Train_notebook.ipynb

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  "metadata": {
    "colab": {
      "name": "helloworld.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zeb7mGdXcHGX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "b271aeb3-978b-4c89-c1e9-e73cc0731ac6"
      },
      "source": [
        "pip install utils"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting utils\n",
            "  Downloading https://files.pythonhosted.org/packages/55/e6/c2d2b2703e7debc8b501caae0e6f7ead148fd0faa3c8131292a599930029/utils-1.0.1-py2.py3-none-any.whl\n",
            "Installing collected packages: utils\n",
            "Successfully installed utils-1.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsW46pzvLufk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import argparse\n",
        "import random\n",
        "import logging\n",
        "import os\n",
        "import utils\n",
        "import torch\n",
        "from torch.optim import Adam\n",
        "import torch.nn as nn\n",
        "from torch.optim.lr_scheduler import LambdaLR\n",
        "from tqdm import trange\n",
        "import numpy\n",
        "from pytorch_pretrained_bert import BertForTokenClassification\n",
        "\n",
        "from data_loader import DataLoader\n",
        "from evaluate import evaluate\n",
        "import utl\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O75mAav3OokZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "69b135d7-8c17-409c-8177-2096fa24256a"
      },
      "source": [
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument('--data_dir', default='data/task1', help=\"Directory containing the dataset\")\n",
        "parser.add_argument('--bert_model_dir', default='bert-base-multilingual-cased', help=\"Directory containing the BERT model in PyTorch\")\n",
        "parser.add_argument('--model_dir', default='experiments/base_model', help=\"Directory containing params.json\")\n",
        "parser.add_argument('--seed', type=int, default=2019, help=\"random seed for initialization\")\n",
        "parser.add_argument('--restore_file', default=None,\n",
        "                    help=\"Optional, name of the file in --model_dir containing weights to reload before training\")\n",
        "parser.add_argument('--multi_gpu', default=False, action='store_true', help=\"Whether to use multiple GPUs if available\")\n",
        "parser.add_argument('--fp16', default=False, action='store_true', help=\"Whether to use 16-bit float precision instead of 32-bit\")\n",
        "parser.add_argument('--loss_scale', type=float, default=0,\n",
        "                        help=\"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\\n\"\n",
        "                             \"0 (default value): dynamic loss scaling.\\n\"\n",
        "                             \"Positive power of 2: static loss scaling value.\\n\")\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "_StoreAction(option_strings=['--loss_scale'], dest='loss_scale', nargs=None, const=None, default=0, type=<class 'float'>, choices=None, help='Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\\n0 (default value): dynamic loss scaling.\\nPositive power of 2: static loss scaling value.\\n', metavar=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgQESXzgLkxM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "33517a96-1c0c-4509-8143-ab085d3af239"
      },
      "source": [
        "pip install pytorch_pretrained_bert"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pytorch_pretrained_bert in /usr/local/lib/python3.6/dist-packages (0.6.2)\n",
            "Requirement already satisfied: torch>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (1.5.0+cu101)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (1.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (4.41.1)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (1.13.19)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (2019.12.20)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=0.4.1->pytorch_pretrained_bert) (0.16.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (2020.4.5.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (2.9)\n",
            "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch_pretrained_bert) (0.3.3)\n",
            "Requirement already satisfied: botocore<1.17.0,>=1.16.19 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch_pretrained_bert) (1.16.19)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch_pretrained_bert) (0.10.0)\n",
            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.17.0,>=1.16.19->boto3->pytorch_pretrained_bert) (0.15.2)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.6/dist-packages (from botocore<1.17.0,>=1.16.19->boto3->pytorch_pretrained_bert) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.17.0,>=1.16.19->boto3->pytorch_pretrained_bert) (1.12.0)\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUMm6NDRO1WT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(model, data_iterator, optimizer, scheduler, params):\n",
        "    \"\"\"Train the model on `steps` batches\"\"\"\n",
        "    # set model to training mode\n",
        "    model.train()\n",
        "    scheduler.step()\n",
        "\n",
        "    # a running average object for loss\n",
        "    loss_avg = utl.RunningAverage()\n",
        "    \n",
        "    # Use tqdm for progress bar\n",
        "    t = trange(params.train_steps)\n",
        "    for i in t:\n",
        "        # fetch the next training batch\n",
        "        batch_data, batch_tags = next(data_iterator)\n",
        "        batch_masks = batch_data.gt(0)\n",
        "\n",
        "        # compute model output and loss\n",
        "        loss = model(batch_data, token_type_ids=None, attention_mask=batch_masks, labels=batch_tags)\n",
        "\n",
        "        if params.n_gpu > 1 and args.multi_gpu:\n",
        "            loss = loss.mean()  # mean() to average on multi-gpu\n",
        "\n",
        "        # clear previous gradients, compute gradients of all variables wrt loss\n",
        "        model.zero_grad()\n",
        "        if args.fp16:\n",
        "            optimizer.backward(loss)\n",
        "        else:\n",
        "            loss.backward()\n",
        "\n",
        "        # gradient clipping\n",
        "        nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=params.clip_grad)\n",
        "\n",
        "        # performs updates using calculated gradients\n",
        "        optimizer.step()\n",
        "\n",
        "        # update the average loss\n",
        "        loss_avg.update(loss.item())\n",
        "        t.set_postfix(loss='{:05.3f}'.format(loss_avg()))\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TeMJdutWWFxl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_and_evaluate(model, train_data, val_data, optimizer, scheduler, params, model_dir, restore_file=None):\n",
        "    \"\"\"Train the model and evaluate every epoch.\"\"\"\n",
        "    # reload weights from restore_file if specified\n",
        "    if restore_file is not None:\n",
        "        restore_path = os.path.join(args.model_dir, args.restore_file + '.pth.tar')\n",
        "        logging.info(\"Restoring parameters from {}\".format(restore_path))\n",
        "        utl.load_checkpoint(restore_path, model, optimizer)\n",
        "        \n",
        "    best_val_f1 = 0.0\n",
        "    patience_counter = 0\n",
        "\n",
        "    for epoch in range(1, params.epoch_num + 1):\n",
        "        # Run one epoch\n",
        "        logging.info(\"Epoch {}/{}\".format(epoch, params.epoch_num))\n",
        "\n",
        "        # Compute number of batches in one epoch\n",
        "        params.train_steps = params.train_size // params.batch_size\n",
        "        params.val_steps = params.val_size // params.batch_size\n",
        "\n",
        "        # data iterator for training\n",
        "        train_data_iterator = data_loader.data_iterator(train_data, shuffle=True)\n",
        "        # Train for one epoch on training set\n",
        "        train(model, train_data_iterator, optimizer, scheduler, params)\n",
        "\n",
        "        # data iterator for evaluation\n",
        "        train_data_iterator = data_loader.data_iterator(train_data, shuffle=False)\n",
        "        val_data_iterator = data_loader.data_iterator(val_data, shuffle=False)\n",
        "\n",
        "        # Evaluate for one epoch on training set and validation set\n",
        "        params.eval_steps = params.train_steps\n",
        "        train_metrics = evaluate(model, train_data_iterator, params, mark='Train')\n",
        "        params.eval_steps = params.val_steps\n",
        "        val_metrics = evaluate(model, val_data_iterator, params, mark='Val')\n",
        "        \n",
        "        val_f1 = val_metrics['f1']\n",
        "        improve_f1 = val_f1 - best_val_f1\n",
        "\n",
        "        # Save weights of the network\n",
        "        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self\n",
        "        optimizer_to_save = optimizer.optimizer if args.fp16 else optimizer\n",
        "        utl.save_checkpoint({'epoch': epoch + 1,\n",
        "                               'state_dict': model_to_save.state_dict(),\n",
        "                               'optim_dict': optimizer_to_save.state_dict()},\n",
        "                               is_best=improve_f1>0,\n",
        "                               checkpoint=model_dir)\n",
        "        if improve_f1 > 0:\n",
        "            logging.info(\"- Found new best F1\")\n",
        "            best_val_f1 = val_f1\n",
        "            if improve_f1 < params.patience:\n",
        "                patience_counter += 1\n",
        "            else:\n",
        "                patience_counter = 0\n",
        "        else:\n",
        "            patience_counter += 1\n",
        "\n",
        "        # Early stopping and logging best f1\n",
        "        if (patience_counter >= params.patience_num and epoch > params.min_epoch_num) or epoch == params.epoch_num:\n",
        "            logging.info(\"Best val f1: {:05.2f}\".format(best_val_f1))\n",
        "            break\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJUhRULxO3pM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fc46627f-53ce-45c1-d184-82ac96440ae8"
      },
      "source": [
        "if __name__ == '__main__':\n",
        "    args, unknown = parser.parse_known_args()\n",
        "    # Load the parameters from json file\n",
        "    json_path = os.path.join(args.model_dir, 'params.json')\n",
        "    assert os.path.isfile(json_path), \"No json configuration file found at {}\".format(json_path)\n",
        "    params = utl.Params(json_path)\n",
        "\n",
        "    # Use GPUs if available\n",
        "    params.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    params.n_gpu = torch.cuda.device_count()\n",
        "    params.multi_gpu = args.multi_gpu\n",
        "\n",
        "    # Set the random seed for reproducible experiments\n",
        "    random.seed(args.seed)\n",
        "    torch.manual_seed(args.seed)\n",
        "    if params.n_gpu > 0:\n",
        "        torch.cuda.manual_seed_all(args.seed)  # set random seed for all GPUs\n",
        "    params.seed = args.seed\n",
        "    \n",
        "    # Set the logger\n",
        "    utl.set_logger(os.path.join(args.model_dir, 'train.log'))\n",
        "    logging.info(\"device: {}, n_gpu: {}, 16-bits training: {}\".format(params.device, params.n_gpu, args.fp16))\n",
        "\n",
        "    # Create the input data pipeline\n",
        "    logging.info(\"Loading the datasets...\")\n",
        "    \n",
        "    # Initialize the DataLoader\n",
        "    data_loader = DataLoader(args.data_dir, args.bert_model_dir, params, token_pad_idx=0)\n",
        "    \n",
        "    # Load training data and test data\n",
        "    train_data = data_loader.load_data('train')\n",
        "    val_data = data_loader.load_data('val')\n",
        "    # Specify the training and validation dataset sizes\n",
        "    params.train_size = train_data['size']\n",
        "    params.val_size = val_data['size']\n",
        "\n",
        "    # Prepare model\n",
        "    model = BertForTokenClassification.from_pretrained(args.bert_model_dir, num_labels=len(params.tag2idx))\n",
        "    model.to(params.device)\n",
        "    if args.fp16:\n",
        "        model.half()\n",
        "\n",
        "    if params.n_gpu > 1 and args.multi_gpu:\n",
        "        model = torch.nn.DataParallel(model)\n",
        "\n",
        "    # Prepare optimizer\n",
        "    if params.full_finetuning:\n",
        "        param_optimizer = list(model.named_parameters())\n",
        "        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
        "        # no_decay = ['bias', 'gamma', 'beta']\n",
        "        optimizer_grouped_parameters = [\n",
        "            {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], \n",
        "             'weight_decay_rate': 0.01},\n",
        "            {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], \n",
        "             'weight_decay_rate': 0.0}\n",
        "        ]\n",
        "    else:\n",
        "        param_optimizer = list(model.classifier.named_parameters()) \n",
        "        optimizer_grouped_parameters = [{'params': [p for n, p in param_optimizer]}]\n",
        "    if args.fp16:\n",
        "        try:\n",
        "            from apex.optimizers import FP16_Optimizer\n",
        "            from apex.optimizers import FusedAdam\n",
        "        except ImportError:\n",
        "            raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n",
        "        optimizer = FusedAdam(optimizer_grouped_parameters,\n",
        "                              lr=params.learning_rate,\n",
        "                              bias_correction=False,\n",
        "                              max_grad_norm=1.0)\n",
        "        scheduler = LambdaLR(optimizer, lr_lambda=lambda epoch: 1/(1 + 0.05*epoch))\n",
        "        if args.loss_scale == 0:\n",
        "            optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)\n",
        "        else:\n",
        "            optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)\n",
        "    else:\n",
        "        optimizer = Adam(optimizer_grouped_parameters, lr=params.learning_rate)\n",
        "        scheduler = LambdaLR(optimizer, lr_lambda=lambda epoch: 1/(1 + 0.05*epoch))\n",
        "\n",
        "    # Train and evaluate the model\n",
        "    logging.info(\"Starting training for {} epoch(s)\".format(params.epoch_num))\n",
        "    train_and_evaluate(model, train_data, val_data, optimizer, scheduler, params, args.model_dir, args.restore_file)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "device: cuda, n_gpu: 1, 16-bits training: False\n",
            "Loading the datasets...\n",
            "The pre-trained model you are loading is a cased model but you have not set `do_lower_case` to False. We are setting `do_lower_case=False` for you but you may want to check this behavior.\n",
            "loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt from cache at /root/.pytorch_pretrained_bert/96435fa287fbf7e469185f1062386e05a075cadbf6838b74da22bf64b080bc32.99bcd55fc66f4f3360bc49ba472b940b8dcf223ea6a345deb969d607ca900729\n",
            "loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz from cache at /root/.pytorch_pretrained_bert/731c19ddf94e294e00ec1ba9a930c69cc2a0fd489b25d3d691373fae4c0986bd.4e367b0d0155d801930846bb6ed98f8a7c23e0ded37888b29caa37009a40c7b9\n",
            "extracting archive file /root/.pytorch_pretrained_bert/731c19ddf94e294e00ec1ba9a930c69cc2a0fd489b25d3d691373fae4c0986bd.4e367b0d0155d801930846bb6ed98f8a7c23e0ded37888b29caa37009a40c7b9 to temp dir /tmp/tmp1890hjlu\n",
            "Model config {\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"directionality\": \"bidi\",\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pooler_fc_size\": 768,\n",
            "  \"pooler_num_attention_heads\": 12,\n",
            "  \"pooler_num_fc_layers\": 3,\n",
            "  \"pooler_size_per_head\": 128,\n",
            "  \"pooler_type\": \"first_token_transform\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"vocab_size\": 119547\n",
            "}\n",
            "\n",
            "Weights of BertForTokenClassification not initialized from pretrained model: ['classifier.weight', 'classifier.bias']\n",
            "Weights from pretrained model not used in BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "Starting training for 20 epoch(s)\n",
            "Epoch 1/20\n",
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "\n",
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            "100%|██████████| 99/99 [00:13<00:00,  7.26it/s, loss=0.391]\n",
            "- Train metrics: loss: 00.25; f1: 26.06\n",
            "- Val metrics: loss: 00.34; f1: 19.87\n",
            "- Found new best F1\n",
            "Epoch 2/20\n",
            "\n",
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            "100%|██████████| 99/99 [00:13<00:00,  7.25it/s, loss=0.251]\n",
            "- Train metrics: loss: 00.16; f1: 30.93\n",
            "- Val metrics: loss: 00.31; f1: 20.22\n",
            "- Found new best F1\n",
            "Epoch 3/20\n",
            "\n",
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            "100%|██████████| 99/99 [00:13<00:00,  7.29it/s, loss=0.189]\n",
            "- Train metrics: loss: 00.11; f1: 40.70\n",
            "- Val metrics: loss: 00.37; f1: 20.71\n",
            "- Found new best F1\n",
            "Epoch 4/20\n",
            "\n",
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            "- Train metrics: loss: 00.08; f1: 39.78\n",
            "- Val metrics: loss: 00.40; f1: 21.04\n",
            "- Found new best F1\n",
            "Epoch 5/20\n",
            "\n",
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            "- Train metrics: loss: 00.06; f1: 41.86\n",
            "- Val metrics: loss: 00.41; f1: 22.49\n",
            "- Found new best F1\n",
            "Epoch 6/20\n",
            "\n",
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            "- Train metrics: loss: 00.06; f1: 49.04\n",
            "- Val metrics: loss: 00.70; f1: 22.26\n",
            "Epoch 7/20\n",
            "\n",
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            "100%|██████████| 99/99 [00:13<00:00,  7.27it/s, loss=0.064]\n",
            "- Train metrics: loss: 00.02; f1: 51.94\n",
            "- Val metrics: loss: 00.53; f1: 24.22\n",
            "- Found new best F1\n",
            "Epoch 8/20\n",
            "\n",
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            "- Train metrics: loss: 00.02; f1: 51.20\n",
            "- Val metrics: loss: 00.52; f1: 25.80\n",
            "- Found new best F1\n",
            "Epoch 9/20\n",
            "\n",
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            "- Train metrics: loss: 00.02; f1: 50.76\n",
            "- Val metrics: loss: 00.64; f1: 24.52\n",
            "Epoch 10/20\n",
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            "- Train metrics: loss: 00.01; f1: 54.79\n",
            "- Val metrics: loss: 00.61; f1: 26.83\n",
            "- Found new best F1\n",
            "Epoch 11/20\n",
            "\n",
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            "- Train metrics: loss: 00.01; f1: 53.26\n",
            "- Val metrics: loss: 00.63; f1: 26.45\n",
            "Epoch 12/20\n",
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            "- Train metrics: loss: 00.01; f1: 55.90\n",
            "- Val metrics: loss: 00.66; f1: 27.89\n",
            "- Found new best F1\n",
            "Epoch 13/20\n",
            "\n",
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            "- Train metrics: loss: 00.01; f1: 54.96\n",
            "- Val metrics: loss: 00.80; f1: 24.68\n",
            "Epoch 14/20\n",
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            "- Train metrics: loss: 00.00; f1: 52.84\n",
            "- Val metrics: loss: 00.74; f1: 25.78\n",
            "Epoch 15/20\n",
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            "100%|██████████| 99/99 [00:13<00:00,  7.36it/s, loss=0.014]\n",
            "- Train metrics: loss: 00.00; f1: 51.44\n",
            "- Val metrics: loss: 00.66; f1: 25.67\n",
            "Best val f1: 27.89\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}