Task_3.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/prajeet26/NER-LLM/blob/main/Task_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **QLoRa Fintuned Automative NER**"
      ],
      "metadata": {
        "id": "-7hYqUsxYQtl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### imports"
      ],
      "metadata": {
        "id": "NT-8d3vZYG4W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "XQV4g6JfeR1_",
        "outputId": "f2fc4131-190e-4801-8708-3ae3db3d3b45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install auto-gptq\n",
        "!pip install optimum\n",
        "!pip install bitsandbytes"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeaOnjP2zsy-",
        "outputId": "5696537c-5bf8-486a-ce0e-32ebe07c60ef"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting auto-gptq\n",
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            "\u001b[?25hCollecting datasets (from auto-gptq)\n",
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            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
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            "Collecting peft>=0.5.0 (from auto-gptq)\n",
            "  Downloading peft-0.9.0-py3-none-any.whl (190 kB)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->auto-gptq) (3.1.3)\n",
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            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->auto-gptq) (0.15.2)\n",
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            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets->auto-gptq) (0.6)\n",
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            "Installing collected packages: rouge, gekko, dill, multiprocess, accelerate, datasets, peft, auto-gptq\n",
            "Successfully installed accelerate-0.27.2 auto-gptq-0.7.1 datasets-2.18.0 dill-0.3.8 gekko-1.0.6 multiprocess-0.70.16 peft-0.9.0 rouge-1.0.1\n",
            "Collecting optimum\n",
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            "Installing collected packages: humanfriendly, coloredlogs, optimum\n",
            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 optimum-1.17.1\n",
            "Collecting bitsandbytes\n",
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            "Installing collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.42.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "from peft import prepare_model_for_kbit_training\n",
        "from peft import LoraConfig, get_peft_model\n",
        "from datasets import load_dataset\n",
        "import transformers"
      ],
      "metadata": {
        "id": "tfSl5xRs0y8J"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load model"
      ],
      "metadata": {
        "id": "VVstnrh-0m2x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "82wnm7NnyKzZ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"openaccess-ai-collective/tiny-mistral\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map=\"auto\",\n",
        "                                             trust_remote_code=False,\n",
        "                                             revision=\"main\")"
      ],
      "metadata": {
        "id": "GFcKal6c96su"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Huq1b5LMyfFt",
        "outputId": "0c928511-ffdd-4940-abd3-07aadc2249d5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Mar  5 22:44:30 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   48C    P0              26W /  70W |  11619MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load tokenizer"
      ],
      "metadata": {
        "id": "bCKC3_yl0pNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)"
      ],
      "metadata": {
        "id": "c3CIa8C80Vtm",
        "outputId": "fe311da8-7f93-4f26-ee1e-24840b6eae50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "15382e84f20045ec86de09e30283c2b0",
            "46ae01194a084dcaace027197fe84e61",
            "804bb8f366d34be5883affdf8e4ebab0",
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            "1fda529950194e58868ce56caeb13f82",
            "a12ac960c8744ad09fcfc973c324f20b"
          ]
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01a58d9a9bcf450db61596b284526e2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5cb2a512391644c1899432a908fcb563"
            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3a36c58d92a4254b97d150e49903fc9"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da0658d891f240528ba33fa1ceca63a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4eb2dafa3cf435e8a580a2b0ed2f06a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Base Model"
      ],
      "metadata": {
        "id": "VnFiyBOc329l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Prompt Engineering"
      ],
      "metadata": {
        "id": "M7XAi8rtC5a9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare Model for Training"
      ],
      "metadata": {
        "id": "nXc8pBsVIy2N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.train()\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "K3PesDIyI1Hn"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=32,\n",
        "    lora_alpha=64,\n",
        "    target_modules=[\n",
        "        \"q_proj\",\n",
        "        \"k_proj\",\n",
        "        \"v_proj\",\n",
        "        \"o_proj\",\n",
        "        \"gate_proj\",\n",
        "        \"up_proj\",\n",
        "        \"down_proj\",\n",
        "        \"lm_head\",\n",
        "    ],\n",
        "    bias=\"none\",\n",
        "    lora_dropout=0.05,  # Conventional\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)"
      ],
      "metadata": {
        "id": "iXOHXZcII5FS"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preparing Training Dataset"
      ],
      "metadata": {
        "id": "tssCKXx3hUqM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"/content/combined.csv\")\n",
        "data"
      ],
      "metadata": {
        "id": "TqWGy2UBI8bo",
        "outputId": "fbb262b4-5390-4778-84ce-1c6a41d91171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        combined_text  \\\n",
              "0   DESCDEF: ON CERTAIN PULL, FIFTH WHEEL AND TOY-...   \n",
              "1   DESCDEF: E-TON IS RECALLING CERTAIN MODEL YEAR...   \n",
              "2   DESCDEF: NAVISTAR IS RECALLING 51,588 HEAVY TR...   \n",
              "3   DESCDEF: Nissan North America, Inc. (Nissan) i...   \n",
              "4   DESCDEF: Altec Industries, Inc. is recalling c...   \n",
              "5   DESCDEF: KZRV, L.P. (KZRV) is recalling variou...   \n",
              "6   DESCDEF: ON CERTAIN BUMPER-PULL HORSE TRAILERS...   \n",
              "7   DESCDEF: ON CERTAIN PASSENGER VEHICLES EQUIPPE...   \n",
              "8   DESCDEF: ON CERTAIN HEAVY DUTY CLASS 8 TRUCKS,...   \n",
              "9   DESCDEF: ON CERTAIN FIRE TRUCKS WITH TANDEM ST...   \n",
              "10  DESCDEF: Thor Motor Coach (Thor) is recalling ...   \n",
              "11  DESCDEF: SKYLINE IS RECALLING 22,372 MY 2004-2...   \n",
              "12  DESCDEF: Cruiser RV is recalling certain model...   \n",
              "13  DESCDEF: CERTAIN MAXZONE HEADLIGHT ASSEMBLIES,...   \n",
              "14  DESCDEF: CERTAIN DELPHI FUEL PRESSURE REGULATO...   \n",
              "15  DESCDEF: FEDERAL MOGUL IS RECALLING 6,903 AFTE...   \n",
              "16  DESCDEF: ShowHauler Trucks, Inc. (ShowHauler) ...   \n",
              "17  DESCDEF: REV Recreation Group (REV) is recalli...   \n",
              "18  DESCDEF: Subaru of America, Inc. (Subaru) is r...   \n",
              "19  DESCDEF: Yamaha Motor Corporation USA (Yamaha)...   \n",
              "20  DESCDEF: Farber Specialty Vehicles (Farber) is...   \n",
              "21  DESCDEF: Pierce Manufacturing (Pierce) is reca...   \n",
              "22  DESCDEF: Winnebago Industries, Inc (Winnebago)...   \n",
              "23  DESCDEF: Volkswagen Group of America, Inc. (Vo...   \n",
              "24  DESCDEF: KZRV, L.P. (KZRV) is recalling certai...   \n",
              "25  DESCDEF: Daimler Trucks North America LLC (DTN...   \n",
              "26  DESCDEF: Forest River, Inc. (Forest River) is ...   \n",
              "27  DESCDEF: Mack Trucks, Inc. (Mack) is recalling...   \n",
              "28  DESCDEF: Michelin North America, Inc. (Micheli...   \n",
              "29  DESCDEF: Cheetah Chassis Corporation (Cheetah)...   \n",
              "30  DESCDEF: VEHICLE DESCRIPTION:  HEAVY DUTY TRUC...   \n",
              "31  DESCDEF: Ford Motor Company (Ford) is recallin...   \n",
              "32  DESCDEF: Harley-Davidson Motor Company (Harley...   \n",
              "33  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "34  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "35  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "36  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "37  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "38  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "39  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "40  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "41  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "\n",
              "                                             entities  \n",
              "0   [{'Entity': '1-888-446-5157', 'Label': 'Contac...  \n",
              "1   [{'Entity': '1999', 'Label': 'Year'}, {'Entity...  \n",
              "2   [{'Entity': '2000', 'Label': 'Year'}, {'Entity...  \n",
              "3   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...  \n",
              "4   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...  \n",
              "5   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...  \n",
              "6   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...  \n",
              "7   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...  \n",
              "8   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...  \n",
              "9   [{'Entity': '2003', 'Label': 'Year'}, {'Entity...  \n",
              "10  [{'Entity': '2003', 'Label': 'Year'}, {'Entity...  \n",
              "11  [{'Entity': '2004', 'Label': 'Year'}, {'Entity...  \n",
              "12  [{'Entity': '2006', 'Label': 'Year'}, {'Entity...  \n",
              "13              [{'Entity': '2006', 'Label': 'Year'}]  \n",
              "14  [{'Entity': '2007', 'Label': 'Year'}, {'Entity...  \n",
              "15  [{'Entity': '2007', 'Label': 'Year'}, {'Entity...  \n",
              "16  [{'Entity': '2007', 'Label': 'Year'}, {'Entity...  \n",
              "17  [{'Entity': '2009', 'Label': 'Year'}, {'Entity...  \n",
              "18  [{'Entity': '2009', 'Label': 'Year'}, {'Entity...  \n",
              "19  [{'Entity': '2009', 'Label': 'Year'}, {'Entity...  \n",
              "20  [{'Entity': '2015', 'Label': 'Year'}, {'Entity...  \n",
              "21  [{'Entity': '2015', 'Label': 'Year'}, {'Entity...  \n",
              "22  [{'Entity': '2015', 'Label': 'Year'}, {'Entity...  \n",
              "23  [{'Entity': '2017', 'Label': 'Year'}, {'Entity...  \n",
              "24  [{'Entity': '2017', 'Label': 'Year'}, {'Entity...  \n",
              "25  [{'Entity': '2018', 'Label': 'Year'}, {'Entity...  \n",
              "26  [{'Entity': '2019', 'Label': 'Year'}, {'Entity...  \n",
              "27  [{'Entity': '2021', 'Label': 'Year'}, {'Entity...  \n",
              "28  [{'Entity': '2023', 'Label': 'Year'}, {'Entity...  \n",
              "29  [{'Entity': '2023', 'Label': 'Year'}, {'Entity...  \n",
              "30  [{'Entity': 'DEALERS WILL INSPECT', 'Label': '...  \n",
              "31  [{'Entity': 'Ford', 'Label': 'Manufacturer'}, ...  \n",
              "32  [{'Entity': 'Harley-Davidson', 'Label': 'Manuf...  \n",
              "33  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "34  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "35  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "36  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "37  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "38  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "39  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "40  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  \n",
              "41  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb20335b-e6b3-40e7-aad3-79c660bc0d82\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>combined_text</th>\n",
              "      <th>entities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DESCDEF: ON CERTAIN PULL, FIFTH WHEEL AND TOY-...</td>\n",
              "      <td>[{'Entity': '1-888-446-5157', 'Label': 'Contac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DESCDEF: E-TON IS RECALLING CERTAIN MODEL YEAR...</td>\n",
              "      <td>[{'Entity': '1999', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DESCDEF: NAVISTAR IS RECALLING 51,588 HEAVY TR...</td>\n",
              "      <td>[{'Entity': '2000', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DESCDEF: Nissan North America, Inc. (Nissan) i...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DESCDEF: Altec Industries, Inc. is recalling c...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DESCDEF: KZRV, L.P. (KZRV) is recalling variou...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>DESCDEF: ON CERTAIN BUMPER-PULL HORSE TRAILERS...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DESCDEF: ON CERTAIN PASSENGER VEHICLES EQUIPPE...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>DESCDEF: ON CERTAIN HEAVY DUTY CLASS 8 TRUCKS,...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>DESCDEF: ON CERTAIN FIRE TRUCKS WITH TANDEM ST...</td>\n",
              "      <td>[{'Entity': '2003', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>DESCDEF: Thor Motor Coach (Thor) is recalling ...</td>\n",
              "      <td>[{'Entity': '2003', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>DESCDEF: SKYLINE IS RECALLING 22,372 MY 2004-2...</td>\n",
              "      <td>[{'Entity': '2004', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>DESCDEF: Cruiser RV is recalling certain model...</td>\n",
              "      <td>[{'Entity': '2006', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>DESCDEF: CERTAIN MAXZONE HEADLIGHT ASSEMBLIES,...</td>\n",
              "      <td>[{'Entity': '2006', 'Label': 'Year'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>DESCDEF: CERTAIN DELPHI FUEL PRESSURE REGULATO...</td>\n",
              "      <td>[{'Entity': '2007', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>DESCDEF: FEDERAL MOGUL IS RECALLING 6,903 AFTE...</td>\n",
              "      <td>[{'Entity': '2007', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>DESCDEF: ShowHauler Trucks, Inc. (ShowHauler) ...</td>\n",
              "      <td>[{'Entity': '2007', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>DESCDEF: REV Recreation Group (REV) is recalli...</td>\n",
              "      <td>[{'Entity': '2009', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>DESCDEF: Subaru of America, Inc. (Subaru) is r...</td>\n",
              "      <td>[{'Entity': '2009', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>DESCDEF: Yamaha Motor Corporation USA (Yamaha)...</td>\n",
              "      <td>[{'Entity': '2009', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>DESCDEF: Farber Specialty Vehicles (Farber) is...</td>\n",
              "      <td>[{'Entity': '2015', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>DESCDEF: Pierce Manufacturing (Pierce) is reca...</td>\n",
              "      <td>[{'Entity': '2015', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>DESCDEF: Winnebago Industries, Inc (Winnebago)...</td>\n",
              "      <td>[{'Entity': '2015', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>DESCDEF: Volkswagen Group of America, Inc. (Vo...</td>\n",
              "      <td>[{'Entity': '2017', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>DESCDEF: KZRV, L.P. (KZRV) is recalling certai...</td>\n",
              "      <td>[{'Entity': '2017', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>DESCDEF: Daimler Trucks North America LLC (DTN...</td>\n",
              "      <td>[{'Entity': '2018', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>DESCDEF: Forest River, Inc. (Forest River) is ...</td>\n",
              "      <td>[{'Entity': '2019', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>DESCDEF: Mack Trucks, Inc. (Mack) is recalling...</td>\n",
              "      <td>[{'Entity': '2021', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>DESCDEF: Michelin North America, Inc. (Micheli...</td>\n",
              "      <td>[{'Entity': '2023', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>DESCDEF: Cheetah Chassis Corporation (Cheetah)...</td>\n",
              "      <td>[{'Entity': '2023', 'Label': 'Year'}, {'Entity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>DESCDEF: VEHICLE DESCRIPTION:  HEAVY DUTY TRUC...</td>\n",
              "      <td>[{'Entity': 'DEALERS WILL INSPECT', 'Label': '...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>DESCDEF: Ford Motor Company (Ford) is recallin...</td>\n",
              "      <td>[{'Entity': 'Ford', 'Label': 'Manufacturer'}, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>DESCDEF: Harley-Davidson Motor Company (Harley...</td>\n",
              "      <td>[{'Entity': 'Harley-Davidson', 'Label': 'Manuf...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-eb20335b-e6b3-40e7-aad3-79c660bc0d82 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eb20335b-e6b3-40e7-aad3-79c660bc0d82');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bf3a3c7b-bc8d-4bcc-b22c-9d54bae43c58 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 42,\n  \"fields\": [\n    {\n      \"column\": \"combined_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"DESCDEF: REV Recreation Group (REV) is recalling certain 2009-2010 and 2012-2014 Holiday Rambler Ambassador, 2010 and 2013-2014 Endeavor, 2009-2010 Neptune, 2010 Scepter, and 2011 Trip diesel motorhomes, equipped with Kidde Plastic-Handle or Push Button 'Pindicator' Fire Extinguishers.  These extinguishers may become clogged, preventing the extinguisher from discharging as expected or excessive force may be needed to activate the extinguisher.  Additionally, in certain models, the nozzle may detach from the valve assembly with enough force that it could cause injury and also render the product inoperable.  For a full list of the affected fire extinguisher models visit: https://static.nhtsa.gov/odi/rcl/2017/RMISC-17E062-5427.pdf CONDEF: In the event of a fire, if the fire extinguisher does not function properly, it can increase the risk of injury. CORR: REV will notify owners, and instruct them to contact Kidde for a replacement fire extinguisher, free of charge.  The recall is expected to begin January 18, 2018.  Owners may contact REV customer service at 1-800-509-3417, or Kidde customer service at 1-855-262-3540 or online at www.kidde.com and click on \\\"Product Safety Recall\\\" for more information.  \",\n          \"DESCDEF: CERTAIN MAXZONE HEADLIGHT ASSEMBLIES, DEPO BRAND, P/NOS. 331-1129L-ASN AND 331-1129R-ASN, SOLD FOR USE AS AFTERMARKET EQUIPMENT FOR THE ABOVE LISTED PASSENGER VEHICLES.  THESE HEADLAMPS FAIL TO CONFORM TO THE PHOTOMETRIC REQUIREMENTS OF FEDERAL MOTOR VEHICLE SAFETY STANDARD NO. 108, LAMPS, REFLECTIVE DEVICES, AND ASSOCIATED EQUIPMENT. CONDEF: DECREASED LIGHTING VISIBILITY MAY RESULT IN A VEHICLE CRASH. CORR: MAXZONE WILL NOTIFY OWNERS AND OFFER REIMBURSEMENT FOR ANY NONCOMPLIANT HEADLIGHTS.  THE RECALL BEGAN ON JULY 28, 2006.  OWNERS MAY CONTACT MAXZONE AT 909-822-3288.\",\n          \"DESCDEF: Altec Industries, Inc. is recalling certain model year 2002-2009 AH75/85/100 and HL125 aerial device vehicles equipped with 16-foot radial outriggers.  An improper torque was applied to the outrigger cylinder piston retention system during assembly.  As a result, the cylinder could loosen from the rod allowing the outrigger leg to protrude beyond the confines of the chassis when it is in a stowed position.    CONDEF: If the cylinder loosens from the rod, the outrigger could strike a person or object, increasing the risk of injury or property damage.    CORR: Altec will notify owners, and will increase the torque on the piston free of charge.  The safety recall began on November 6, 2012.  Owners may contact Altec Industries at 1-877-462-5832. \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"[{'Entity': '2009', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2012', 'Label': 'Year'}, {'Entity': '2014', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2013', 'Label': 'Year'}, {'Entity': '2014', 'Label': 'Year'}, {'Entity': '2009', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2011', 'Label': 'Year'}, {'Entity': '2017', 'Label': 'Year'}, {'Entity': '2018', 'Label': 'Year'}, {'Entity': '1-800-509-3417', 'Label': 'Contact_Information'}, {'Entity': '1-855-262-3540', 'Label': 'Contact_Information'}]\",\n          \"[{'Entity': '2006', 'Label': 'Year'}]\",\n          \"[{'Entity': '2002', 'Label': 'Year'}, {'Entity': '2009', 'Label': 'Year'}, {'Entity': '2012', 'Label': 'Year'}, {'Entity': '1-877-462-5832', 'Label': 'Contact_Information'}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "entlist = [\n",
        "    \"Manufacturer:like name of the car manufacturer\",\n",
        "    \"Model: for example like  a model like focus from manufacturer ford\",\n",
        "    \"Year: for example like made  in the  year 2000\",\n",
        "    \"Recall_Number:for example like  02V288000\",\n",
        "    \"Component: for example like ELECTRICAL SYSTEM or  BATTERY CABLES\",\n",
        "    \"Issue_Defect: for example like CERTAIN PASSENGER VEHICLES EQUIPPED WITH ZETEC ENGINES or  LOOSE OR BROKEN ATTACHMENTS AND MISROUTED BATTERY CABLES COULD LEAD TO CABLE INSULATION DAMAGE\",\n",
        "    \"Notification_Dates:for example like  OWNER NOTIFICATION BEGAN FEBRUARY 10, 2003.\",\n",
        "    \"Corrective Action:for example like  DEALERS WILL INSPECT THE BATTERY CABLES FOR THE CONDITION OF THE CABLE INSULATION AND PROPER TIGHTENING OF THE TERMINAL ENDS or AS NECESSARY, CABLES WILL BE REROUTED, RETAINING CLIPS INSTALLED, AND DAMAGED BATTERY CABLES REPLACED.\",\n",
        "    \"Contact_Information:for example like  1-866-436-7332. or SAFETY ADMINISTRATION'S AUTO SAFETY HOTLINE AT 1-888-DASH-2-DOT (1-888-327-4236).\",\n",
        "    \"high risk: when really high risk causing heavy injury like brain damage/ribcage fracture/ legs broken etc \",\n",
        "    \"low risk:Low risk like small sprains/burns/bruises etc\"\n",
        "    \"safety risk:airbag deployment failure, brake failure, acceleration issues, seatbelt malfunction, tire blowouts, electrical system failure, fuel system leaks, compromised structural integrity}\"\n",
        "]\n",
        "\n",
        "instr =\"You are a smart and intelligent Name Entity Recognition (NER) System. \" \\\n",
        "           \"The task involves using to extract and identify all instances of automotive domain entities \" \\\n",
        "           \"from a given dataset containing text related to the automotive domain. The identified entities should \" \\\n",
        "           \"be enclosed within JSON keys corresponding to their entity type in the output, which must have the same content as \" \\\n",
        "           \"\"\"the input. For instance, a car  manufacturer should be represented as [{\"Entity\": \"\", \"Label\": \"Failure Issue\"}\"\"\"\\\n",
        "           \"while a car model should be represented as \\\"car_model\\\": \\\"<Model of a car>\\\". It is important \" \\\n",
        "           \"to ensure that all relevant automotive domain entities are identified, and false identifications are avoided.\" \\\n",
        "           f\"\"\"\\n\\nHere are the List Of Entities\\n{entlist}\"\"\"\\\n",
        "           \"\\n\\n\" \\\n",
        "           \"I would want you to identify all instances of automotive domain entities in the below text from in the format like this \"\\\n",
        "           \"\"\"[{\"Entity\": \"bottoming out the suspension\", \"Label\": \"Failure Issue\"},\n",
        "              {\"Entity\": \"amplification of the stress\", \"Label\": \"Failure Issue\"},\n",
        "              {\"Entity\": \"floor truss network\", \"Label\": \"Component\"},\"\"\"\\\n",
        "           \"with reference to the enitity list already given there should be No false idenitifications, dont give me any process just idenitfy the entities properly with no mistakes\"\\\n",
        "           \"With Each entity-label pair in a newline. Are the instructions clear to you?\"\\\n",
        "\n",
        "\n",
        "data[\"instruction\"]=instr\n",
        "data"
      ],
      "metadata": {
        "id": "Zi1haEZpfj1U",
        "outputId": "5b3a610b-87b7-4514-9f92-dd41ade8c6b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        combined_text  \\\n",
              "0   DESCDEF: ON CERTAIN PULL, FIFTH WHEEL AND TOY-...   \n",
              "1   DESCDEF: E-TON IS RECALLING CERTAIN MODEL YEAR...   \n",
              "2   DESCDEF: NAVISTAR IS RECALLING 51,588 HEAVY TR...   \n",
              "3   DESCDEF: Nissan North America, Inc. (Nissan) i...   \n",
              "4   DESCDEF: Altec Industries, Inc. is recalling c...   \n",
              "5   DESCDEF: KZRV, L.P. (KZRV) is recalling variou...   \n",
              "6   DESCDEF: ON CERTAIN BUMPER-PULL HORSE TRAILERS...   \n",
              "7   DESCDEF: ON CERTAIN PASSENGER VEHICLES EQUIPPE...   \n",
              "8   DESCDEF: ON CERTAIN HEAVY DUTY CLASS 8 TRUCKS,...   \n",
              "9   DESCDEF: ON CERTAIN FIRE TRUCKS WITH TANDEM ST...   \n",
              "10  DESCDEF: Thor Motor Coach (Thor) is recalling ...   \n",
              "11  DESCDEF: SKYLINE IS RECALLING 22,372 MY 2004-2...   \n",
              "12  DESCDEF: Cruiser RV is recalling certain model...   \n",
              "13  DESCDEF: CERTAIN MAXZONE HEADLIGHT ASSEMBLIES,...   \n",
              "14  DESCDEF: CERTAIN DELPHI FUEL PRESSURE REGULATO...   \n",
              "15  DESCDEF: FEDERAL MOGUL IS RECALLING 6,903 AFTE...   \n",
              "16  DESCDEF: ShowHauler Trucks, Inc. (ShowHauler) ...   \n",
              "17  DESCDEF: REV Recreation Group (REV) is recalli...   \n",
              "18  DESCDEF: Subaru of America, Inc. (Subaru) is r...   \n",
              "19  DESCDEF: Yamaha Motor Corporation USA (Yamaha)...   \n",
              "20  DESCDEF: Farber Specialty Vehicles (Farber) is...   \n",
              "21  DESCDEF: Pierce Manufacturing (Pierce) is reca...   \n",
              "22  DESCDEF: Winnebago Industries, Inc (Winnebago)...   \n",
              "23  DESCDEF: Volkswagen Group of America, Inc. (Vo...   \n",
              "24  DESCDEF: KZRV, L.P. (KZRV) is recalling certai...   \n",
              "25  DESCDEF: Daimler Trucks North America LLC (DTN...   \n",
              "26  DESCDEF: Forest River, Inc. (Forest River) is ...   \n",
              "27  DESCDEF: Mack Trucks, Inc. (Mack) is recalling...   \n",
              "28  DESCDEF: Michelin North America, Inc. (Micheli...   \n",
              "29  DESCDEF: Cheetah Chassis Corporation (Cheetah)...   \n",
              "30  DESCDEF: VEHICLE DESCRIPTION:  HEAVY DUTY TRUC...   \n",
              "31  DESCDEF: Ford Motor Company (Ford) is recallin...   \n",
              "32  DESCDEF: Harley-Davidson Motor Company (Harley...   \n",
              "33  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "34  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "35  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "36  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "37  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "38  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "39  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "40  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "41  DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...   \n",
              "\n",
              "                                             entities  \\\n",
              "0   [{'Entity': '1-888-446-5157', 'Label': 'Contac...   \n",
              "1   [{'Entity': '1999', 'Label': 'Year'}, {'Entity...   \n",
              "2   [{'Entity': '2000', 'Label': 'Year'}, {'Entity...   \n",
              "3   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...   \n",
              "4   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...   \n",
              "5   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...   \n",
              "6   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...   \n",
              "7   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...   \n",
              "8   [{'Entity': '2002', 'Label': 'Year'}, {'Entity...   \n",
              "9   [{'Entity': '2003', 'Label': 'Year'}, {'Entity...   \n",
              "10  [{'Entity': '2003', 'Label': 'Year'}, {'Entity...   \n",
              "11  [{'Entity': '2004', 'Label': 'Year'}, {'Entity...   \n",
              "12  [{'Entity': '2006', 'Label': 'Year'}, {'Entity...   \n",
              "13              [{'Entity': '2006', 'Label': 'Year'}]   \n",
              "14  [{'Entity': '2007', 'Label': 'Year'}, {'Entity...   \n",
              "15  [{'Entity': '2007', 'Label': 'Year'}, {'Entity...   \n",
              "16  [{'Entity': '2007', 'Label': 'Year'}, {'Entity...   \n",
              "17  [{'Entity': '2009', 'Label': 'Year'}, {'Entity...   \n",
              "18  [{'Entity': '2009', 'Label': 'Year'}, {'Entity...   \n",
              "19  [{'Entity': '2009', 'Label': 'Year'}, {'Entity...   \n",
              "20  [{'Entity': '2015', 'Label': 'Year'}, {'Entity...   \n",
              "21  [{'Entity': '2015', 'Label': 'Year'}, {'Entity...   \n",
              "22  [{'Entity': '2015', 'Label': 'Year'}, {'Entity...   \n",
              "23  [{'Entity': '2017', 'Label': 'Year'}, {'Entity...   \n",
              "24  [{'Entity': '2017', 'Label': 'Year'}, {'Entity...   \n",
              "25  [{'Entity': '2018', 'Label': 'Year'}, {'Entity...   \n",
              "26  [{'Entity': '2019', 'Label': 'Year'}, {'Entity...   \n",
              "27  [{'Entity': '2021', 'Label': 'Year'}, {'Entity...   \n",
              "28  [{'Entity': '2023', 'Label': 'Year'}, {'Entity...   \n",
              "29  [{'Entity': '2023', 'Label': 'Year'}, {'Entity...   \n",
              "30  [{'Entity': 'DEALERS WILL INSPECT', 'Label': '...   \n",
              "31  [{'Entity': 'Ford', 'Label': 'Manufacturer'}, ...   \n",
              "32  [{'Entity': 'Harley-Davidson', 'Label': 'Manuf...   \n",
              "33  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "34  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "35  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "36  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "37  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "38  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "39  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "40  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "41  [{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...   \n",
              "\n",
              "                                          instruction  \n",
              "0   You are a smart and intelligent Name Entity Re...  \n",
              "1   You are a smart and intelligent Name Entity Re...  \n",
              "2   You are a smart and intelligent Name Entity Re...  \n",
              "3   You are a smart and intelligent Name Entity Re...  \n",
              "4   You are a smart and intelligent Name Entity Re...  \n",
              "5   You are a smart and intelligent Name Entity Re...  \n",
              "6   You are a smart and intelligent Name Entity Re...  \n",
              "7   You are a smart and intelligent Name Entity Re...  \n",
              "8   You are a smart and intelligent Name Entity Re...  \n",
              "9   You are a smart and intelligent Name Entity Re...  \n",
              "10  You are a smart and intelligent Name Entity Re...  \n",
              "11  You are a smart and intelligent Name Entity Re...  \n",
              "12  You are a smart and intelligent Name Entity Re...  \n",
              "13  You are a smart and intelligent Name Entity Re...  \n",
              "14  You are a smart and intelligent Name Entity Re...  \n",
              "15  You are a smart and intelligent Name Entity Re...  \n",
              "16  You are a smart and intelligent Name Entity Re...  \n",
              "17  You are a smart and intelligent Name Entity Re...  \n",
              "18  You are a smart and intelligent Name Entity Re...  \n",
              "19  You are a smart and intelligent Name Entity Re...  \n",
              "20  You are a smart and intelligent Name Entity Re...  \n",
              "21  You are a smart and intelligent Name Entity Re...  \n",
              "22  You are a smart and intelligent Name Entity Re...  \n",
              "23  You are a smart and intelligent Name Entity Re...  \n",
              "24  You are a smart and intelligent Name Entity Re...  \n",
              "25  You are a smart and intelligent Name Entity Re...  \n",
              "26  You are a smart and intelligent Name Entity Re...  \n",
              "27  You are a smart and intelligent Name Entity Re...  \n",
              "28  You are a smart and intelligent Name Entity Re...  \n",
              "29  You are a smart and intelligent Name Entity Re...  \n",
              "30  You are a smart and intelligent Name Entity Re...  \n",
              "31  You are a smart and intelligent Name Entity Re...  \n",
              "32  You are a smart and intelligent Name Entity Re...  \n",
              "33  You are a smart and intelligent Name Entity Re...  \n",
              "34  You are a smart and intelligent Name Entity Re...  \n",
              "35  You are a smart and intelligent Name Entity Re...  \n",
              "36  You are a smart and intelligent Name Entity Re...  \n",
              "37  You are a smart and intelligent Name Entity Re...  \n",
              "38  You are a smart and intelligent Name Entity Re...  \n",
              "39  You are a smart and intelligent Name Entity Re...  \n",
              "40  You are a smart and intelligent Name Entity Re...  \n",
              "41  You are a smart and intelligent Name Entity Re...  "
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              "      <th></th>\n",
              "      <th>combined_text</th>\n",
              "      <th>entities</th>\n",
              "      <th>instruction</th>\n",
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              "      <th>0</th>\n",
              "      <td>DESCDEF: ON CERTAIN PULL, FIFTH WHEEL AND TOY-...</td>\n",
              "      <td>[{'Entity': '1-888-446-5157', 'Label': 'Contac...</td>\n",
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              "      <th>1</th>\n",
              "      <td>DESCDEF: E-TON IS RECALLING CERTAIN MODEL YEAR...</td>\n",
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              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "      <th>2</th>\n",
              "      <td>DESCDEF: NAVISTAR IS RECALLING 51,588 HEAVY TR...</td>\n",
              "      <td>[{'Entity': '2000', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DESCDEF: Nissan North America, Inc. (Nissan) i...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DESCDEF: Altec Industries, Inc. is recalling c...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DESCDEF: KZRV, L.P. (KZRV) is recalling variou...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>DESCDEF: ON CERTAIN BUMPER-PULL HORSE TRAILERS...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DESCDEF: ON CERTAIN PASSENGER VEHICLES EQUIPPE...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>DESCDEF: ON CERTAIN HEAVY DUTY CLASS 8 TRUCKS,...</td>\n",
              "      <td>[{'Entity': '2002', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>DESCDEF: ON CERTAIN FIRE TRUCKS WITH TANDEM ST...</td>\n",
              "      <td>[{'Entity': '2003', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>DESCDEF: Thor Motor Coach (Thor) is recalling ...</td>\n",
              "      <td>[{'Entity': '2003', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>DESCDEF: SKYLINE IS RECALLING 22,372 MY 2004-2...</td>\n",
              "      <td>[{'Entity': '2004', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>DESCDEF: Cruiser RV is recalling certain model...</td>\n",
              "      <td>[{'Entity': '2006', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>DESCDEF: CERTAIN MAXZONE HEADLIGHT ASSEMBLIES,...</td>\n",
              "      <td>[{'Entity': '2006', 'Label': 'Year'}]</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>DESCDEF: CERTAIN DELPHI FUEL PRESSURE REGULATO...</td>\n",
              "      <td>[{'Entity': '2007', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>DESCDEF: FEDERAL MOGUL IS RECALLING 6,903 AFTE...</td>\n",
              "      <td>[{'Entity': '2007', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>DESCDEF: ShowHauler Trucks, Inc. (ShowHauler) ...</td>\n",
              "      <td>[{'Entity': '2007', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>DESCDEF: REV Recreation Group (REV) is recalli...</td>\n",
              "      <td>[{'Entity': '2009', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>DESCDEF: Subaru of America, Inc. (Subaru) is r...</td>\n",
              "      <td>[{'Entity': '2009', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>DESCDEF: Yamaha Motor Corporation USA (Yamaha)...</td>\n",
              "      <td>[{'Entity': '2009', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>DESCDEF: Farber Specialty Vehicles (Farber) is...</td>\n",
              "      <td>[{'Entity': '2015', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>DESCDEF: Pierce Manufacturing (Pierce) is reca...</td>\n",
              "      <td>[{'Entity': '2015', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>DESCDEF: Winnebago Industries, Inc (Winnebago)...</td>\n",
              "      <td>[{'Entity': '2015', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>DESCDEF: Volkswagen Group of America, Inc. (Vo...</td>\n",
              "      <td>[{'Entity': '2017', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>DESCDEF: KZRV, L.P. (KZRV) is recalling certai...</td>\n",
              "      <td>[{'Entity': '2017', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>DESCDEF: Daimler Trucks North America LLC (DTN...</td>\n",
              "      <td>[{'Entity': '2018', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>DESCDEF: Forest River, Inc. (Forest River) is ...</td>\n",
              "      <td>[{'Entity': '2019', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>DESCDEF: Mack Trucks, Inc. (Mack) is recalling...</td>\n",
              "      <td>[{'Entity': '2021', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
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              "      <td>[{'Entity': '2023', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>DESCDEF: Cheetah Chassis Corporation (Cheetah)...</td>\n",
              "      <td>[{'Entity': '2023', 'Label': 'Year'}, {'Entity...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>DESCDEF: VEHICLE DESCRIPTION:  HEAVY DUTY TRUC...</td>\n",
              "      <td>[{'Entity': 'DEALERS WILL INSPECT', 'Label': '...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>DESCDEF: Ford Motor Company (Ford) is recallin...</td>\n",
              "      <td>[{'Entity': 'Ford', 'Label': 'Manufacturer'}, ...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>DESCDEF: Harley-Davidson Motor Company (Harley...</td>\n",
              "      <td>[{'Entity': 'Harley-Davidson', 'Label': 'Manuf...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>DESCDEF: Mercedes-Benz USA, LLC (MBUSA) is rec...</td>\n",
              "      <td>[{'Entity': 'Mercedes-Benz', 'Label': 'Manufac...</td>\n",
              "      <td>You are a smart and intelligent Name Entity Re...</td>\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0f504687-6c69-4df9-9ab1-5041d1b6ad89 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 42,\n  \"fields\": [\n    {\n      \"column\": \"combined_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"DESCDEF: REV Recreation Group (REV) is recalling certain 2009-2010 and 2012-2014 Holiday Rambler Ambassador, 2010 and 2013-2014 Endeavor, 2009-2010 Neptune, 2010 Scepter, and 2011 Trip diesel motorhomes, equipped with Kidde Plastic-Handle or Push Button 'Pindicator' Fire Extinguishers.  These extinguishers may become clogged, preventing the extinguisher from discharging as expected or excessive force may be needed to activate the extinguisher.  Additionally, in certain models, the nozzle may detach from the valve assembly with enough force that it could cause injury and also render the product inoperable.  For a full list of the affected fire extinguisher models visit: https://static.nhtsa.gov/odi/rcl/2017/RMISC-17E062-5427.pdf CONDEF: In the event of a fire, if the fire extinguisher does not function properly, it can increase the risk of injury. CORR: REV will notify owners, and instruct them to contact Kidde for a replacement fire extinguisher, free of charge.  The recall is expected to begin January 18, 2018.  Owners may contact REV customer service at 1-800-509-3417, or Kidde customer service at 1-855-262-3540 or online at www.kidde.com and click on \\\"Product Safety Recall\\\" for more information.  \",\n          \"DESCDEF: CERTAIN MAXZONE HEADLIGHT ASSEMBLIES, DEPO BRAND, P/NOS. 331-1129L-ASN AND 331-1129R-ASN, SOLD FOR USE AS AFTERMARKET EQUIPMENT FOR THE ABOVE LISTED PASSENGER VEHICLES.  THESE HEADLAMPS FAIL TO CONFORM TO THE PHOTOMETRIC REQUIREMENTS OF FEDERAL MOTOR VEHICLE SAFETY STANDARD NO. 108, LAMPS, REFLECTIVE DEVICES, AND ASSOCIATED EQUIPMENT. CONDEF: DECREASED LIGHTING VISIBILITY MAY RESULT IN A VEHICLE CRASH. CORR: MAXZONE WILL NOTIFY OWNERS AND OFFER REIMBURSEMENT FOR ANY NONCOMPLIANT HEADLIGHTS.  THE RECALL BEGAN ON JULY 28, 2006.  OWNERS MAY CONTACT MAXZONE AT 909-822-3288.\",\n          \"DESCDEF: Altec Industries, Inc. is recalling certain model year 2002-2009 AH75/85/100 and HL125 aerial device vehicles equipped with 16-foot radial outriggers.  An improper torque was applied to the outrigger cylinder piston retention system during assembly.  As a result, the cylinder could loosen from the rod allowing the outrigger leg to protrude beyond the confines of the chassis when it is in a stowed position.    CONDEF: If the cylinder loosens from the rod, the outrigger could strike a person or object, increasing the risk of injury or property damage.    CORR: Altec will notify owners, and will increase the torque on the piston free of charge.  The safety recall began on November 6, 2012.  Owners may contact Altec Industries at 1-877-462-5832. \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"[{'Entity': '2009', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2012', 'Label': 'Year'}, {'Entity': '2014', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2013', 'Label': 'Year'}, {'Entity': '2014', 'Label': 'Year'}, {'Entity': '2009', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2010', 'Label': 'Year'}, {'Entity': '2011', 'Label': 'Year'}, {'Entity': '2017', 'Label': 'Year'}, {'Entity': '2018', 'Label': 'Year'}, {'Entity': '1-800-509-3417', 'Label': 'Contact_Information'}, {'Entity': '1-855-262-3540', 'Label': 'Contact_Information'}]\",\n          \"[{'Entity': '2006', 'Label': 'Year'}]\",\n          \"[{'Entity': '2002', 'Label': 'Year'}, {'Entity': '2009', 'Label': 'Year'}, {'Entity': '2012', 'Label': 'Year'}, {'Entity': '1-877-462-5832', 'Label': 'Contact_Information'}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"You are a smart and intelligent Name Entity Recognition (NER) System. The task involves using to extract and identify all instances of automotive domain entities from a given dataset containing text related to the automotive domain. The identified entities should be enclosed within JSON keys corresponding to their entity type in the output, which must have the same content as the input. For instance, a car  manufacturer should be represented as [{\\\"Entity\\\": \\\"\\\", \\\"Label\\\": \\\"Failure Issue\\\"}while a car model should be represented as \\\"car_model\\\": \\\"<Model of a car>\\\". It is important to ensure that all relevant automotive domain entities are identified, and false identifications are avoided.\\n\\nHere are the List Of Entities\\n['Manufacturer:like name of the car manufacturer', 'Model: for example like  a model like focus from manufacturer ford', 'Year: for example like made  in the  year 2000', 'Recall_Number:for example like  02V288000', 'Component: for example like ELECTRICAL SYSTEM or  BATTERY CABLES', 'Issue_Defect: for example like CERTAIN PASSENGER VEHICLES EQUIPPED WITH ZETEC ENGINES or  LOOSE OR BROKEN ATTACHMENTS AND MISROUTED BATTERY CABLES COULD LEAD TO CABLE INSULATION DAMAGE', 'Notification_Dates:for example like  OWNER NOTIFICATION BEGAN FEBRUARY 10, 2003.', 'Corrective Action:for example like  DEALERS WILL INSPECT THE BATTERY CABLES FOR THE CONDITION OF THE CABLE INSULATION AND PROPER TIGHTENING OF THE TERMINAL ENDS or AS NECESSARY, CABLES WILL BE REROUTED, RETAINING CLIPS INSTALLED, AND DAMAGED BATTERY CABLES REPLACED.', \\\"Contact_Information:for example like  1-866-436-7332. or SAFETY ADMINISTRATION'S AUTO SAFETY HOTLINE AT 1-888-DASH-2-DOT (1-888-327-4236).\\\", 'high risk: when really high risk causing heavy injury like brain damage/ribcage fracture/ legs broken etc ', 'low risk:Low risk like small sprains/burns/bruises etcsafety risk:airbag deployment failure, brake failure, acceleration issues, seatbelt malfunction, tire blowouts, electrical system failure, fuel system leaks, compromised structural integrity}']\\n\\nI would want you to identify all instances of automotive domain entities in the below text from in the format like this [{\\\"Entity\\\": \\\"bottoming out the suspension\\\", \\\"Label\\\": \\\"Failure Issue\\\"},\\n              {\\\"Entity\\\": \\\"amplification of the stress\\\", \\\"Label\\\": \\\"Failure Issue\\\"},\\n              {\\\"Entity\\\": \\\"floor truss network\\\", \\\"Label\\\": \\\"Component\\\"},with reference to the enitity list already given there should be No false idenitifications, dont give me any process just idenitfy the entities properly with no mistakesWith Each entity-label pair in a newline. Are the instructions clear to you?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(instr))"
      ],
      "metadata": {
        "id": "Ll8mqLBXl3gC",
        "outputId": "f1f1eb9c-16fd-4b9a-ee39-973a36f4d0f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2640\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def formatting_func(example):\n",
        "    text = f\"### {example['instruction']}: \\n### User: {example['combined_text']} \\n### Assistant: {example['entities']}\"\n",
        "    return text"
      ],
      "metadata": {
        "id": "l2zNkQNHkmfR"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "df_train, df_test = train_test_split(data, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "NkPv4Nfxn4uA"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "id": "QXVh1SwUld2v"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "max_length = 2300 # differs from datasets to datasets\n",
        "def generate_and_tokenize_prompt(prompt):\n",
        "    result = tokenizer(\n",
        "        formatting_func(prompt),\n",
        "        truncation=True,\n",
        "        max_length=max_length,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result\n",
        "\n",
        "train_dataset = Dataset.from_pandas(df_train)\n",
        "eval_dataset = Dataset.from_pandas(df_test)\n",
        "\n",
        "train_dataset = train_dataset\n",
        "eval_dataset = eval_dataset\n",
        "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)\n",
        "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)"
      ],
      "metadata": {
        "id": "B0yXYHtRlgOu",
        "outputId": "c0489188-8032-42c9-c3b0-d6c17f8e6e99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "d0dbe2dc0a1f434886e448722cda79b3",
            "8afd182a27f541fbbfb3f2e53d42c9e9",
            "03aac1eeb0df418abb38f975255798b2",
            "e2655ceff74e4bf5afaa8f3d266ea66a",
            "79fa1eb9892b49efab08878d461164a8",
            "9793695a425d42e0abd861346376a0c8",
            "b1b4b6db6de54bff968f1f1cec815808",
            "c355dcfc81c84c0db74b227c3972c9b1",
            "b7ba3e0899ba4923a11d9695d58a0636",
            "38d0041ed4604ecd9cc6323d57d3bf0e",
            "f75885dbbdd944a78b5dcb2af8da2d7d",
            "ab556de818d8468681b7d06b9b21c731",
            "94223c80bd284f0e8bed0d936a153064",
            "df3d1aa15098482dbd544eb892d7916a",
            "486d62ff5b224a9db1c773053ed7edd7",
            "e60c54f16b474b9fab17b8e71a9080d2",
            "7c29fabedc944e88b27a6c59ece4359d",
            "4d8e7f24478342a3a37cfe99ddf59baa",
            "f14348313f5b422db3104165d3286d65",
            "fecd2338aad6438aa6ddc5c9500a2764",
            "b3b7655229cd4c3e9da5973b89197efe",
            "9ad0524ca1a2410191d4a4ddee85f048"
          ]
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/33 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0dbe2dc0a1f434886e448722cda79b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab556de818d8468681b7d06b9b21c731"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n"
      ],
      "metadata": {
        "id": "pow8yLjKcJM8"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fine-tuning Model"
      ],
      "metadata": {
        "id": "e-evbaTxhQTC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:1500\""
      ],
      "metadata": {
        "id": "o9L7oU9Av31e"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from datetime import datetime\n",
        "\n",
        "project = \"LLM-NER\"\n",
        "base_model_name = \"mistral\"\n",
        "run_name = base_model_name + \"-\" + project\n",
        "output_dir = \"./\" + run_name\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_val_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        output_dir=output_dir,\n",
        "        warmup_steps=1,\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=1,\n",
        "        gradient_checkpointing=True,\n",
        "        max_steps=500,\n",
        "        learning_rate=2.5e-4,\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        logging_steps=25,\n",
        "        logging_dir=\"./logs\",\n",
        "        save_strategy=\"steps\",\n",
        "        save_steps=25,\n",
        "        evaluation_strategy=\"steps\",\n",
        "        eval_steps=25,\n",
        "        do_eval=True,\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "PK_aou9ptH_j",
        "outputId": "b74fbb13-bdc3-4b29-8f08-7d8d67a2aa90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 20:31, Epoch 15/16]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>5.333500</td>\n",
              "      <td>2.187877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.601700</td>\n",
              "      <td>1.320358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.970200</td>\n",
              "      <td>1.181173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.801900</td>\n",
              "      <td>1.141954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.695100</td>\n",
              "      <td>1.125628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.522700</td>\n",
              "      <td>1.097551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.549200</td>\n",
              "      <td>1.117222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.468400</td>\n",
              "      <td>1.103067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>0.349000</td>\n",
              "      <td>1.110708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.285200</td>\n",
              "      <td>1.119694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>0.259300</td>\n",
              "      <td>1.122613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.158800</td>\n",
              "      <td>1.143289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>0.145900</td>\n",
              "      <td>1.145732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.091600</td>\n",
              "      <td>1.171484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>0.072400</td>\n",
              "      <td>1.184833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.054800</td>\n",
              "      <td>1.210729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>0.047700</td>\n",
              "      <td>1.201743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.030500</td>\n",
              "      <td>1.212431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>1.217498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.021000</td>\n",
              "      <td>1.218679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=0.6241841201782227, metrics={'train_runtime': 1232.8046, 'train_samples_per_second': 0.406, 'train_steps_per_second': 0.406, 'total_flos': 1364794828800000.0, 'train_loss': 0.6241841201782227, 'epoch': 15.15})"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    }
  ]
}