_with_warmup(\n",
    "            self.optimizer,\n",
    "            num_warmup_steps=num_warmup_steps,\n",
    "            num_training_steps=num_training_steps,\n",
    "        )\n",
    "\n",
    "    def apply_data_parallel(self) -> None:\n",
    "        \"\"\"\n",
    "        Apply data parallelism to the model if multiple GPUs are available\n",
    "        \"\"\"\n",
    "\n",
    "        if torch.cuda.device_count() > 1:\n",
    "            self.model = nn.DataParallel(self.model)\n",
    "\n",
    "    def load_data(\n",
    "        self, filename: str, split: str, max_samples: int = None\n",
    "    ) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        Load the dataset from a given source.\n",
    "        :param filename: Name of the file or dataset.\n",
    "        :param split: Specific split of the dataset (e.g., 'train', 'test').\n",
    "        :param max_samples: Maximum number of samples to load.\n",
    "        :return: The dataset as a pandas DataFrame.\n",
    "        \"\"\"\n",
    "\n",
    "        dataset = load_dataset(filename, split=split)\n",
    "        df = dataset.to_pandas()\n",
    "        if max_samples is not None:\n",
    "            df = df[:max_samples]\n",
    "        return df\n",
    "\n",
    "    def train(self, dataset: CustomDataset, batch_size: int = 32, epochs: int = 3):\n",
    "        \"\"\"\n",
    "        Train the model on the provided dataset\n",
    "        :param dataset: The dataset to train the model on\n",
    "        :param batch_size: The size of each batch during training\n",
    "        :param epochs: The number of epochs to train the model\n",
    "        \"\"\"\n",
    "\n",
    "        dataloader = DataLoader(\n",
    "            dataset,\n",
    "            batch_size=batch_size,\n",
    "            shuffle=True,\n",
    "            num_workers=self.num_workers,\n",
    "            pin_memory=True,\n",
    "        )\n",
    "\n",
    "        self.model.train()\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            total_loss = 0\n",
    "\n",
    "            for batch_index, batch in enumerate(tqdm(dataloader)):\n",
    "                input_ids = batch[\"input_ids\"].to(self.device)\n",
    "                attention_mask = batch[\"attention_mask\"].to(self.device)\n",
    "                labels = batch[\"label\"].to(self.device)\n",
    "\n",
    "                self.optimizer.zero_grad()\n",
    "\n",
    "                with autocast():\n",
    "                    outputs = self.model(\n",
    "                        input_ids, attention_mask=attention_mask, labels=labels\n",
    "                    )\n",
    "                    loss = outputs.loss\n",
    "\n",
    "                self.scaler.scale(loss).backward()\n",
    "                self.scaler.step(self.optimizer)\n",
    "                self.scaler.update()\n",
    "\n",
    "                loss_value = loss.item()\n",
    "                total_loss += loss_value\n",
    "                wandb.log(\n",
    "                    {\"loss\": loss_value, \"epoch\": epoch, \"batch_index\": batch_index}\n",
    "                )\n",
    "\n",
    "            avg_loss = total_loss / len(dataloader)\n",
    "            logger.info(f\"Epoch {epoch + 1}/{epochs} - Loss: {avg_loss:.4f}\")\n",
    "\n",
    "            if self.scheduler:\n",
    "                self.scheduler.step()\n",
    "\n",
    "    def validate(self, dataset: CustomDataset, batch_size: int = 32) -> dict:\n",
    "        \"\"\"\n",
    "        Validate the model on the provided dataset\n",
    "        :param dataset: The dataset to validate the model on\n",
    "        :param batch_size: The size of each batch during validation\n",
    "        :return: A dictionary containing validation labels and predictions\n",
    "        \"\"\"\n",
    "\n",
    "        dataloader = DataLoader(\n",
    "            dataset,\n",
    "            batch_size=batch_size,\n",
    "            shuffle=True,\n",
    "            num_workers=self.num_workers,\n",
    "            pin_memory=True,\n",
    "        )\n",
    "\n",
    "        self.model.eval()\n",
    "\n",
    "        valid_labels = []\n",
    "        valid_preds = []\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for batch in tqdm(dataloader):\n",
    "                input_ids = batch[\"input_ids\"].to(self.device)\n",
    "                attention_mask = batch[\"attention_mask\"].to(self.device)\n",
    "                labels = batch[\"label\"].to(self.device)\n",
    "\n",
    "                outputs = self.model(input_ids, attention_mask=attention_mask)\n",
    "\n",
    "                logits = outputs.logits\n",
    "                predictions = torch.argmax(logits, dim=1)\n",
    "                valid_preds.extend(predictions.cpu().numpy())\n",
    "                valid_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "        return {\"valid_labels\": valid_labels, \"valid_preds\": valid_preds}\n",
    "\n",
    "    def compute_metrics_report(self, labels, predictions):\n",
    "        \"\"\"\n",
    "        Compute and return metrics for the validation\n",
    "        :param labels: True labels of the validation set\n",
    "        :param predictions: Model's predictions on the validation set\n",
    "        :return: A dictionary with computed metrics\n",
    "        \"\"\"\n",
    "\n",
    "        f1 = f1_score(labels, predictions, average=\"weighted\")\n",
    "        precision = precision_score(labels, predictions, average=\"weighted\")\n",
    "        recall = recall_score(labels, predictions, average=\"weighted\")\n",
    "        report = classification_report(labels, predictions)\n",
    "\n",
    "        metrics = {\n",
    "            \"f1_score\": f1,\n",
    "            \"precision_score\": precision,\n",
    "            \"recall_score\": recall,\n",
    "            \"classification_report\": report,\n",
    "        }\n",
    "\n",
    "        wandb.run.summary[\"f1_score\"] = f1\n",
    "        wandb.run.summary[\"precision\"] = precision\n",
    "        wandb.run.summary[\"recall\"] = recall\n",
    "\n",
    "        return metrics\n",
    "\n",
    "    def save_model(self, dst_path: str) -> None:\n",
    "        \"\"\"\n",
    "        Save the trained model to a specified path\n",
    "        :param dst_path: Destination path for saving the model\n",
    "        \"\"\"\n",
    "\n",
    "        model_to_save = (\n",
    "            self.model.module if hasattr(self.model, \"module\") else self.model\n",
    "        )\n",
    "\n",
    "        if not os.path.exists(os.path.dirname(dst_path)):\n",
    "            os.makedirs(os.path.dirname(dst_path))\n",
    "\n",
    "        torch.save(model_to_save.state_dict(), dst_path)\n",
    "\n",
    "    def run_experiment(\n",
    "        self,\n",
    "        train_file: str,\n",
    "        test_file: str,\n",
    "        batch_size: int = 32,\n",
    "        epochs: int = 2,\n",
    "        max_samples: int = None,\n",
    "        save_path:str = None,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Run the experiment including training and validation\n",
    "        :param train_file: Path or name of the training data file\n",
    "        :param test_file: Path or name of the testing/validation data file\n",
    "        :param batch_size: Batch size for both training and validation\n",
    "        :param epochs: Number of epochs to train the model\n",
    "        :param max_samples: Maximum number of samples to be used in training/testing\n",
    "        :param save_path: Path to save the trained model. If None, the model will not be saved\n",
    "        \"\"\"\n",
    "\n",
    "        wandb.init(\n",
    "            project=\"bdt-nlp-hw1\",\n",
    "            name=f\"experiment_{uuid.uuid4()}\",\n",
    "            config={\n",
    "                \"model\": self.model_name,\n",
    "                \"learning_rate\": self.learning_rate,\n",
    "                \"tokenizer_max_length\": self.max_length,\n",
    "                \"num_workers\": self.num_workers,\n",
    "                \"weight_decay\": self.weight_decay,\n",
    "                \"architecture\": \"BERT\",\n",
    "                \"dataset\": train_file,\n",
    "                \"epochs\": epochs,\n",
    "                \"optimizer\": \"AdamW\",\n",
    "                \"scheduler\": \"linear_schedule_with_warmup\",\n",
    "            },\n",
    "        )\n",
    "\n",
    "        logger.info(\"Running experiment!\")\n",
    "        logger.info(\n",
    "            f\"Loading training data (tokenize_at_init = {self.datasets_tokenize_at_init})\"\n",
    "        )\n",
    "        train_data = self.load_data(train_file, split=\"train\", max_samples=max_samples)\n",
    "        train_dataset = CustomDataset(\n",
    "            texts=train_data[\"text\"].tolist(),\n",
    "            labels=train_data[\"label\"].tolist(),\n",
    "            tokenizer=self.tokenizer,\n",
    "            max_length=self.max_length,\n",
    "            tokenize_at_init=self.datasets_tokenize_at_init,\n",
    "        )\n",
    "\n",
    "        logger.info(\n",
    "            f\"Loading validation data (tokenize_at_init = {self.datasets_tokenize_at_init})\"\n",
    "        )\n",
    "        test_data = self.load_data(test_file, split=\"test\", max_samples=max_samples)\n",
    "        test_dataset = CustomDataset(\n",
    "            texts=test_data[\"text\"].tolist(),\n",
    "            labels=test_data[\"label\"].tolist(),\n",
    "            tokenizer=self.tokenizer,\n",
    "            max_length=self.max_length,\n",
    "            tokenize_at_init=self.datasets_tokenize_at_init,\n",
    "        )\n",
    "\n",
    "        logger.info(\"Trying apply Data Parallel\")\n",
    "        self.apply_data_parallel()\n",
    "\n",
    "        logger.info(\"Configuring optimizer\")\n",
    "        self.configure_optimizer(self.weight_decay)\n",
    "\n",
    "        logger.info(\"Configuring scheduler\")\n",
    "        total_steps = len(train_dataset) // batch_size * epochs\n",
    "        self.configure_scheduler(num_warmup_steps=0, num_training_steps=total_steps)\n",
    "\n",
    "        logger.info(\"Running model training\")\n",
    "        self.train(train_dataset, batch_size=batch_size, epochs=epochs)\n",
    "\n",
    "        logger.info(\"Running model validation\")\n",
    "        validation_results = self.validate(test_dataset, batch_size=batch_size)\n",
    "\n",
    "        logger.info(\"Computing metrics\")\n",
    "        metrics = self.compute_metrics_report(\n",
    "            validation_results[\"valid_labels\"], validation_results[\"valid_preds\"]\n",
    "        )\n",
    "        for key, value in metrics.items():\n",
    "            logger.info(f\"\\n{key}:\\n{value}\\n\")\n",
    "\n",
    "        if save_path is not None:\n",
    "            logger.info(\"Saving model\")\n",
    "            self.save_model(save_path)\n",
    "\n",
    "        logger.info(\"Experiment successfully completed!\")\n",
    "\n",
    "        wandb.finish()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4835,
     "status": "ok",
     "timestamp": 1702338361917,
     "user": {
      "displayName": "Даниил Богданов",
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     "text": [
      "INFO:__main__:Initializing ModelTrainer\n",
      "INFO:__main__:Using model distilbert-base-uncased\n",
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_transform.bias']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_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",
      "INFO:__main__:ModelTrainer successfully initialized!\n"
     ]
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   ],
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    "model_trainer = ModelTrainer(\n",
    "    model_name='distilbert-base-uncased',\n",
    "    num_labels=2,\n",
    "    learning_rate=3e-5,\n",
    "    max_length=256,\n",
    "    num_workers=2,\n",
    "    weight_decay=5e-2,\n",
    "    datasets_tokenize_at_init=False\n",
    ")"
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      "INFO:__main__:Running experiment!\n",
      "INFO:__main__:Loading training data (tokenize_at_init = False)\n",
      "INFO:__main__:Loading validation data (tokenize_at_init = False)\n",
      "INFO:__main__:Trying apply Data Parallel\n",
      "INFO:__main__:Configuring optimizer\n",
      "INFO:__main__:Configuring scheduler\n",
      "INFO:__main__:Running model training\n"
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      "INFO:__main__:Computing metrics\n",
      "INFO:__main__:\n",
      "f1_score:\n",
      "0.9057922482678108\n",
      "\n",
      "INFO:__main__:\n",
      "precision_score:\n",
      "0.9073979164005953\n",
      "\n",
      "INFO:__main__:\n",
      "recall_score:\n",
      "0.90588\n",
      "\n",
      "INFO:__main__:\n",
      "classification_report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.94      0.91     12500\n",
      "           1       0.93      0.88      0.90     12500\n",
      "\n",
      "    accuracy                           0.91     25000\n",
      "   macro avg       0.91      0.91      0.91     25000\n",
      "weighted avg       0.91      0.91      0.91     25000\n",
      "\n",
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>batch_index</td><td>▁▂▂▃▃▄▄▅▅▆▇▇█▁▁▂▃▃▄▄▅▅▆▆▇█▁▁▂▂▃▄▄▅▅▆▆▇▇█</td></tr><tr><td>epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▅▅▅▅▅▅▅▅▅▅▅▅▅██████████████</td></tr><tr><td>loss</td><td>█▄▆▄▄▅▄▃▅▄▄▅▄▃▂▁▂▂▂▂▄▄▃▂▄▂▂▂▃▃▂▂▂▃▂▂▄▃▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>batch_index</td><td>390</td></tr><tr><td>epoch</td><td>2</td></tr><tr><td>f1_score</td><td>0.90579</td></tr><tr><td>loss</td><td>0.04176</td></tr><tr><td>precision</td><td>0.9074</td></tr><tr><td>recall</td><td>0.90588</td></tr></table><br/></div></div>"
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