fine_tune_BERT_doc_level_annots.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gonzaq94/NER-LLM-classification/blob/main/fine_tune_BERT_doc_level_annots.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vfLms2TWYnow"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPxYOY1wYrmQ",
        "outputId": "fb39d568-10b8-4b68-9610-370a056c43df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import comet_ml\n",
        "import comet_llm\n",
        "import os\n",
        "import gdown\n",
        "from datasets import load_dataset, concatenate_datasets\n",
        "import random\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, TrainerCallback\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "from sklearn.metrics import precision_recall_fscore_support, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "from openai import OpenAI\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from torch.nn import CrossEntropyLoss"
      ],
      "metadata": {
        "id": "aGcKHyewYzcq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FPJAgMkj6oxu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download and augment dataset with GPT"
      ],
      "metadata": {
        "id": "nDn9Zev3ZZ-2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the CONLL dataset\n",
        "dataset_train = load_dataset(\"conll2003\", split=\"train\").shuffle().select(range(1000))\n",
        "dataset_valid = load_dataset(\"conll2003\", split=\"validation\").shuffle().select(range(1000))"
      ],
      "metadata": {
        "id": "BTa66jThZREq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "5e01adf1-c572-4956-ab5a-d5c02084af36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-dfbacce7798d>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Load the CONLL dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"conll2003\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdataset_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"conll2003\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"validation\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m     \u001b[0;31m# Create a dataset builder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2132\u001b[0;31m     builder_instance = load_dataset_builder(\n\u001b[0m\u001b[1;32m   2133\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2134\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\u001b[0m\n\u001b[1;32m   1851\u001b[0m         \u001b[0mdownload_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdownload_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdownload_config\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mDownloadConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m         \u001b[0mdownload_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1853\u001b[0;31m     dataset_module = dataset_module_factory(\n\u001b[0m\u001b[1;32m   1854\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1855\u001b[0m         \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m                 \u001b[0m_raise_if_offline_mode_is_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m                 dataset_readme_path = api.hf_hub_download(\n\u001b[0m\u001b[1;32m   1600\u001b[0m                     \u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m                     \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREPOCARD_FILENAME\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(self, repo_id, filename, subfolder, repo_type, revision, cache_dir, local_dir, force_download, proxies, etag_timeout, token, local_files_only, resume_download, legacy_cache_layout, force_filename, local_dir_use_symlinks)\u001b[0m\n\u001b[1;32m   5278\u001b[0m             \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5280\u001b[0;31m         return hf_hub_download(\n\u001b[0m\u001b[1;32m   5281\u001b[0m             \u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5282\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m                     \u001b[0mmessage\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"\\n\\n\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcustom_message\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, legacy_cache_layout, resume_download, force_filename, local_dir_use_symlinks)\u001b[0m\n\u001b[1;32m   1238\u001b[0m         )\n\u001b[1;32m   1239\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1240\u001b[0;31m         return _hf_hub_download_to_cache_dir(\n\u001b[0m\u001b[1;32m   1241\u001b[0m             \u001b[0;31m# Destination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1242\u001b[0m             \u001b[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_hf_hub_download_to_cache_dir\u001b[0;34m(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, proxies, token, local_files_only, force_download)\u001b[0m\n\u001b[1;32m   1301\u001b[0m     \u001b[0;31m# Try to get metadata (etag, commit_hash, url, size) from the server.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m     \u001b[0;31m# If we can't, a HEAD request error is returned.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1303\u001b[0;31m     (url_to_download, etag, commit_hash, expected_size, head_call_error) = _get_metadata_or_catch_error(\n\u001b[0m\u001b[1;32m   1304\u001b[0m         \u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1305\u001b[0m         \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_get_metadata_or_catch_error\u001b[0;34m(repo_id, filename, repo_type, revision, endpoint, proxies, etag_timeout, headers, token, local_files_only, relative_filename, storage_folder)\u001b[0m\n\u001b[1;32m   1750\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1752\u001b[0;31m                 metadata = get_hf_file_metadata(\n\u001b[0m\u001b[1;32m   1753\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0metag_timeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1754\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout, library_name, library_version, user_agent, headers)\u001b[0m\n\u001b[1;32m   1672\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1673\u001b[0m     \u001b[0;31m# Retrieve metadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1674\u001b[0;31m     r = _request_wrapper(\n\u001b[0m\u001b[1;32m   1675\u001b[0m         \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"HEAD\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1676\u001b[0m         \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[0;31m# Recursively follow relative redirects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfollow_relative_redirects\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m         response = _request_wrapper(\n\u001b[0m\u001b[1;32m    377\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0;31m# Perform request and return if status_code is not in the retry list.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    400\u001b[0m     \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    587\u001b[0m         }\n\u001b[1;32m    588\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_http.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;34m\"\"\"Catch any RequestException to append request id to the error message for debugging.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRequestException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mrequest_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_AMZN_TRACE_ID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m             resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    668\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m             \u001b[0;31m# Make the request on the HTTPConnection object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m             response = self._make_request(\n\u001b[0m\u001b[1;32m    790\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    534\u001b[0m         \u001b[0;31m# Receive the response from the server\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    537\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m         \u001b[0;31m# Get the response from http.client.HTTPConnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1373\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1375\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1376\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1377\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    706\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1301\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1303\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1304\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1305\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test = load_dataset(\"conll2003\", split=\"test\")"
      ],
      "metadata": {
        "id": "SiZzwWWrIHa1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "client = OpenAI(api_key=OPEN_AI_API_KEY)\n",
        "\n",
        "# Function to classify a document\n",
        "def classify_document(document_text):\n",
        "\n",
        "    prompt = f\"\"\"\n",
        "    You are an AI document classifier. Please classify the following document into one of the following categories: World, Sport, Business, Technology, Other.\n",
        "\n",
        "    Document:\n",
        "    \"{document_text}\"\n",
        "\n",
        "    Category:\n",
        "    \"\"\"\n",
        "\n",
        "    response = client.chat.completions.create(\n",
        "        messages=[\n",
        "            {\n",
        "                \"role\": \"user\",\n",
        "                \"content\": prompt,\n",
        "            }\n",
        "        ],\n",
        "        model=\"gpt-4o-mini\",\n",
        "        max_tokens=10,  # Only need a few tokens for the category response\n",
        "        temperature=0  # Lower temperature for deterministic results\n",
        "    )\n",
        "\n",
        "    return response.choices[0].message.content\n",
        "\n",
        "# Classify some example documents\n",
        "\n",
        "for i in range(5):\n",
        "\n",
        "  example = \" \".join(dataset_train[i]['tokens'])\n",
        "\n",
        "  category = classify_document(example)\n",
        "\n",
        "  print(f\"Example {i}:\", example)\n",
        "  print(\"Classification:\", category)\n",
        "  print()\n"
      ],
      "metadata": {
        "id": "yqJvSyHZZVV8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Augment all dataset with document-level annotations"
      ],
      "metadata": {
        "id": "RztsCMwXFQEE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc_label_dct = {\"World\": 0, \"Sport\": 1, \"Business\": 2, \"Technology\": 3, \"Other\": 4}\n",
        "\n",
        "def add_document_label(row):\n",
        "\n",
        "    text = \" \".join(row['tokens'])\n",
        "    doc_label = classify_document(text)\n",
        "    row[\"doc_labels\"] = doc_label_dct[doc_label.lstrip(\"Category: \")]\n",
        "\n",
        "    return row"
      ],
      "metadata": {
        "id": "b83u4QZdATNU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train_augmented = dataset_train.map(add_document_label)\n",
        "dataset_valid_augmented = dataset_valid.map(add_document_label)"
      ],
      "metadata": {
        "id": "RyD1IdV0H7Ki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_augmented = dataset_test.map(add_document_label)"
      ],
      "metadata": {
        "id": "37aQPBKbPKce"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(dataset_train_augmented['doc_labels'])\n",
        "plt.title('Distribution of document labels in train set')\n",
        "plt.xlabel('Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "QdXUfG3HKPSM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(dataset_valid_augmented['doc_labels'])\n",
        "plt.title('Distribution of document labels in valid set')\n",
        "plt.xlabel('Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "o7Qt_7i5RPyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(dataset_test_augmented['doc_labels'])\n",
        "plt.title('Distribution of document labels in test set')\n",
        "plt.xlabel('Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "tWDqJ4tcZoGy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the dataset to a directory\n",
        "dataset_train_augmented.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented_1k\")\n",
        "dataset_valid_augmented.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented_1k\")\n",
        "dataset_test_augmented.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_test_augmented\")"
      ],
      "metadata": {
        "id": "SpsFjHXNHmXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create a balanced augmented"
      ],
      "metadata": {
        "id": "lUQM3syIPmjb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the CONLL dataset\n",
        "dataset_train = load_dataset(\"conll2003\", split=\"train\")\n",
        "dataset_valid = load_dataset(\"conll2003\", split=\"validation\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383,
          "referenced_widgets": [
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            "1f5a3b37cf3445cc910cda39dceb283e",
            "87fdf46c68544557997da9b96b0f26ee",
            "bd545ceb8fac4e41886100434570d7a9",
            "c098941ff9894a4dae0b3227b767f782",
            "259a3020205b495a92522dda09973b3d",
            "525e7481e5e343ce86026a52a5266d85",
            "898a413514fe4d1c83b39ccb3f5d1bc7"
          ]
        },
        "id": "mnpyjP5MPsz1",
        "outputId": "fd51fa75-10af-48ff-fccd-b2b157588490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/12.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "318d41fb78cd42b9b800ee9fa53d255a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "conll2003.py:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1347b7550ed4977b77c3afb1c4225b9"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for conll2003 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conll2003.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "982960f00055427c944756860ff1dc75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/14041 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7b64edcf47446b081e0e911328dd369"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c292e8f581664b7691b6b01c6e62ec14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/3453 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b23a349d50474de7872f845cdc636be4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client = OpenAI(api_key=OPEN_AI_API_KEY)\n",
        "\n",
        "# Function to classify a document\n",
        "def classify_document(document_text):\n",
        "\n",
        "    prompt = f\"\"\"\n",
        "    You are an AI document classifier. Please classify the following document into one of the following categories: World, Sport, Business, Technology, Other.\n",
        "\n",
        "    Document:\n",
        "    \"{document_text}\"\n",
        "\n",
        "    Category:\n",
        "    \"\"\"\n",
        "\n",
        "    response = client.chat.completions.create(\n",
        "        messages=[\n",
        "            {\n",
        "                \"role\": \"user\",\n",
        "                \"content\": prompt,\n",
        "            }\n",
        "        ],\n",
        "        model=\"gpt-4o-mini\",\n",
        "        max_tokens=10,  # Only need a few tokens for the category response\n",
        "        temperature=0  # Lower temperature for deterministic results\n",
        "    )\n",
        "\n",
        "    return response.choices[0].message.content\n",
        "\n",
        "doc_label_dct = {\"World\": 0, \"Sport\": 1, \"Business\": 2, \"Technology\": 3, \"Other\": 4}\n",
        "\n",
        "def add_document_label(row):\n",
        "\n",
        "    text = \" \".join(row['tokens'])\n",
        "    doc_label = classify_document(text)\n",
        "    row[\"doc_labels\"] = doc_label_dct[doc_label.lstrip(\"Category: \")]\n",
        "\n",
        "    return row"
      ],
      "metadata": {
        "id": "5f5oe1-tP6Av"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dataset_train_augmented = dataset_train.map(add_document_label)\n",
        "dataset_valid_augmented = dataset_valid.map(add_document_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5b039c782b534cbd94dd4789a424966a",
            "2f02c995b7a348f08d94fde50c5ec751",
            "c2ca0decdfea4e84968aead57da1b19c",
            "eac823c3c4bd4a5abdfdb290bd321f0c",
            "5ac2fde9d45249c29c524a036ca1253a",
            "c4fcb61dc62d402dacd47317963eeddc",
            "45f089b3b026414fb2244cccc9c214e6",
            "85482b48a6814c9099b29454119cb5b9",
            "aad4c3a210cd4306aa539b78aa532ed3",
            "f9a9aa7ea50e450594b8d5344dbfcaad",
            "11f02c19a6524b109921919aad14216a"
          ]
        },
        "id": "CvgfTerLP2_u",
        "outputId": "fea53dd3-c6cf-4eeb-fc78-37e946a2da44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b039c782b534cbd94dd4789a424966a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the dataset to a directory\n",
        "#dataset_train_augmented.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented\")\n",
        "dataset_valid_augmented.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "694e1132ad9241b9b12d581cdd5dfd14",
            "bbc5018ef5a84654a55db248d57008ce",
            "2a5e3a067b1c4c40870de51249d75978",
            "25635984b71e45d597c46e6ec2af5f56",
            "bdb1bc86bd7346d6952eded5143ad084",
            "a449413a8af444c1b8665a6b336ac03f",
            "16d87519e1f7452b9897c00b54659963",
            "dddc72d757f1466dba034e584bcc26dc",
            "635aade61b8a4de7b242d692a34137da",
            "944e195c2acb434094f52ce3d7909e2e",
            "5102ad847bc14c58a2654288f450b39e"
          ]
        },
        "id": "vuP2FZPvQHTN",
        "outputId": "af53680f-a4ca-4c32-b833-299b5c866fe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "694e1132ad9241b9b12d581cdd5dfd14"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset_train_augmented[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TnegRFvBsrWI",
        "outputId": "7ea3bfbd-9e84-4ef9-b4e3-fae52ba8df47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0], 'doc_labels': 2}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(dataset_train_augmented['doc_labels'])\n",
        "plt.title('Distribution of document labels in train set')\n",
        "plt.xlabel('Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XSlH4JvyQEH0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download previously augmented dataset"
      ],
      "metadata": {
        "id": "d4X8inw3yPN7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_from_disk\n",
        "\n",
        "loaded_train_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented\")\n",
        "loaded_valid_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented\")\n",
        "loaded_test_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_test_augmented\")"
      ],
      "metadata": {
        "id": "p2zaU2qtRWdu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "d3916900-0ed5-487b-f8df-b0ca4f06001b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-cf5565851078>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_from_disk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mloaded_train_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_from_disk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mloaded_valid_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_from_disk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mloaded_test_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_from_disk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/My Drive/Colab Notebooks/conll2003_test_augmented\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_from_disk\u001b[0;34m(dataset_path, keep_in_memory, storage_options)\u001b[0m\n\u001b[1;32m   2212\u001b[0m         \u001b[0mposixpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDATASET_STATE_JSON_FILENAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2213\u001b[0m     ):\n\u001b[0;32m-> 2214\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_disk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_in_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_in_memory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2215\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposixpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDATASETDICT_JSON_FILENAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2216\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mDatasetDict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_disk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_in_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_in_memory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mload_from_disk\u001b[0;34m(dataset_path, keep_in_memory, storage_options)\u001b[0m\n\u001b[1;32m   1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1695\u001b[0m         arrow_table = concat_tables(\n\u001b[0;32m-> 1696\u001b[0;31m             thread_map(\n\u001b[0m\u001b[1;32m   1697\u001b[0m                 \u001b[0mtable_cls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1698\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mposixpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdest_dataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_file\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"filename\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdata_file\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_data_files\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/contrib/concurrent.py\u001b[0m in \u001b[0;36mthread_map\u001b[0;34m(fn, *iterables, **tqdm_kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \"\"\"\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mconcurrent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfutures\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mThreadPoolExecutor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_executor_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mThreadPoolExecutor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0miterables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtqdm_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/contrib/concurrent.py\u001b[0m in \u001b[0;36m_executor_map\u001b[0;34m(PoolExecutor, fn, *iterables, **tqdm_kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m         with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock,\n\u001b[1;32m     50\u001b[0m                           initargs=(lk,)) as ex:\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0miterables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1167\u001b[0m         \u001b[0;31m# (note: keep this check outside the loop for performance)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1168\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1169\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1170\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1171\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    619\u001b[0m                     \u001b[0;31m# Careful not to keep a reference to the popped future\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m                         \u001b[0;32myield\u001b[0m \u001b[0m_result_or_cancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    622\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0m_result_or_cancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m_result_or_cancel\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m             \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    451\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"train:\", loaded_train_dataset)\n",
        "print(\"valid:\", loaded_valid_dataset)\n",
        "print(\"test:\", loaded_test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mE9AFaadRURI",
        "outputId": "248a9ee4-7417-4426-bf15-abf545bafebf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: Dataset({\n",
            "    features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'doc_labels'],\n",
            "    num_rows: 14041\n",
            "})\n",
            "valid: Dataset({\n",
            "    features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'doc_labels'],\n",
            "    num_rows: 3250\n",
            "})\n",
            "test: Dataset({\n",
            "    features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'doc_labels'],\n",
            "    num_rows: 3453\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(3,1,1)\n",
        "plt.hist(loaded_train_dataset['doc_labels'])\n",
        "plt.title('Distribution of document labels in train set')\n",
        "plt.xlabel('Label')\n",
        "plt.yscale('log')\n",
        "plt.show()\n",
        "\n",
        "plt.subplot(3,1,2)\n",
        "plt.hist(loaded_valid_dataset['doc_labels'])\n",
        "plt.title('Distribution of document labels in valid set')\n",
        "plt.xlabel('Label')\n",
        "plt.yscale('log')\n",
        "plt.show()\n",
        "\n",
        "plt.subplot(3,1,3)\n",
        "plt.hist(loaded_test_dataset['doc_labels'])\n",
        "plt.title('Distribution of document labels in test set')\n",
        "plt.xlabel('Label')\n",
        "plt.yscale('log')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "bKuY-H445SZ1",
        "outputId": "0f3d0ac2-8fb4-49eb-89f9-96373ed8e738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-67786e4a4ea1>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Label'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    525\u001b[0m     \"\"\"\n\u001b[1;32m    526\u001b[0m     \u001b[0m_warn_if_gui_out_of_main_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 527\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_get_backend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m             display(\n\u001b[0m\u001b[1;32m     91\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-2>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2156\u001b[0m                 \u001b[0;31m# so that we can inject the orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_draw_disabled\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"tight\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3153\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3154\u001b[0;31m                 mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3155\u001b[0m                     renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3032\u001b[0m                 \u001b[0martists\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3034\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_title_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3036\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxison\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2976\u001b[0m                 \u001b[0mtop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2977\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2978\u001b[0;31m                     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# update offsetText\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2979\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffsetText\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2980\u001b[0m                         \u001b[0mbb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffsetText\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1332\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_minor_ticks\u001b[0;34m(self, numticks)\u001b[0m\n\u001b[1;32m   1650\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1651\u001b[0m             \u001b[0;31m# Update the new tick label properties from the old.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1652\u001b[0;31m             \u001b[0mtick\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1653\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1654\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_copy_tick_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \"_tick_class or reimplement _get_tick()\")\n\u001b[1;32m   1561\u001b[0m         \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_major_tick_kw\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1562\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tick_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_tick_label_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 470\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    471\u001b[0m         \u001b[0;31m# x in axes coords, y in data coords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, size, width, color, tickdir, pad, labelsize, labelcolor, labelfontfamily, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0mGRIDLINE_INTERPOLATION_STEPS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         self.label1 = mtext.Text(\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabelsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabelcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvisible\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel1On\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, text, color, verticalalignment, horizontalalignment, multialignment, fontproperties, rotation, linespacing, rotation_mode, usetex, wrap, transform_rotates_text, parse_math, antialiased, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m         self._reset_visual_defaults(\n\u001b[0m\u001b[1;32m    140\u001b[0m             \u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m             \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_reset_visual_defaults\u001b[0;34m(self, text, color, fontproperties, usetex, parse_math, wrap, verticalalignment, horizontalalignment, multialignment, rotation, transform_rotates_text, linespacing, rotation_mode, antialiased)\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_val_or_rc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"text.color\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_fontproperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontproperties\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_usetex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musetex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_parse_math\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_val_or_rc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparse_math\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'text.parse_math'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mset_fontproperties\u001b[0;34m(self, fp)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mabsolute\u001b[0m \u001b[0mpath\u001b[0m \u001b[0mto\u001b[0m \u001b[0ma\u001b[0m \u001b[0mfont\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m         \"\"\"\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFontProperties\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36m_from_any\u001b[0;34m(cls, arg)\u001b[0m\n\u001b[1;32m    626\u001b[0m         \"\"\"\n\u001b[1;32m    627\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/font_manager.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, family, style, variant, weight, stretch, size, fname, math_fontfamily)\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_variant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_stretch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstretch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "kFZRbZUZ7vxV"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jg7PU4em7m6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downsample in a stratified fashion\n"
      ],
      "metadata": {
        "id": "27YEY0jw77hb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train set\n",
        "train_dataset_cls_3 = loaded_train_dataset.filter(lambda example: example['doc_labels'] == 3)\n",
        "downsampled_loaded_train_dataset = loaded_train_dataset.filter(lambda example: example['doc_labels'] != 3).shuffle().select(range(1000-len(train_dataset_cls_3)))\n",
        "combined_train_dataset = concatenate_datasets([train_dataset_cls_3, downsampled_loaded_train_dataset])\n",
        "\n",
        "# valid set\n",
        "valid_dataset_cls_3 = loaded_valid_dataset.filter(lambda example: example['doc_labels'] == 3)\n",
        "downsampled_loaded_valid_dataset = loaded_valid_dataset.filter(lambda example: example['doc_labels'] != 3).shuffle().select(range(1000-len(valid_dataset_cls_3)))\n",
        "combined_valid_dataset = concatenate_datasets([train_dataset_cls_3, downsampled_loaded_valid_dataset])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "01890bb9651b4c15ae6d4ee66515bea3",
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            "b6f55f6268ed4ad591a99b76d0a9e2a7",
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            "5fe9f8a4ce454b39a5ca5de9148ca97e",
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            "7d7ac628a5394be88b5cc0d7d9e36878"
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        },
        "id": "qCZbHRsn8DyR",
        "outputId": "9f95daff-ed45-4eb7-eb54-b1439e8a26c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01890bb9651b4c15ae6d4ee66515bea3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4f693a7db574096b07174fe7473a20e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/1030 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d72c0c46a63d408aaecb58118c5b603f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/1030 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41ea193849744360ad1b529c56018ebc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(3,1,1)\n",
        "plt.hist(combined_train_dataset['doc_labels'])\n",
        "plt.title('Distribution of document labels in train set')\n",
        "plt.xlabel('Label')\n",
        "plt.yscale('log')\n",
        "plt.show()\n",
        "\n",
        "plt.subplot(3,1,2)\n",
        "plt.hist(combined_valid_dataset['doc_labels'])\n",
        "plt.title('Distribution of document labels in valid set')\n",
        "plt.xlabel('Label')\n",
        "plt.yscale('log')\n",
        "plt.show()\n",
        "\n",
        "plt.subplot(3,1,3)\n",
        "plt.hist(loaded_test_dataset['doc_labels'])\n",
        "plt.title('Distribution of document labels in test set')\n",
        "plt.xlabel('Label')\n",
        "plt.yscale('log')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "YeyWg8Gt9hXL",
        "outputId": "c940b245-d09c-43fe-c423-c3637233e479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "H1Pyt4AT75RS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the dataset to a directory\n",
        "combined_train_dataset.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented_1k_strat\")\n",
        "combined_valid_dataset.save_to_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented_1k_strat\")"
      ],
      "metadata": {
        "id": "VMtKZuPq8tEO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ESXqprQhZeHU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare data"
      ],
      "metadata": {
        "id": "-wPdKlbag2l7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_from_disk\n",
        "\n",
        "loaded_train_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented_1k_strat\")\n",
        "loaded_valid_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented_1k_strat\")\n",
        "loaded_test_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_test_augmented\")"
      ],
      "metadata": {
        "id": "CWJz_dq2Zarw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "sBE0aAiHgsib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'bert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304,
          "referenced_widgets": [
            "2de2bbb9a6564d968f28969bb20f6fe3",
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            "c0e7a23245454faf93819ab7f760697f",
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            "7241aba6679b4a8588c75ab934ccc906",
            "7b9902e6b34e4a678d645b20f356b4e3",
            "bb316c5259f74e9293dde7739359133d",
            "3c7228783e814fcd9a0d19c904b0a7d2",
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            "651994d38950419d9c6f0d7c1e9328b4",
            "edae7e21399c435fb0e63103e1c57981",
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            "edacfe649e6141b1affc0f4abb13f69e",
            "0364e13fb9344ae38e53a96bab4111ce",
            "917237fef3a4482e9f65e67126e3e893",
            "2ea8472977884c8ebd906605b0ae8a4a",
            "39b1dfeb98194e71babc6bc0a0106d89",
            "366421335ef247c99eb2e98af0201a4d",
            "a65196ee138b4e4ba73b68f274697505",
            "a407124d25a94ae09c6fc5ae221d16d4",
            "d105ebc9e5ab48a49ad6a22f994ec1eb",
            "bdd98a2632eb458d87a7c24fb4c1e405",
            "ae1fa761eff145a898fc4c5977d3f118",
            "e0d790c1dcb44bb2a3993794c6318e76"
          ]
        },
        "id": "COKCLGvBhK4T",
        "outputId": "824b6282-9558-40da-8d8c-076e4395fb30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2de2bbb9a6564d968f28969bb20f6fe3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c7228783e814fcd9a0d19c904b0a7d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9b69820fd3d454aa97dec5c8979a6ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0364e13fb9344ae38e53a96bab4111ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "a-IkAXYTVwIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, texts, labels, tokenizer, max_length):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = \" \".join(self.texts[idx])\n",
        "        label = self.labels[idx]\n",
        "\n",
        "        encoding = self.tokenizer(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_length,\n",
        "            truncation=True,\n",
        "            padding=\"max_length\",\n",
        "            return_tensors=\"pt\"\n",
        "        )\n",
        "\n",
        "        return {\n",
        "            'input_ids': encoding['input_ids'].squeeze(),\n",
        "            'attention_mask': encoding['attention_mask'].squeeze(),\n",
        "            'label': torch.tensor(label)\n",
        "        }\n"
      ],
      "metadata": {
        "id": "Kg_7sM5pgrqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_custom_dataset = CustomDataset(\n",
        "    texts=combined_train_dataset['tokens'],\n",
        "    labels=combined_train_dataset['doc_labels'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")\n",
        "\n",
        "valid_custom_dataset = CustomDataset(\n",
        "    texts=combined_valid_dataset['tokens'],\n",
        "    labels=combined_valid_dataset['doc_labels'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")"
      ],
      "metadata": {
        "id": "qoVigJZug_2J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "e42e94d9-3265-4c47-c5c5-e909167028a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'combined_train_dataset' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-49238d056161>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m train_custom_dataset = CustomDataset(\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mtexts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcombined_train_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tokens'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcombined_train_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'doc_labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'combined_train_dataset' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune model"
      ],
      "metadata": {
        "id": "CDGv8iclhU-z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# log results and assets to Comet\n",
        "os.environ[\"COMET_LOG_ASSETS\"] = \"True\"\n",
        "os.environ[\"COMET_WORKSPACE\"] = \"gonzaq94\"\n",
        "os.environ[\"COMET_PROJECT_NAME\"] = \"ner_classif-bert-doc_level_annots\""
      ],
      "metadata": {
        "id": "HvtvSbF7hpc2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_label_dct = {\"World\": 0, \"Sport\": 1, \"Business\": 2, \"Technology\": 3, \"Other\": 4}\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name,\n",
        "                                                           num_labels=len(doc_label_dct),\n",
        "                                                           attention_probs_dropout_prob=0.0,\n",
        "                                                           hidden_dropout_prob=0.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kydAi0ZhhOBr",
        "outputId": "6fb29637-d487-485a-a377-42d61902b1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall\n",
        "    }"
      ],
      "metadata": {
        "id": "opvL4PdNh8BS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "experiment = comet_ml.Experiment(api_key=os.getenv(\"COMET_API_KEY\"), project_name=os.getenv(\"COMET_PROJECT_NAME\"), workspace=os.getenv(\"COMET_WORKSPACE\"))\n",
        "\n",
        "# Define a custom callback to ensure experiment.end() is called\n",
        "class CometCallback(TrainerCallback):\n",
        "    def __init__(self, experiment):\n",
        "        self.experiment = experiment\n",
        "\n",
        "    def on_train_end(self, args, state, control, **kwargs):\n",
        "        self.experiment.end()  # Ends the experiment properly when training ends\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    num_train_epochs=15,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"f1\",\n",
        "    greater_is_better=True,\n",
        "    save_total_limit=1,\n",
        "    per_device_train_batch_size=24,\n",
        "    per_device_eval_batch_size=16,\n",
        "    warmup_steps=63,\n",
        "    learning_rate=2e-5,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir=None,\n",
        "    dataloader_drop_last=False,\n",
        "    fp16=True, # mixed precision\n",
        "    report_to=[\"comet_ml\"]\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_custom_dataset,\n",
        "    eval_dataset=valid_custom_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[CometCallback(experiment)]\n",
        ")\n"
      ],
      "metadata": {
        "id": "-0p4WrDSh9st",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "632ff41b-26cc-49a8-ff99-2491bfc5b244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml ExistingExperiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : balanced batches\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-doc-level-annots-strat-data/07ec6a340cd4400d868089ee6b349f63\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-doc-level-annots/103d9447d1c84ffca636ff3a76856e3b\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "A4sNazL8iEGF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "6406e0df-6b55-4a17-d597-8120222dc417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='155' max='8790' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 155/8790 00:38 < 36:39, 3.93 it/s, Epoch 0.26/15]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1936\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1938\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1939\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1940\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2339\u001b[0m                             \u001b[0mgrad_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_grad_norm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2341\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2343\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_optimizer_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/optimizer.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    157\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_patched_step_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/grad_scaler.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, optimizer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m         ), \"No inf checks were recorded for this optimizer.\"\n\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_opt_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[0moptimizer_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stage\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOptState\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSTEPPED\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/grad_scaler.py\u001b[0m in \u001b[0;36m_maybe_opt_step\u001b[0;34m(self, optimizer, optimizer_state, *args, **kwargs)\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0mretval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizer_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"found_inf_per_device\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m             \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mretval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/optimizer.py\u001b[0m in \u001b[0;36mpatched_step\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpatched_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0maccelerated_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accelerate_step_called\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpatched_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    135\u001b[0m                     \u001b[0mopt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopt_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m                     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opt_called\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m  \u001b[0;31m# type: ignore[union-attr]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m                 \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrapped_by_lr_sched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                             )\n\u001b[1;32m    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    488\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36m_use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"differentiable\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_break\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_break\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adamw.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    218\u001b[0m             )\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             adamw(\n\u001b[0m\u001b[1;32m    221\u001b[0m                 \u001b[0mparams_with_grad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mmaybe_fallback\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    152\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mdisabled_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_fallback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adamw.py\u001b[0m in \u001b[0;36madamw\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_single_tensor_adamw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m     func(\n\u001b[0m\u001b[1;32m    783\u001b[0m         \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m         \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adamw.py\u001b[0m in \u001b[0;36m_multi_tensor_adamw\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable, has_complex)\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_foreach_lerp_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_exp_avgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_grads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_foreach_mul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_exp_avg_sqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m         torch._foreach_addcmul_(\n\u001b[1;32m    535\u001b[0m             \u001b[0mdevice_exp_avg_sqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_grads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_grads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for elem in train_custom_dataset:\n",
        "  print(elem['input_ids'].size())\n",
        "  break"
      ],
      "metadata": {
        "id": "r-tOLiCT1UP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate model"
      ],
      "metadata": {
        "id": "BOaDiA6TjvFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, class_names):\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
        "    plt.title('Normalized confusion Matrix')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.show()\n",
        "\n",
        "def compute_metrics_eval(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "    # Calculate confusion matrix\n",
        "    conf_matrix = confusion_matrix(true_labels, true_predictions, normalize='true')\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'confusion_matrix': conf_matrix\n",
        "    }\n",
        "\n",
        "\n",
        "class Evaluator:\n",
        "    def __init__(self, model, tokenizer, compute_metrics):\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.compute_metrics = compute_metrics\n",
        "\n",
        "    def evaluate(self, eval_dataset, batch_size=16):\n",
        "        trainer = Trainer(\n",
        "            model=self.model,\n",
        "            args=training_args,\n",
        "            compute_metrics=self.compute_metrics,\n",
        "            eval_dataset=eval_dataset,\n",
        "        )\n",
        "        return trainer.evaluate()"
      ],
      "metadata": {
        "id": "lD6D9w8Rjw1e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluator = Evaluator(model, tokenizer, compute_metrics_eval)\n",
        "eval_results = evaluator.evaluate(valid_custom_dataset)"
      ],
      "metadata": {
        "id": "sxKT95L3kETM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "98c62e6b-a368-40f5-ca0a-4578e7fd3f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'training_args' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-c5c39282afb0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mevaluator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEvaluator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_metrics_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meval_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_custom_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-19-8612350143cf>\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, eval_dataset, batch_size)\u001b[0m\n\u001b[1;32m     39\u001b[0m         trainer = Trainer(\n\u001b[1;32m     40\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m             \u001b[0mcompute_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0meval_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'training_args' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc_label_dct = {\"World\": 0, \"Sport\": 1, \"Business\": 2, \"Technology\": 3, \"Other\": 4}\n",
        "class_names = [k for k, v in doc_label_dct.items()]\n",
        "plot_confusion_matrix(eval_results['eval_confusion_matrix'], class_names)"
      ],
      "metadata": {
        "id": "9N85wvcEjzAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rtW_2trEkaaT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune stratified data"
      ],
      "metadata": {
        "id": "Scx1gDBH99Uc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_from_disk\n",
        "\n",
        "loaded_train_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_train_augmented_1k_strat\")\n",
        "loaded_valid_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_valid_augmented_1k_strat\")\n",
        "loaded_test_dataset = load_from_disk(\"/content/drive/My Drive/Colab Notebooks/conll2003_test_augmented\")"
      ],
      "metadata": {
        "id": "Yy9gFeRj9_kZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# log results and assets to Comet\n",
        "os.environ[\"COMET_LOG_ASSETS\"] = \"True\"\n",
        "os.environ[\"COMET_WORKSPACE\"] = \"gonzaq94\"\n",
        "os.environ[\"COMET_PROJECT_NAME\"] = \"ner_classif-bert-doc_level_annots-strat_data\""
      ],
      "metadata": {
        "id": "7QBoCOIN-IlH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'bert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, texts, labels, tokenizer, max_length):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = \" \".join(self.texts[idx])\n",
        "        label = self.labels[idx]\n",
        "\n",
        "        encoding = self.tokenizer(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_length,\n",
        "            truncation=True,\n",
        "            padding=\"max_length\",\n",
        "            return_tensors=\"pt\"\n",
        "        )\n",
        "\n",
        "        return {\n",
        "            'input_ids': encoding['input_ids'].squeeze(),\n",
        "            'attention_mask': encoding['attention_mask'].squeeze(),\n",
        "            'label': torch.tensor(label)\n",
        "        }\n"
      ],
      "metadata": {
        "id": "IBHr5FPeVjf_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_custom_dataset = CustomDataset(\n",
        "    texts=loaded_train_dataset['tokens'],\n",
        "    labels=loaded_train_dataset['doc_labels'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")\n",
        "\n",
        "valid_custom_dataset = CustomDataset(\n",
        "    texts=loaded_valid_dataset['tokens'],\n",
        "    labels=loaded_valid_dataset['doc_labels'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")\n",
        "\n",
        "test_custom_dataset = CustomDataset(\n",
        "    texts=loaded_test_dataset['tokens'],\n",
        "    labels=loaded_test_dataset['doc_labels'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")"
      ],
      "metadata": {
        "id": "oHGiDOEgV1MZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_label_dct = {\"World\": 0, \"Sport\": 1, \"Business\": 2, \"Technology\": 3, \"Other\": 4}\n",
        "\n",
        "model_name = 'bert-base-uncased'\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name,\n",
        "                                                           num_labels=len(doc_label_dct),\n",
        "                                                           attention_probs_dropout_prob=0.0,\n",
        "                                                           hidden_dropout_prob=0.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27jWqQ5r-LMe",
        "outputId": "0fbb28ae-fe4a-4cde-c105-0f653a7a452b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall\n",
        "    }"
      ],
      "metadata": {
        "id": "V4_dj9hV-mnt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_freqs = torch.tensor(np.histogram(loaded_train_dataset['doc_labels'], bins=5)[0]).to(\"cuda\")\n",
        "class_weights = 1.0 / class_freqs\n",
        "class_weights = class_weights / class_weights.sum()\n",
        "print(class_weights, class_freqs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0TupMG5H1lK",
        "outputId": "65519185-09cf-4997-828f-576181130963"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0.1638, 0.0662, 0.1502, 0.5709, 0.0490], device='cuda:0') tensor([122, 302, 133,  35, 408], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader, WeightedRandomSampler\n",
        "\n",
        "# weighted loss\n",
        "class WeightedTrainer(Trainer):\n",
        "  def compute_loss(self, model, inputs, num_items_in_batch=None, return_outputs=False):\n",
        "      labels = inputs.get(\"labels\")\n",
        "      outputs = model(**inputs)\n",
        "      logits = outputs.get(\"logits\")\n",
        "\n",
        "      loss_fct = CrossEntropyLoss(weight=class_weights)\n",
        "      loss = loss_fct(logits.view(-1, len(class_weights)), labels.view(-1))\n",
        "\n",
        "      return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "\n",
        "# weighted data loader\n",
        "sampler = WeightedRandomSampler(weights=class_weights, num_samples=len(class_weights), replacement=True)\n",
        "\n",
        "def torch_weighted_data_collator(features):\n",
        "\n",
        "    # Sample features with weights according to their label\n",
        "    labels = [f[\"label\"] for f in features]\n",
        "    weights = [class_weights[label] for label in labels]\n",
        "    total_weight = sum(weights)\n",
        "    probabilities = [weight / total_weight for weight in weights]\n",
        "\n",
        "    # Sample with probabilities\n",
        "    features = random.choices(features, weights=probabilities, k=len(features))\n",
        "\n",
        "    batch = {}\n",
        "\n",
        "    # Convert features to tensors\n",
        "    for k in features[0].keys():\n",
        "        if k == 'label':\n",
        "            newk = 'labels'\n",
        "        else:\n",
        "            newk = k\n",
        "        if isinstance(features[0][k], torch.Tensor):\n",
        "            batch[newk] = torch.stack([f[k] for f in features])  # Use stack for tensors\n",
        "        else:\n",
        "            batch[newk] = torch.tensor([f[k] for f in features])  # Convert to tensor\n",
        "\n",
        "    return batch\n",
        "\n",
        "def torch_default_data_collator(features):\n",
        "\n",
        "    first = features[0]\n",
        "    batch = {}\n",
        "\n",
        "    # Special handling for labels.\n",
        "    # Ensure that tensor is created with the correct type\n",
        "    # (it should be automatically the case, but let's make sure of it.)\n",
        "    if \"label\" in first and first[\"label\"] is not None:\n",
        "        label = first[\"label\"].item() if isinstance(first[\"label\"], torch.Tensor) else first[\"label\"]\n",
        "        dtype = torch.long if isinstance(label, int) else torch.float\n",
        "        batch[\"labels\"] = torch.tensor([f[\"label\"] for f in features], dtype=dtype)\n",
        "    elif \"label_ids\" in first and first[\"label_ids\"] is not None:\n",
        "        if isinstance(first[\"label_ids\"], torch.Tensor):\n",
        "            batch[\"labels\"] = torch.stack([f[\"label_ids\"] for f in features])\n",
        "        else:\n",
        "            dtype = torch.long if isinstance(first[\"label_ids\"][0], int) else torch.float\n",
        "            batch[\"labels\"] = torch.tensor([f[\"label_ids\"] for f in features], dtype=dtype)\n",
        "\n",
        "    # Handling of all other possible keys.\n",
        "    # Again, we will use the first element to figure out which key/values are not None for this model.\n",
        "    for k, v in first.items():\n",
        "        if k not in (\"label\", \"label_ids\") and v is not None and not isinstance(v, str):\n",
        "            if isinstance(v, torch.Tensor):\n",
        "                batch[k] = torch.stack([f[k] for f in features])\n",
        "            elif isinstance(v, np.ndarray):\n",
        "                batch[k] = torch.from_numpy(np.stack([f[k] for f in features]))\n",
        "            else:\n",
        "                batch[k] = torch.tensor([f[k] for f in features])\n",
        "\n",
        "    return batch"
      ],
      "metadata": {
        "id": "J2s9q0sfHK-0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "experiment = comet_ml.Experiment(api_key=os.getenv(\"COMET_API_KEY\"), project_name=os.getenv(\"COMET_PROJECT_NAME\"), workspace=os.getenv(\"COMET_WORKSPACE\"))\n",
        "\n",
        "# Define a custom callback to ensure experiment.end() is called\n",
        "class CometCallback(TrainerCallback):\n",
        "    def __init__(self, experiment):\n",
        "        self.experiment = experiment\n",
        "\n",
        "    def on_train_end(self, args, state, control, **kwargs):\n",
        "        self.experiment.end()  # Ends the experiment properly when training ends\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    num_train_epochs=15,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"f1\",\n",
        "    greater_is_better=True,\n",
        "    save_total_limit=1,\n",
        "    per_device_train_batch_size=24,\n",
        "    per_device_eval_batch_size=16,\n",
        "    warmup_steps=63,\n",
        "    learning_rate=2e-5,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    weight_decay=0.01,\n",
        "    logging_dir=None,\n",
        "    dataloader_drop_last=False,\n",
        "    fp16=True, # mixed precision\n",
        "    report_to=[\"comet_ml\"]\n",
        ")\n",
        "\n",
        "trainer = WeightedTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_custom_dataset,\n",
        "    eval_dataset=valid_custom_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    #compute_loss_func=compute_loss,\n",
        "    data_collator=torch_weighted_data_collator\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MA0D_bgy-pqv",
        "outputId": "6433d6cb-f674-4275-87c8-13a47b5a0f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : written_trellis_6255\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-doc-level-annots-strat-data/faf8484751af4911afd02c61713151c1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [2]               : (0.8334781349551115, 0.8533980582524272)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_confusion_matrix           : [[9.16317992e-01 1.25523013e-02 2.51046025e-02 0.00000000e+00\n",
            "  4.60251046e-02]\n",
            " [1.56372166e-03 8.98358092e-01 7.81860829e-04 0.00000000e+00\n",
            "  9.92963253e-02]\n",
            " [5.48245614e-02 4.38596491e-03 8.55263158e-01 4.38596491e-03\n",
            "  8.11403509e-02]\n",
            " [0.00000000e+00 0.00000000e+00 2.14285714e-01 5.71428571e-01\n",
            "  2.14285714e-01]\n",
            " [7.44027304e-02 1.21501706e-01 4.36860068e-02 1.36518771e-03\n",
            "  7.59044369e-01]]\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [2]                     : (0.8339902590250613, 0.8544374936526783)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [2]                   : (0.44833827018737793, 0.5044994354248047)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_model_preparation_time [2] : (0.0061, 0.0077)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [2]              : (0.8416399874694014, 0.858938873821347)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [2]                 : (0.8334781349551115, 0.8533980582524272)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [2]                : (4.2636, 13.7648)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [2]     : (241.581, 250.857)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [2]       : (15.245, 15.692)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_doc_level_annots.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 2e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov03_09-13-12_34a5c7980b00\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : linear\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 15\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['comet_ml']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 63\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : ./deploy/checkpoint-336\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForSequenceClassification']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : single_label_classification\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : float32\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-doc-level-annots-strat-data/b5163229a9f4498bb5250bfe8e264c7e\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "dZuah_E6-re5",
        "outputId": "69d2ef43-9812-4873-f0fb-4e5ace0f5e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='630' max='630' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [630/630 06:56, Epoch 15/15]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.486900</td>\n",
              "      <td>1.524992</td>\n",
              "      <td>0.246602</td>\n",
              "      <td>0.300888</td>\n",
              "      <td>0.669320</td>\n",
              "      <td>0.246602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.162700</td>\n",
              "      <td>0.943489</td>\n",
              "      <td>0.725243</td>\n",
              "      <td>0.687734</td>\n",
              "      <td>0.797345</td>\n",
              "      <td>0.725243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.604800</td>\n",
              "      <td>0.512404</td>\n",
              "      <td>0.788350</td>\n",
              "      <td>0.745636</td>\n",
              "      <td>0.841016</td>\n",
              "      <td>0.788350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.281500</td>\n",
              "      <td>0.355001</td>\n",
              "      <td>0.846602</td>\n",
              "      <td>0.838321</td>\n",
              "      <td>0.850453</td>\n",
              "      <td>0.846602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.122900</td>\n",
              "      <td>0.319327</td>\n",
              "      <td>0.874757</td>\n",
              "      <td>0.875973</td>\n",
              "      <td>0.878188</td>\n",
              "      <td>0.874757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.079100</td>\n",
              "      <td>0.331729</td>\n",
              "      <td>0.870874</td>\n",
              "      <td>0.875622</td>\n",
              "      <td>0.889026</td>\n",
              "      <td>0.870874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.049000</td>\n",
              "      <td>0.303910</td>\n",
              "      <td>0.873786</td>\n",
              "      <td>0.877310</td>\n",
              "      <td>0.886287</td>\n",
              "      <td>0.873786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.044000</td>\n",
              "      <td>0.329623</td>\n",
              "      <td>0.879612</td>\n",
              "      <td>0.883973</td>\n",
              "      <td>0.899113</td>\n",
              "      <td>0.879612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.027300</td>\n",
              "      <td>0.353356</td>\n",
              "      <td>0.878641</td>\n",
              "      <td>0.883001</td>\n",
              "      <td>0.895149</td>\n",
              "      <td>0.878641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.019000</td>\n",
              "      <td>0.412960</td>\n",
              "      <td>0.855340</td>\n",
              "      <td>0.859606</td>\n",
              "      <td>0.869277</td>\n",
              "      <td>0.855340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.013400</td>\n",
              "      <td>0.463171</td>\n",
              "      <td>0.872816</td>\n",
              "      <td>0.877472</td>\n",
              "      <td>0.892178</td>\n",
              "      <td>0.872816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.006500</td>\n",
              "      <td>0.401393</td>\n",
              "      <td>0.869903</td>\n",
              "      <td>0.873989</td>\n",
              "      <td>0.883838</td>\n",
              "      <td>0.869903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.005800</td>\n",
              "      <td>0.382700</td>\n",
              "      <td>0.879612</td>\n",
              "      <td>0.882898</td>\n",
              "      <td>0.890810</td>\n",
              "      <td>0.879612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.409092</td>\n",
              "      <td>0.859223</td>\n",
              "      <td>0.864605</td>\n",
              "      <td>0.883987</td>\n",
              "      <td>0.859223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.005700</td>\n",
              "      <td>0.428948</td>\n",
              "      <td>0.854369</td>\n",
              "      <td>0.859371</td>\n",
              "      <td>0.872412</td>\n",
              "      <td>0.854369</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=630, training_loss=0.26133333905821754, metrics={'train_runtime': 400.7718, 'train_samples_per_second': 37.428, 'train_steps_per_second': 1.572, 'total_flos': 1973386068480000.0, 'train_loss': 0.26133333905821754, 'epoch': 15.0})"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "experiment = comet_ml.ExistingExperiment(api_key=os.getenv(\"COMET_API_KEY\"), previous_experiment=\"b5163229a9f4498bb5250bfe8e264c7e\")\n",
        "experiment.log_model(\"BERT-base-doc_classif-1k-stratified-wloss_wsampling-cos_ann\", \"results/checkpoint-336\")\n",
        "experiment.register_model(\"BERT-base-doc_classif-1k-stratified-wloss_wsampling-cos_ann\")"
      ],
      "metadata": {
        "id": "vA5Tk-cqdLuF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28684707-2b42-458e-aade-2a1c909e929a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : frightened_apricot_5344\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-doc-level-annots-strat-data/b5163229a9f4498bb5250bfe8e264c7e\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epoch [31]                   : (1.0, 15.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [15]           : (0.24660194174757283, 0.8796116504854369)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [15]                 : (0.3008875445810343, 0.8839729131071746)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [15]               : (0.3039099872112274, 1.524991750717163)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [15]          : (0.6693201569376076, 0.8991127672593024)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [15]             : (0.24660194174757283, 0.8796116504854369)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [15]            : (3.7182, 4.6998)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [15] : (219.158, 277.014)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [15]   : (13.83, 17.481)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     grad_norm [15]               : (0.03937815874814987, 12.573273658752441)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     learning_rate [15]           : (1.3814530889433298e-09, 1.9950307753654016e-05)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [79]                    : (0.002025780500844121, 1.664706826210022)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     total_flos                   : 1973386068480000.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss                   : 0.26133333905821754\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_runtime                : 400.7718\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_samples_per_second     : 37.428\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_steps_per_second       : 1.572\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_doc_level_annots.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 2e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov03_09-32-45_34a5c7980b00\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : cosine\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 15\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['comet_ml']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : epoch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 63\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : bert-base-uncased\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForMaskedLM']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 7 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-doc-level-annots-strat-data/b5163229a9f4498bb5250bfe8e264c7e\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate best model"
      ],
      "metadata": {
        "id": "XrFYvnNyYHNd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from comet_ml import API\n",
        "\n",
        "api = API(api_key=os.getenv(\"COMET_API_KEY\"))\n",
        "\n",
        "# model name\n",
        "model_name = \"BERT-base-doc_classif-1k-stratified-wloss_wsampling\"\n",
        "\n",
        "#get the Model object\n",
        "model = api.get_model(workspace=os.getenv(\"COMET_WORKSPACE\"), model_name=model_name)\n",
        "\n",
        "# Download a Registry Model:\n",
        "model.download(\"1.0.0\", \"./deploy\", expand=True)"
      ],
      "metadata": {
        "id": "QB0ZoajpdTG_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22b9281b-7ed3-4a32-ef23-c14a39f36a36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'gonzaq94/BERT-base-doc_classif-1k-stratified-wloss_wsampling:1.0.0' download has been started asynchronously.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 14 file(s), remaining 2.45 GB/2.45 GB\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/training_args.bin' has been overwritten by asset '1c35cbc477b844b5998f8fd742e9820a' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/scheduler.pt' has been overwritten by asset '4e36675ca40b48dbb2d1cb5b0182695e' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/trainer_state.json' has been overwritten by asset '3acd262a0cae466482c69fa6c3a32ae0' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/scheduler.pt' has been overwritten by asset '532308c9cdf4429088bfadfbf7b62421' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/config.json' has been overwritten by asset '5edf49a92c5241b7b411f1b5eafd967a' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/optimizer.pt' has been overwritten by asset '180b842b308f4ddbb349c42a22e6f6f7' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/optimizer.pt' has been overwritten by asset '72897f55e64048e1b39bf78d0815d334' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/model.safetensors' has been overwritten by asset '9c13f83d83734f8abddfd98eb74dd9cf' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/model.safetensors' has been overwritten by asset '6f39046ab22145e491f548b5a848c394' of remote model\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 9 file(s), remaining 1.46 GB/2.45 GB, Throughput 67.53 MB/s, ETA ~23s\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/trainer_state.json' has been overwritten by asset 'a47e5046840a4ab6b8e7c694503cb3a4' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/config.json' has been overwritten by asset 'be474d3eb17744ec99748ebe626e550f' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/rng_state.pth' has been overwritten by asset 'e56ceaf701e34622b5aac660ab3f36f7' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/training_args.bin' has been overwritten by asset 'f57cb0e5b9e74328a3525b8dc2f41257' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-336/rng_state.pth' has been overwritten by asset 'ffc5b02f083d4cb58b65586d374b914d' of remote model\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 2 file(s), remaining 529.95 MB/2.45 GB, Throughput 64.09 MB/s, ETA ~9s\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'gonzaq94/BERT-base-doc_classif-1k-stratified-wloss_wsampling:1.0.0' has been successfully downloaded.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Downloaded asset files is in './deploy' folder.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, class_names):\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
        "    plt.title('Normalized confusion Matrix')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.show()\n",
        "\n",
        "def compute_metrics_eval(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "    # Calculate confusion matrix\n",
        "    conf_matrix = confusion_matrix(true_labels, true_predictions, normalize='true')\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'confusion_matrix': conf_matrix\n",
        "    }\n",
        "\n",
        "\n",
        "class Evaluator:\n",
        "    def __init__(self, model, tokenizer, compute_metrics):\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.compute_metrics = compute_metrics\n",
        "\n",
        "    def evaluate(self, eval_dataset, batch_size=16):\n",
        "        trainer = Trainer(\n",
        "            model=self.model,\n",
        "            args=training_args,\n",
        "            compute_metrics=self.compute_metrics,\n",
        "            eval_dataset=eval_dataset,\n",
        "        )\n",
        "        return trainer.evaluate()"
      ],
      "metadata": {
        "id": "EIEhSlXkVAyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_label_dct = {\"World\": 0, \"Sport\": 1, \"Business\": 2, \"Technology\": 3, \"Other\": 4}\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"./deploy/checkpoint-336\", num_labels=len(doc_label_dct))\n"
      ],
      "metadata": {
        "id": "4ifpWVtRWpbs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluator = Evaluator(model, tokenizer, compute_metrics_eval)\n",
        "eval_results = evaluator.evaluate(test_custom_dataset)"
      ],
      "metadata": {
        "id": "pB2auBbxYP1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "88cc49fc-4fab-44a9-f02a-8f3240ff5349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='216' max='216' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [216/216 00:11]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Cannot safely convert array([[9.16317992e-01, 1.25523013e-02, 2.51046025e-02, 0.00000000e+00,\n",
            "        4.60251046e-02],\n",
            "       [1.56372166e-03, 8.98358092e-01, 7.81860829e-04, 0.00000000e+00,\n",
            "        9.92963253e-02],\n",
            "       [5.48245614e-02, 4.38596491e-03, 8.55263158e-01, 4.38596491e-03,\n",
            "        8.11403509e-02],\n",
            "       [0.00000000e+00, 0.00000000e+00, 2.14285714e-01, 5.71428571e-01,\n",
            "        2.14285714e-01],\n",
            "       [7.44027304e-02, 1.21501706e-01, 4.36860068e-02, 1.36518771e-03,\n",
            "        7.59044369e-01]]) object to a scalar value, using its string representation for logging. Resulting string might be invalid\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = [k for k, v in doc_label_dct.items()]\n",
        "plot_confusion_matrix(eval_results['eval_confusion_matrix'], class_names)"
      ],
      "metadata": {
        "id": "RPr0yd6CYSc_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "5c1622c1-d8e9-45b4-82c1-8ea3e2f82b5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in eval_results.items():\n",
        "  if key != 'eval_confusion_matrix':\n",
        "    print(f\"{key}: {value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmKij6pBX-tk",
        "outputId": "bbd70ada-b341-4960-ec72-19b30d685266"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "eval_loss: 0.5044994354248047\n",
            "eval_model_preparation_time: 0.0035\n",
            "eval_accuracy: 0.8334781349551115\n",
            "eval_f1: 0.8339902590250613\n",
            "eval_precision: 0.8416399874694014\n",
            "eval_recall: 0.8334781349551115\n",
            "eval_runtime: 12.0451\n",
            "eval_samples_per_second: 286.674\n",
            "eval_steps_per_second: 17.933\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dS8kKb95XW9O"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}