noble_mac_refactor_Llama2_finetune_CMS_medicare.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/stigsfoot/datascience-2023/blob/main/noble_mac_refactor_Llama2_finetune_CMS_medicare.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2EgqEPDQ8v6"
      },
      "source": [
        "## Installing Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mNnkgBq7Q3EU"
      },
      "outputs": [],
      "source": [
        "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
        "!pip install -q datasets bitsandbytes einops wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset details\n",
        "U.S. Department of Health & Human Services and Centeres for Medicare & Medicaid Data can be downloaded for free on Google BigQuery and from [Kaggle](https://www.kaggle.com/datasets/bigquery/cms-medicare?select=home_health_agencies_2014). Here, I use the bigquery-public-data.cms_medicare.home_health_agencies_2014 Table for this demonstration."
      ],
      "metadata": {
        "id": "e2OTXLba3bnJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6opcW_KjR8j9",
        "outputId": "d8c393a9-c10f-44e3-f822-08f16f70507a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4fUrzTUTyOK",
        "outputId": "4e4d35c2-38a9-40a7-b76d-cd3a5048d55b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cms_home_health_agencies_2014.csv  ICH_CAHPS_FACILITY.csv  qip23-cmpl.csv\n",
            "DFC_FACILITY.csv\t\t   ICH_CAHPS_NATIONAL.csv  qip23-ichc.csv\n",
            "DFC_NATIONAL.csv\t\t   ICH_CAHPS_STATE.csv\n",
            "DFC_STATE.csv\t\t\t   qip23-cdpr.csv\n"
          ]
        }
      ],
      "source": [
        "!ls /content/drive/MyDrive/Code/llm/demos/'data'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KJmHtRksTUmv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "cms_dataset_path = \"/content/drive/MyDrive/Code/llm/demos/data/cms_home_health_agencies_2014.csv\"\n",
        "df_cms = pd.read_csv(cms_dataset_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Agency Performance Categorization and Dataset Preparation\n",
        "\n",
        "Here, I am categorizing Agency performance based on specific criteria, split the data into training and test sets, and convert the training set into a machine learning-compatible format."
      ],
      "metadata": {
        "id": "IKqOeuaOC8jP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 527
        },
        "outputId": "1f7649e7-3f24-42a6-ea2b-64ce86b45d8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     provider_id                            agency_name  \\\n",
              "249        58442                 SUMMIT HOME HEALTH INC   \n",
              "433       747414            ABSOLUTE KHEIR SERVICES INC   \n",
              "19        107785         USA HOME HEALTH SERVICES, INC.   \n",
              "322        58274           ROYALE HOME HEALTH CARE, INC   \n",
              "332        58224  MOTHER THERESA'S HOME HEALTH SERVICES   \n",
              "\n",
              "                   street_address          city state  zip_code  \\\n",
              "249          2139 TAPO ST STE 210   SIMI VALLEY    CA     93063   \n",
              "433    651 N EGRET BAY BLVD STE K   LEAGUE CITY    TX     77573   \n",
              "19    5300 W ATLANTIC AVE STE 300  DELRAY BEACH    FL     33484   \n",
              "322  221 N SAN DIMAS AVE, SUITE B     SAN DIMAS    CA     91773   \n",
              "332   847 N. HOLLYWOOD WAY, # 203       BURBANK    CA     91505   \n",
              "\n",
              "     total_episodes_non_lupa  distinct_beneficiaries_non_lupa  \\\n",
              "249                      557                              255   \n",
              "433                      543                              179   \n",
              "19                      2205                             1437   \n",
              "322                      592                              458   \n",
              "332                      597                              295   \n",
              "\n",
              "     average_number_of_total_visits_per_episode_non_lupa  \\\n",
              "249                                               22.6     \n",
              "433                                               22.6     \n",
              "19                                                23.1     \n",
              "322                                               18.5     \n",
              "332                                               17.1     \n",
              "\n",
              "     average_number_of_skilled_nursing_visits_per_episode_non_lupa  ...  \\\n",
              "249                                               11.9              ...   \n",
              "433                                                9.8              ...   \n",
              "19                                                15.5              ...   \n",
              "322                                               14.0              ...   \n",
              "332                                               11.0              ...   \n",
              "\n",
              "     percent_of_beneficiaries_with_depression  \\\n",
              "249                                        68   \n",
              "433                                        37   \n",
              "19                                         46   \n",
              "322                                        37   \n",
              "332                                        47   \n",
              "\n",
              "     percent_of_beneficiaries_with_diabetes  \\\n",
              "249                                    55.0   \n",
              "433                                    52.0   \n",
              "19                                     45.0   \n",
              "322                                    51.0   \n",
              "332                                    51.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_hyperlipidemia  \\\n",
              "249                                          69.0   \n",
              "433                                          56.0   \n",
              "19                                           73.0   \n",
              "322                                          68.0   \n",
              "332                                           NaN   \n",
              "\n",
              "     percent_of_beneficiaries_with_hypertension  \\\n",
              "249                                         NaN   \n",
              "433                                         NaN   \n",
              "19                                          NaN   \n",
              "322                                         NaN   \n",
              "332                                         NaN   \n",
              "\n",
              "     percent_of_beneficiaries_with_ihd  \\\n",
              "249                               57.0   \n",
              "433                               64.0   \n",
              "19                                69.0   \n",
              "322                               63.0   \n",
              "332                               63.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_osteoporosis  \\\n",
              "249                                          25   \n",
              "433                                          16   \n",
              "19                                           15   \n",
              "322                                          16   \n",
              "332                                          17   \n",
              "\n",
              "     percent_of_beneficiaries_with_ra_oa  \\\n",
              "249                                 72.0   \n",
              "433                                 64.0   \n",
              "19                                  60.0   \n",
              "322                                 58.0   \n",
              "332                                 71.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_schizophrenia  \\\n",
              "249                                            6   \n",
              "433                                            9   \n",
              "19                                            12   \n",
              "322                                           10   \n",
              "332                                            9   \n",
              "\n",
              "     percent_of_beneficiaries_with_stroke  performance_tier  \n",
              "249                                    13     Above Average  \n",
              "433                                    25     Above Average  \n",
              "19                                     14     Above Average  \n",
              "322                                    12     Above Average  \n",
              "332                                     9     Above Average  \n",
              "\n",
              "[5 rows x 50 columns]"
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              "      <td>107785</td>\n",
              "      <td>USA HOME HEALTH SERVICES, INC.</td>\n",
              "      <td>5300 W ATLANTIC AVE STE 300</td>\n",
              "      <td>DELRAY BEACH</td>\n",
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              "\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (50) exceeds max_columns (20) limiting to first (20) columns.\n"
          ]
        }
      ],
      "source": [
        "# Define a function to categorize agency performance based on selected features\n",
        "def categorize_performance(row):\n",
        "    avg_visits = row['average_number_of_total_visits_per_episode_non_lupa']\n",
        "    beneficiaries_with_copd = row['percent_of_beneficiaries_with_copd']\n",
        "\n",
        "    if avg_visits >= 15 and beneficiaries_with_copd <= 20:\n",
        "        return 'Top Performer'\n",
        "    elif avg_visits >= 10:\n",
        "        return 'Above Average'\n",
        "    elif avg_visits >= 5:\n",
        "        return 'Average'\n",
        "    else:\n",
        "        return 'Below Average'\n",
        "\n",
        "# Apply the categorization function to create a new 'performance_tier' column\n",
        "df_cms['performance_tier'] = df_cms.apply(categorize_performance, axis=1)\n",
        "\n",
        "# Cell 6: Split CMS data into training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_df, test_df = train_test_split(df_cms, test_size=0.2, random_state=42)\n",
        "\n",
        "# Cell 9: Create DatasetDict from CMS data\n",
        "from datasets import Dataset, DatasetDict\n",
        "train_dataset_dict = DatasetDict({\n",
        "    \"train\": Dataset.from_pandas(train_df),\n",
        "})\n",
        "\n",
        "# Displaying the first few rows of the training DataFrame to verify changes\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Splitting the CMS Data into Training and Test Sets\n",
        "\n",
        "This cell divides the CMS (Centers for Medicare & Medicaid Services) data into training and test sets using an 80-20 split ratio. The first 10 rows of the training DataFrame are then displayed to verify the changes."
      ],
      "metadata": {
        "id": "A42583vDE336"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GkmdTDF0Wc3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "outputId": "b593ecbb-09ad-42f1-abe8-098ed8c67f13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     provider_id                            agency_name  \\\n",
              "249        58442                 SUMMIT HOME HEALTH INC   \n",
              "433       747414            ABSOLUTE KHEIR SERVICES INC   \n",
              "19        107785         USA HOME HEALTH SERVICES, INC.   \n",
              "322        58274           ROYALE HOME HEALTH CARE, INC   \n",
              "332        58224  MOTHER THERESA'S HOME HEALTH SERVICES   \n",
              "56        679407                  THERACARE HOME HEALTH   \n",
              "301        59049    VALLEY HOME HEALTH CARE AGENCY, INC   \n",
              "229       557747   ASIAN NETWORK PACIFIC HOME CARE, INC   \n",
              "331       557792            QUALITY CARE HH SPECIALISTS   \n",
              "132        58110   NEW HAVEN HOME HEALTH SERVICES, INC.   \n",
              "\n",
              "                       street_address          city state  zip_code  \\\n",
              "249              2139 TAPO ST STE 210   SIMI VALLEY    CA     93063   \n",
              "433        651 N EGRET BAY BLVD STE K   LEAGUE CITY    TX     77573   \n",
              "19        5300 W ATLANTIC AVE STE 300  DELRAY BEACH    FL     33484   \n",
              "322      221 N SAN DIMAS AVE, SUITE B     SAN DIMAS    CA     91773   \n",
              "332       847 N. HOLLYWOOD WAY, # 203       BURBANK    CA     91505   \n",
              "56             697 SOUTH STEMMONS FWY    LEWISVILLE    TX     75067   \n",
              "301     5530 CORBIN AVENUE, SUITE 112       TARZANA    CA     91356   \n",
              "229         212 9TH STREET, SUITE 205       OAKLAND    CA     94607   \n",
              "331                 127 N LANG AVENUE   WEST COVINA    CA     91790   \n",
              "132  333 GELLERT BOULEVARD, SUITE 249     DALY CITY    CA     94015   \n",
              "\n",
              "     total_episodes_non_lupa  distinct_beneficiaries_non_lupa  \\\n",
              "249                      557                              255   \n",
              "433                      543                              179   \n",
              "19                      2205                             1437   \n",
              "322                      592                              458   \n",
              "332                      597                              295   \n",
              "56                       891                              700   \n",
              "301                      671                              350   \n",
              "229                      713                              585   \n",
              "331                      567                              238   \n",
              "132                      795                              641   \n",
              "\n",
              "     average_number_of_total_visits_per_episode_non_lupa  \\\n",
              "249                                               22.6     \n",
              "433                                               22.6     \n",
              "19                                                23.1     \n",
              "322                                               18.5     \n",
              "332                                               17.1     \n",
              "56                                                18.2     \n",
              "301                                               19.6     \n",
              "229                                               17.0     \n",
              "331                                               21.2     \n",
              "132                                               15.0     \n",
              "\n",
              "     average_number_of_skilled_nursing_visits_per_episode_non_lupa  ...  \\\n",
              "249                                               11.9              ...   \n",
              "433                                                9.8              ...   \n",
              "19                                                15.5              ...   \n",
              "322                                               14.0              ...   \n",
              "332                                               11.0              ...   \n",
              "56                                                 4.7              ...   \n",
              "301                                               15.5              ...   \n",
              "229                                                9.2              ...   \n",
              "331                                               16.1              ...   \n",
              "132                                                8.8              ...   \n",
              "\n",
              "     percent_of_beneficiaries_with_depression  \\\n",
              "249                                        68   \n",
              "433                                        37   \n",
              "19                                         46   \n",
              "322                                        37   \n",
              "332                                        47   \n",
              "56                                         47   \n",
              "301                                        41   \n",
              "229                                        19   \n",
              "331                                        47   \n",
              "132                                        32   \n",
              "\n",
              "     percent_of_beneficiaries_with_diabetes  \\\n",
              "249                                    55.0   \n",
              "433                                    52.0   \n",
              "19                                     45.0   \n",
              "322                                    51.0   \n",
              "332                                    51.0   \n",
              "56                                     46.0   \n",
              "301                                    66.0   \n",
              "229                                    46.0   \n",
              "331                                    57.0   \n",
              "132                                    47.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_hyperlipidemia  \\\n",
              "249                                          69.0   \n",
              "433                                          56.0   \n",
              "19                                           73.0   \n",
              "322                                          68.0   \n",
              "332                                           NaN   \n",
              "56                                           73.0   \n",
              "301                                          75.0   \n",
              "229                                          56.0   \n",
              "331                                          67.0   \n",
              "132                                          63.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_hypertension  \\\n",
              "249                                         NaN   \n",
              "433                                         NaN   \n",
              "19                                          NaN   \n",
              "322                                         NaN   \n",
              "332                                         NaN   \n",
              "56                                          NaN   \n",
              "301                                         NaN   \n",
              "229                                         NaN   \n",
              "331                                         NaN   \n",
              "132                                         NaN   \n",
              "\n",
              "     percent_of_beneficiaries_with_ihd  \\\n",
              "249                               57.0   \n",
              "433                               64.0   \n",
              "19                                69.0   \n",
              "322                               63.0   \n",
              "332                               63.0   \n",
              "56                                53.0   \n",
              "301                                NaN   \n",
              "229                               43.0   \n",
              "331                                NaN   \n",
              "132                               53.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_osteoporosis  \\\n",
              "249                                          25   \n",
              "433                                          16   \n",
              "19                                           15   \n",
              "322                                          16   \n",
              "332                                          17   \n",
              "56                                           20   \n",
              "301                                          25   \n",
              "229                                          20   \n",
              "331                                          20   \n",
              "132                                          18   \n",
              "\n",
              "     percent_of_beneficiaries_with_ra_oa  \\\n",
              "249                                 72.0   \n",
              "433                                 64.0   \n",
              "19                                  60.0   \n",
              "322                                 58.0   \n",
              "332                                 71.0   \n",
              "56                                  68.0   \n",
              "301                                  NaN   \n",
              "229                                 39.0   \n",
              "331                                 59.0   \n",
              "132                                 46.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_schizophrenia  \\\n",
              "249                                            6   \n",
              "433                                            9   \n",
              "19                                            12   \n",
              "322                                           10   \n",
              "332                                            9   \n",
              "56                                             8   \n",
              "301                                           21   \n",
              "229                                            4   \n",
              "331                                           33   \n",
              "132                                            6   \n",
              "\n",
              "     percent_of_beneficiaries_with_stroke  performance_tier  \n",
              "249                                    13     Above Average  \n",
              "433                                    25     Above Average  \n",
              "19                                     14     Above Average  \n",
              "322                                    12     Above Average  \n",
              "332                                     9     Above Average  \n",
              "56                                     18     Above Average  \n",
              "301                                    11     Above Average  \n",
              "229                                    16     Above Average  \n",
              "331                                    13     Above Average  \n",
              "132                                    14     Above Average  \n",
              "\n",
              "[10 rows x 50 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-ef7c4b70-68e9-4fce-ae88-b67489a8793c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>provider_id</th>\n",
              "      <th>agency_name</th>\n",
              "      <th>street_address</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>total_episodes_non_lupa</th>\n",
              "      <th>distinct_beneficiaries_non_lupa</th>\n",
              "      <th>average_number_of_total_visits_per_episode_non_lupa</th>\n",
              "      <th>average_number_of_skilled_nursing_visits_per_episode_non_lupa</th>\n",
              "      <th>...</th>\n",
              "      <th>percent_of_beneficiaries_with_depression</th>\n",
              "      <th>percent_of_beneficiaries_with_diabetes</th>\n",
              "      <th>percent_of_beneficiaries_with_hyperlipidemia</th>\n",
              "      <th>percent_of_beneficiaries_with_hypertension</th>\n",
              "      <th>percent_of_beneficiaries_with_ihd</th>\n",
              "      <th>percent_of_beneficiaries_with_osteoporosis</th>\n",
              "      <th>percent_of_beneficiaries_with_ra_oa</th>\n",
              "      <th>percent_of_beneficiaries_with_schizophrenia</th>\n",
              "      <th>percent_of_beneficiaries_with_stroke</th>\n",
              "      <th>performance_tier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>58442</td>\n",
              "      <td>SUMMIT HOME HEALTH INC</td>\n",
              "      <td>2139 TAPO ST STE 210</td>\n",
              "      <td>SIMI VALLEY</td>\n",
              "      <td>CA</td>\n",
              "      <td>93063</td>\n",
              "      <td>557</td>\n",
              "      <td>255</td>\n",
              "      <td>22.6</td>\n",
              "      <td>11.9</td>\n",
              "      <td>...</td>\n",
              "      <td>68</td>\n",
              "      <td>55.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.0</td>\n",
              "      <td>25</td>\n",
              "      <td>72.0</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>747414</td>\n",
              "      <td>ABSOLUTE KHEIR SERVICES INC</td>\n",
              "      <td>651 N EGRET BAY BLVD STE K</td>\n",
              "      <td>LEAGUE CITY</td>\n",
              "      <td>TX</td>\n",
              "      <td>77573</td>\n",
              "      <td>543</td>\n",
              "      <td>179</td>\n",
              "      <td>22.6</td>\n",
              "      <td>9.8</td>\n",
              "      <td>...</td>\n",
              "      <td>37</td>\n",
              "      <td>52.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.0</td>\n",
              "      <td>16</td>\n",
              "      <td>64.0</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>107785</td>\n",
              "      <td>USA HOME HEALTH SERVICES, INC.</td>\n",
              "      <td>5300 W ATLANTIC AVE STE 300</td>\n",
              "      <td>DELRAY BEACH</td>\n",
              "      <td>FL</td>\n",
              "      <td>33484</td>\n",
              "      <td>2205</td>\n",
              "      <td>1437</td>\n",
              "      <td>23.1</td>\n",
              "      <td>15.5</td>\n",
              "      <td>...</td>\n",
              "      <td>46</td>\n",
              "      <td>45.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>69.0</td>\n",
              "      <td>15</td>\n",
              "      <td>60.0</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>58274</td>\n",
              "      <td>ROYALE HOME HEALTH CARE, INC</td>\n",
              "      <td>221 N SAN DIMAS AVE, SUITE B</td>\n",
              "      <td>SAN DIMAS</td>\n",
              "      <td>CA</td>\n",
              "      <td>91773</td>\n",
              "      <td>592</td>\n",
              "      <td>458</td>\n",
              "      <td>18.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>37</td>\n",
              "      <td>51.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.0</td>\n",
              "      <td>16</td>\n",
              "      <td>58.0</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>58224</td>\n",
              "      <td>MOTHER THERESA'S HOME HEALTH SERVICES</td>\n",
              "      <td>847 N. HOLLYWOOD WAY, # 203</td>\n",
              "      <td>BURBANK</td>\n",
              "      <td>CA</td>\n",
              "      <td>91505</td>\n",
              "      <td>597</td>\n",
              "      <td>295</td>\n",
              "      <td>17.1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>51.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.0</td>\n",
              "      <td>17</td>\n",
              "      <td>71.0</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>679407</td>\n",
              "      <td>THERACARE HOME HEALTH</td>\n",
              "      <td>697 SOUTH STEMMONS FWY</td>\n",
              "      <td>LEWISVILLE</td>\n",
              "      <td>TX</td>\n",
              "      <td>75067</td>\n",
              "      <td>891</td>\n",
              "      <td>700</td>\n",
              "      <td>18.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>46.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>20</td>\n",
              "      <td>68.0</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>59049</td>\n",
              "      <td>VALLEY HOME HEALTH CARE AGENCY, INC</td>\n",
              "      <td>5530 CORBIN AVENUE, SUITE 112</td>\n",
              "      <td>TARZANA</td>\n",
              "      <td>CA</td>\n",
              "      <td>91356</td>\n",
              "      <td>671</td>\n",
              "      <td>350</td>\n",
              "      <td>19.6</td>\n",
              "      <td>15.5</td>\n",
              "      <td>...</td>\n",
              "      <td>41</td>\n",
              "      <td>66.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>557747</td>\n",
              "      <td>ASIAN NETWORK PACIFIC HOME CARE, INC</td>\n",
              "      <td>212 9TH STREET, SUITE 205</td>\n",
              "      <td>OAKLAND</td>\n",
              "      <td>CA</td>\n",
              "      <td>94607</td>\n",
              "      <td>713</td>\n",
              "      <td>585</td>\n",
              "      <td>17.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>...</td>\n",
              "      <td>19</td>\n",
              "      <td>46.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>20</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>557792</td>\n",
              "      <td>QUALITY CARE HH SPECIALISTS</td>\n",
              "      <td>127 N LANG AVENUE</td>\n",
              "      <td>WEST COVINA</td>\n",
              "      <td>CA</td>\n",
              "      <td>91790</td>\n",
              "      <td>567</td>\n",
              "      <td>238</td>\n",
              "      <td>21.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>59.0</td>\n",
              "      <td>33</td>\n",
              "      <td>13</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>58110</td>\n",
              "      <td>NEW HAVEN HOME HEALTH SERVICES, INC.</td>\n",
              "      <td>333 GELLERT BOULEVARD, SUITE 249</td>\n",
              "      <td>DALY CITY</td>\n",
              "      <td>CA</td>\n",
              "      <td>94015</td>\n",
              "      <td>795</td>\n",
              "      <td>641</td>\n",
              "      <td>15.0</td>\n",
              "      <td>8.8</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>47.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>18</td>\n",
              "      <td>46.0</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 50 columns</p>\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
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              "\n",
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              "\n",
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              "      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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef7c4b70-68e9-4fce-ae88-b67489a8793c 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-ef7c4b70-68e9-4fce-ae88-b67489a8793c');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (50) exceeds max_columns (20) limiting to first (20) columns.\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_df, test_df = train_test_split(df_cms, test_size=0.2, random_state=42)\n",
        "\n",
        "train_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "id": "sYD0jpyYWr4D",
        "outputId": "8dfaf930-4852-41a0-bf50-cf907c4ee439"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     provider_id                               agency_name  \\\n",
              "361       107399         MEDCARE HOME HEALTH SERVICES INC.   \n",
              "73        147756               ELITE HOME HEALTH CARE, INC   \n",
              "374       147886                 HOME HEALTH PROVIDERS, PC   \n",
              "155        59524  GUARDIAN HOME HEALTH CARE & HOSPICE, INC   \n",
              "104       743179                EVERWELL HEALTH AGENCY LLC   \n",
              "394       347176                         REX HOME SERVICES   \n",
              "377       147655            AMERICAN HOME CARE EXPRESS INC   \n",
              "124        57539                           SHARP HOME CARE   \n",
              "68         59465          TOTAL CARE HOME HEALTH CARE, INC   \n",
              "450        59308            GOLDEN ANGELS HOME HEALTH CARE   \n",
              "\n",
              "                        street_address         city state  zip_code  \\\n",
              "361            100 NW 82ND AVE STE 302   PLANTATION    FL     33324   \n",
              "73                 330 MELVIN DRIVE #3   NORTHBROOK    IL     60062   \n",
              "374                 16650 S HARLEM AVE  TINLEY PARK    IL     60477   \n",
              "155  39055 HASTINGS STREET, SUITE 202B      FREMONT    CA     94538   \n",
              "104       13601 PRESTON ROAD SUITE 930       DALLAS    TX     75240   \n",
              "394       1500 SUNDAY DRIVE, SUITE 113      RALEIGH    NC     27607   \n",
              "377           6421 NORTH HAMLIN AVENUE  LINCOLNWOOD    IL     60712   \n",
              "124       8080 DAGGET STREET SUITE 200    SAN DIEGO    CA     92111   \n",
              "68     105 WEST ALAMEDA AVE, SUITE 218      BURBANK    CA     91502   \n",
              "450    43136 CHRISTY STREET, SUITE 112      FREMONT    CA     94538   \n",
              "\n",
              "     total_episodes_non_lupa  distinct_beneficiaries_non_lupa  \\\n",
              "361                     1576                              978   \n",
              "73                      1901                              719   \n",
              "374                      748                              372   \n",
              "155                      354                              309   \n",
              "104                      332                               92   \n",
              "394                     1625                             1377   \n",
              "377                      966                              463   \n",
              "124                     1113                              852   \n",
              "68                       686                              410   \n",
              "450                      472                              325   \n",
              "\n",
              "     average_number_of_total_visits_per_episode_non_lupa  \\\n",
              "361                                               25.9     \n",
              "73                                                15.0     \n",
              "374                                               19.9     \n",
              "155                                               14.2     \n",
              "104                                               11.7     \n",
              "394                                               14.5     \n",
              "377                                               12.8     \n",
              "124                                               14.1     \n",
              "68                                                16.8     \n",
              "450                                               16.4     \n",
              "\n",
              "     average_number_of_skilled_nursing_visits_per_episode_non_lupa  ...  \\\n",
              "361                                               16.5              ...   \n",
              "73                                                10.8              ...   \n",
              "374                                                7.1              ...   \n",
              "155                                                8.5              ...   \n",
              "104                                                8.2              ...   \n",
              "394                                                4.6              ...   \n",
              "377                                                7.0              ...   \n",
              "124                                                7.3              ...   \n",
              "68                                                11.6              ...   \n",
              "450                                                7.9              ...   \n",
              "\n",
              "     percent_of_beneficiaries_with_depression  \\\n",
              "361                                        50   \n",
              "73                                         44   \n",
              "374                                        35   \n",
              "155                                        34   \n",
              "104                                        39   \n",
              "394                                        34   \n",
              "377                                        41   \n",
              "124                                        34   \n",
              "68                                         54   \n",
              "450                                        35   \n",
              "\n",
              "     percent_of_beneficiaries_with_diabetes  \\\n",
              "361                                    49.0   \n",
              "73                                     48.0   \n",
              "374                                    56.0   \n",
              "155                                    48.0   \n",
              "104                                    58.0   \n",
              "394                                    38.0   \n",
              "377                                    59.0   \n",
              "124                                    44.0   \n",
              "68                                     63.0   \n",
              "450                                    50.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_hyperlipidemia  \\\n",
              "361                                          74.0   \n",
              "73                                           66.0   \n",
              "374                                          65.0   \n",
              "155                                          63.0   \n",
              "104                                           NaN   \n",
              "394                                          64.0   \n",
              "377                                          66.0   \n",
              "124                                          62.0   \n",
              "68                                            NaN   \n",
              "450                                          68.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_hypertension  \\\n",
              "361                                         NaN   \n",
              "73                                          NaN   \n",
              "374                                         NaN   \n",
              "155                                         NaN   \n",
              "104                                         NaN   \n",
              "394                                         NaN   \n",
              "377                                         NaN   \n",
              "124                                         NaN   \n",
              "68                                          NaN   \n",
              "450                                         NaN   \n",
              "\n",
              "     percent_of_beneficiaries_with_ihd  \\\n",
              "361                               70.0   \n",
              "73                                 NaN   \n",
              "374                               55.0   \n",
              "155                               62.0   \n",
              "104                               57.0   \n",
              "394                               45.0   \n",
              "377                               56.0   \n",
              "124                               51.0   \n",
              "68                                74.0   \n",
              "450                               43.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_osteoporosis  \\\n",
              "361                                          15   \n",
              "73                                           22   \n",
              "374                                          11   \n",
              "155                                          12   \n",
              "104                                          11   \n",
              "394                                          17   \n",
              "377                                          13   \n",
              "124                                          15   \n",
              "68                                           23   \n",
              "450                                          23   \n",
              "\n",
              "     percent_of_beneficiaries_with_ra_oa  \\\n",
              "361                                 56.0   \n",
              "73                                   NaN   \n",
              "374                                 71.0   \n",
              "155                                 53.0   \n",
              "104                                  NaN   \n",
              "394                                 65.0   \n",
              "377                                 72.0   \n",
              "124                                 54.0   \n",
              "68                                   NaN   \n",
              "450                                 51.0   \n",
              "\n",
              "     percent_of_beneficiaries_with_schizophrenia  \\\n",
              "361                                           19   \n",
              "73                                             8   \n",
              "374                                            9   \n",
              "155                                           14   \n",
              "104                                           13   \n",
              "394                                            5   \n",
              "377                                            8   \n",
              "124                                            6   \n",
              "68                                            13   \n",
              "450                                            7   \n",
              "\n",
              "     percent_of_beneficiaries_with_stroke  performance_tier  \n",
              "361                                    13     Above Average  \n",
              "73                                      9     Above Average  \n",
              "374                                    14     Above Average  \n",
              "155                                    12     Above Average  \n",
              "104                                    11     Above Average  \n",
              "394                                    11     Above Average  \n",
              "377                                    11     Above Average  \n",
              "124                                    11     Above Average  \n",
              "68                                      7     Above Average  \n",
              "450                                    11     Top Performer  \n",
              "\n",
              "[10 rows x 50 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>provider_id</th>\n",
              "      <th>agency_name</th>\n",
              "      <th>street_address</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>total_episodes_non_lupa</th>\n",
              "      <th>distinct_beneficiaries_non_lupa</th>\n",
              "      <th>average_number_of_total_visits_per_episode_non_lupa</th>\n",
              "      <th>average_number_of_skilled_nursing_visits_per_episode_non_lupa</th>\n",
              "      <th>...</th>\n",
              "      <th>percent_of_beneficiaries_with_depression</th>\n",
              "      <th>percent_of_beneficiaries_with_diabetes</th>\n",
              "      <th>percent_of_beneficiaries_with_hyperlipidemia</th>\n",
              "      <th>percent_of_beneficiaries_with_hypertension</th>\n",
              "      <th>percent_of_beneficiaries_with_ihd</th>\n",
              "      <th>percent_of_beneficiaries_with_osteoporosis</th>\n",
              "      <th>percent_of_beneficiaries_with_ra_oa</th>\n",
              "      <th>percent_of_beneficiaries_with_schizophrenia</th>\n",
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              "      <th>performance_tier</th>\n",
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              "      <th>361</th>\n",
              "      <td>107399</td>\n",
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              "      <td>100 NW 82ND AVE STE 302</td>\n",
              "      <td>PLANTATION</td>\n",
              "      <td>FL</td>\n",
              "      <td>33324</td>\n",
              "      <td>1576</td>\n",
              "      <td>978</td>\n",
              "      <td>25.9</td>\n",
              "      <td>16.5</td>\n",
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              "      <td>50</td>\n",
              "      <td>49.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.0</td>\n",
              "      <td>15</td>\n",
              "      <td>56.0</td>\n",
              "      <td>19</td>\n",
              "      <td>13</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>147756</td>\n",
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              "      <td>330 MELVIN DRIVE #3</td>\n",
              "      <td>NORTHBROOK</td>\n",
              "      <td>IL</td>\n",
              "      <td>60062</td>\n",
              "      <td>1901</td>\n",
              "      <td>719</td>\n",
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              "      <td>10.8</td>\n",
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              "      <td>66.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>Above Average</td>\n",
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              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>147886</td>\n",
              "      <td>HOME HEALTH PROVIDERS, PC</td>\n",
              "      <td>16650 S HARLEM AVE</td>\n",
              "      <td>TINLEY PARK</td>\n",
              "      <td>IL</td>\n",
              "      <td>60477</td>\n",
              "      <td>748</td>\n",
              "      <td>372</td>\n",
              "      <td>19.9</td>\n",
              "      <td>7.1</td>\n",
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              "      <td>14</td>\n",
              "      <td>Above Average</td>\n",
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              "      <th>155</th>\n",
              "      <td>59524</td>\n",
              "      <td>GUARDIAN HOME HEALTH CARE &amp; HOSPICE, INC</td>\n",
              "      <td>39055 HASTINGS STREET, SUITE 202B</td>\n",
              "      <td>FREMONT</td>\n",
              "      <td>CA</td>\n",
              "      <td>94538</td>\n",
              "      <td>354</td>\n",
              "      <td>309</td>\n",
              "      <td>14.2</td>\n",
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              "      <td>...</td>\n",
              "      <td>34</td>\n",
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              "      <td>12</td>\n",
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              "      <th>104</th>\n",
              "      <td>743179</td>\n",
              "      <td>EVERWELL HEALTH AGENCY LLC</td>\n",
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              "      <td>DALLAS</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>347176</td>\n",
              "      <td>REX HOME SERVICES</td>\n",
              "      <td>1500 SUNDAY DRIVE, SUITE 113</td>\n",
              "      <td>RALEIGH</td>\n",
              "      <td>NC</td>\n",
              "      <td>27607</td>\n",
              "      <td>1625</td>\n",
              "      <td>1377</td>\n",
              "      <td>14.5</td>\n",
              "      <td>4.6</td>\n",
              "      <td>...</td>\n",
              "      <td>34</td>\n",
              "      <td>38.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.0</td>\n",
              "      <td>17</td>\n",
              "      <td>65.0</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>377</th>\n",
              "      <td>147655</td>\n",
              "      <td>AMERICAN HOME CARE EXPRESS INC</td>\n",
              "      <td>6421 NORTH HAMLIN AVENUE</td>\n",
              "      <td>LINCOLNWOOD</td>\n",
              "      <td>IL</td>\n",
              "      <td>60712</td>\n",
              "      <td>966</td>\n",
              "      <td>463</td>\n",
              "      <td>12.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>...</td>\n",
              "      <td>41</td>\n",
              "      <td>59.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.0</td>\n",
              "      <td>13</td>\n",
              "      <td>72.0</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>57539</td>\n",
              "      <td>SHARP HOME CARE</td>\n",
              "      <td>8080 DAGGET STREET SUITE 200</td>\n",
              "      <td>SAN DIEGO</td>\n",
              "      <td>CA</td>\n",
              "      <td>92111</td>\n",
              "      <td>1113</td>\n",
              "      <td>852</td>\n",
              "      <td>14.1</td>\n",
              "      <td>7.3</td>\n",
              "      <td>...</td>\n",
              "      <td>34</td>\n",
              "      <td>44.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>51.0</td>\n",
              "      <td>15</td>\n",
              "      <td>54.0</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>59465</td>\n",
              "      <td>TOTAL CARE HOME HEALTH CARE, INC</td>\n",
              "      <td>105 WEST ALAMEDA AVE, SUITE 218</td>\n",
              "      <td>BURBANK</td>\n",
              "      <td>CA</td>\n",
              "      <td>91502</td>\n",
              "      <td>686</td>\n",
              "      <td>410</td>\n",
              "      <td>16.8</td>\n",
              "      <td>11.6</td>\n",
              "      <td>...</td>\n",
              "      <td>54</td>\n",
              "      <td>63.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450</th>\n",
              "      <td>59308</td>\n",
              "      <td>GOLDEN ANGELS HOME HEALTH CARE</td>\n",
              "      <td>43136 CHRISTY STREET, SUITE 112</td>\n",
              "      <td>FREMONT</td>\n",
              "      <td>CA</td>\n",
              "      <td>94538</td>\n",
              "      <td>472</td>\n",
              "      <td>325</td>\n",
              "      <td>16.4</td>\n",
              "      <td>7.9</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>50.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>23</td>\n",
              "      <td>51.0</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>Top Performer</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 50 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e537a152-82d6-427f-bc92-10cb085aa164')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-4d3db8a0-a68c-498e-8261-a3f93ea0e62c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d3db8a0-a68c-498e-8261-a3f93ea0e62c')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4d3db8a0-a68c-498e-8261-a3f93ea0e62c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      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",
              "    [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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e537a152-82d6-427f-bc92-10cb085aa164 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-e537a152-82d6-427f-bc92-10cb085aa164');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (50) exceeds max_columns (20) limiting to first (20) columns.\n"
          ]
        }
      ],
      "source": [
        "test_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Converting Training DataFrame into DatasetDict\n",
        "\n",
        "We do this part for further preprocessing or training machine learning models."
      ],
      "metadata": {
        "id": "GjekzaPsFM3Q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I1BL-ToXXbaI"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset, DatasetDict\n",
        "train_dataset_dict = DatasetDict({\n",
        "    \"train\": Dataset.from_pandas(train_df),\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjOMoSbGSxx9"
      },
      "source": [
        "# Loading a Quantized Causal Language Model\n",
        "\n",
        "The model is configured to use 4-bit quantization with a specific quantization type and computation data type, optimizing performance and memory usage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
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      "outputs": [
        {
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        {
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            ],
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          },
          "metadata": {}
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      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNqIYtQcUBSm"
      },
      "source": [
        "## Initializing the Tokenizer for the Pretrained Model\n",
        "\n",
        "Critical step for preparing the text data for model inference."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "XDS2yYmlUAD6",
        "outputId": "258a11e5-deab-4854-bc9a-30cf044ef3ec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/676 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
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      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Text generation on Llama 2 directly before fine-tuning\n",
        "\n",
        "We're going to prompt Llama 2 directly to analyze and predict NY COPD Data.  This step will be useful for evaluating LLM accuracy later"
      ],
      "metadata": {
        "id": "7ihwFhZt4IlD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "# Creating a text generation pipeline using the pretrained model and tokenizer\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",           # Specifying the task as text generation\n",
        "    model=model,                 # Pretrained causal language model\n",
        "    tokenizer=tokenizer,         # Tokenizer corresponding to the model\n",
        "    torch_dtype=torch.bfloat16,  # Setting the data type as bfloat16 for efficiency\n",
        "    trust_remote_code=True,      # Trusting remote code from the model repository\n",
        "    device_map=\"auto\",           # Automatically mapping the computation device (CPU/GPU)\n",
        ")\n",
        "\n",
        "# Defining the input sequences for text generation, each containing metrics and a prompt for performance tier.\n",
        "'''\n",
        "**Analyzing NY COPD Data**\n",
        "Predicting and summarizing information related to % of Chronic Obstructive Pulmonary Disease and classifying the prediction into a Performance Tier:\n",
        "Performance Evaluation\n",
        "Decision Support\n",
        "Automation\n",
        "'''\n",
        "\n",
        "sequences = pipeline(\n",
        "    [\"Average Visits: 12, COPD Percentage: 22, Location: New York -> Performance Tier:\",\n",
        "     \"Average Visits: 18, COPD Percentage: 15, Location: California -> Performance Tier:\",\n",
        "     \"Average Visits: 8, COPD Percentage: 30, Location: Texas -> Performance Tier:\"],\n",
        "    max_length=200,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "# Finally we print the generated text for each input sequence\n",
        "\n",
        "for seq in sequences:\n",
        "    print(f\"Result: {seq[0]['generated_text']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9L7Fr5x7EBMQ",
        "outputId": "ad99405f-6445-4a94-9b4b-163c05f6f6be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result: Average Visits: 12, COPD Percentage: 22, Location: New York -> Performance Tier: 1. февруари в 03:18\n",
            "20.05.11 17:51:25 27.649.186.184 Average Visits: 10, COPD Percentage: 54, Location: New York -> Performance Tier: 5. февруари в 02:49\n",
            "19.12.08 16:32:02 24.97.54.93 Average Visits: 8, COPD Percentage: 22, Location: California -> Performance Tier: 7. декември в 10:20\n",
            "Result: Average Visits: 18, COPD Percentage: 15, Location: California -> Performance Tier: Tier 1 -> Visit Date: 6/10/19\n",
            " февруари 31, 2016.\n",
            "Result: Average Visits: 8, COPD Percentage: 30, Location: Texas -> Performance Tier: 1 -> Location: New York City 2. sierp. 2010.\n",
            "19. 8. COPD 19 2. Average Visits: 8, COPD Percentage: 0.7, Location: New York City 2. 7. Average Visits: 9, COPD Percentage: 45, Location: Pennsylvania (Pittsburgh) 8. 8. Average Visits: 8, COPD Percentage: 60, Location: Texas -> Performance Tier: 3 -> Location: California -> 2. 7. Average Visits: 8, COPD Percentage: 32, Location: California -> 8. 8. Average Visits: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuAx3zBeUL1q"
      },
      "source": [
        "Below we will load the configuration file in order to create the LoRA model. According to QLoRA paper, it is important to consider all linear layers in the transformer block for maximum performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQdvjTYTT1vQ"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\"q_proj\",\"v_proj\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzsYHLwIZoLm"
      },
      "source": [
        "## Loading the trainer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTBJVE4PaJwK"
      },
      "source": [
        "Here we will use the [`SFTTrainer` from TRL library](https://huggingface.co/docs/trl/main/en/sft_trainer) that gives a wrapper around transformers `Trainer` to easily fine-tune models on instruction based datasets using PEFT adapters. Let's first load the training arguments below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OCFTvGW6aspE"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 4\n",
        "gradient_accumulation_steps = 4\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 10\n",
        "logging_steps = 1\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "max_steps = 120\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    fp16=True,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I3t6b2TkcJwy"
      },
      "source": [
        "Then finally pass everthing to the trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "541f2e5f5497486287c1b60da23d60a4",
            "da74d696e0524a16971972dde290ca14",
            "fc5831f0593643948d91c9995a53137d",
            "83ba792211fe4b00bf590a6cc092fe51",
            "3583f4bb1c11406c8bd36737c99c0a11",
            "44c5fcef0b5a491bbf91f26ec5068628",
            "b3f582851ab8414685dc7cd8001f91e1",
            "0748912399794145bd2ca87e2af21e96",
            "5aa456b29e1d4801b8001ede6c748964",
            "59c2d0da238b4b4fb05eb75e06b20eaf",
            "ddb52764e6fc49068baa997917aa187a"
          ]
        },
        "id": "TNeOBgZeTl2H",
        "outputId": "12587f70-1e52-4dc1-b7b5-5e0b510eb289"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/400 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "541f2e5f5497486287c1b60da23d60a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:207: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = 512\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_dataset_dict['train'],\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"agency_name\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWplqqDjb3sS"
      },
      "source": [
        "We will also pre-process the model by upcasting the layer norms in float 32 for more stable training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7OyIvEx7b1GT"
      },
      "outputs": [],
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JApkSrCcL3O"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjvisllacNZM"
      },
      "source": [
        "Now let's train the model! Simply call `trainer.train()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "_kbS7nRxcMt7",
        "outputId": "6238fe11-38a0-47d6-98cb-2a7d45661efe"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.15.8"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/noble-ackerson/huggingface/runs/ne2t5x3f' target=\"_blank\">swept-fire-2</a></strong> to <a href='https://wandb.ai/noble-ackerson/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/noble-ackerson/huggingface' target=\"_blank\">https://wandb.ai/noble-ackerson/huggingface</a>"
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              " View run at <a href='https://wandb.ai/noble-ackerson/huggingface/runs/ne2t5x3f' target=\"_blank\">https://wandb.ai/noble-ackerson/huggingface/runs/ne2t5x3f</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='120' max='120' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [120/120 07:08, Epoch 4/5]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.166900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.999600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.602700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.857200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>4.600700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.859600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.209500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.414200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.732200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>3.835400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>4.637300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>4.881500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.704300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>2.428400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>3.701700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>2.579500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>2.869100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>4.290500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.132500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.752500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>2.296300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.753600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>2.168100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>3.838200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>3.609400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>2.138100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>2.095300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.959000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.856000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>2.235400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>2.791400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.854100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.646600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.699400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.500800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.593400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>2.685200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.495800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.293000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.313300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.293700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.604900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.747000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.124100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.195400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.308400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.585400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.487700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.644900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.440800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.224900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.097600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.399000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.114800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.377100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.535500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.150400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.061700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>1.217900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.203300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>1.358800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>1.401400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.883100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>1.227800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>1.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>1.214700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.182000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>1.681400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>1.128900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.111900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>1.410400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>1.168100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>1.213200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>1.600900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.172100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.961200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>1.119600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>1.140400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.959500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.118500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>1.115000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.983700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>1.127100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>1.245100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>1.242400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>1.338100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>1.732200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.963000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.899200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.204000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.909300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>1.219300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>1.274800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>1.001300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.979400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.821300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.997900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>1.355100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>1.330600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.954700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.796900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>0.967000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.982400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.822900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>1.076100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>1.339700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.944100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.938200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>0.943000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.885900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>1.117800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>1.305700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>0.857800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>0.943800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>1.028300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>0.967000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>1.102900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>1.099600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>0.853200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.729800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=120, training_loss=1.604409065345923, metrics={'train_runtime': 668.4451, 'train_samples_per_second': 2.872, 'train_steps_per_second': 0.18, 'total_flos': 566296234229760.0, 'train_loss': 1.604409065345923, 'epoch': 4.8})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the fine-tuned model locally\n",
        "model_save_path = \"/content/drive/MyDrive/Code/llm/demos/models/\"\n",
        "trainer.save_model(model_save_path)\n",
        "print(f\"Model saved to {model_save_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kssESr0SoIK",
        "outputId": "0a276d46-1c68-4aed-c5cc-fed7d72fc81c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved to /content/drive/MyDrive/Code/llm/demos/models/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lst_test_data = list(test_df['agency_name'])"
      ],
      "metadata": {
        "id": "E2tFZUKII4gL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(lst_test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXFR60GMvkZr",
        "outputId": "4992f234-9ac9-4a97-b265-8a764b5e41bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_size = 25\n",
        "lst_test_data_short = lst_test_data[:sample_size]"
      ],
      "metadata": {
        "id": "XizZTnNivpFx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generating Text Sequences with a Pre-trained Model"
      ],
      "metadata": {
        "id": "lPqIiWNUHXYP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.float16,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "sequences = pipeline(\n",
        "    lst_test_data_short,\n",
        "    max_length=100,  #200,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "for ix,seq in enumerate(sequences):\n",
        "    print(ix,seq[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ob_VSuyBkZF",
        "outputId": "37ea14f9-72e8-4330-d3a0-50836791b5df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 MEDCARE HOME HEALTH SERVICES INC. październopad 5, 2017\n",
            "We are very pleased with the care given to our Mom.\n",
            "Kimberly L. mazowiecki, WI 03.03.2017\n",
            "Our caregiver was amazing, caring, and very attentive to my mother's needs. I highly recommend Medcare Home Health Services\n",
            "1 ELITE HOME HEALTH CARE, INC. październiana 2001 - luty 2003 (7 mies.) (Została zatrudniona przez Elite Home Health Care).\n",
            "* ELITE HOME HEALTH CARE, INC. marzec 1997 - październik 2001 (4 lata i 7 mies.) (Została zatrudniona przez\n",
            "2 HOME HEALTH PROVIDERS, PC 1985 CHICAGO AVE SPRINGFIELD, MA 01108 PROVIDENCE CHOICE HOME HEALTH SERVICES, INC 1215 BROADWAY PROVIDENCE, RI 02908 PROVIDENT CARE AT HOME, LLC PO BOX 27795 SACRAMENT\n",
            "3 GUARDIAN HOME HEALTH CARE & HOSPICE, INC. февруари 2017 – февруари 2018 1 год. 3 мес.\n",
            "I am a CNA and i have worked in healthcare for 10 years. I would like to continue my career in healthcare but i would love to have a career in home health care. I am very dedicated to my patients and i\n",
            "4 EVERWELL HEALTH AGENCY LLC\n",
            " sierpniem 2015 – marcie 2019 (3 lat) - New Haven, Connecticut\n",
            "Assists with the provision of personal care services, including, where appropriate, assisting with the activities of daily living (ADL), assisting with healthcare related procedures. Provides for supervised care for persons who are chronically ill, infirm, and/or disabled. Provides\n",
            "5 REX HOME SERVICES, INC. kwieta 2021 – nadal Raleigh, North Carolina\n",
            "Hospice Nurse - Home Based RN Full Time 6:00am -1:55 PM\n",
            "Hospice of the Carolinas - HickORY, NC, USA\n",
            "We are looking for a caring and compassionate Registered Nurse who is looking to make a difference in people's lives to join\n",
            "6 AMERICAN HOME CARE EXPRESS INC HHA HHAV 181936988 HOME HEALTH CARE SERVICES 8200 EAST SAMOHTA AVE STE B SAN DIEGO CA 92123 JEFFREY WESTCOTT MD PA 3640 6TH AVE STE 525 BERN\n",
            "7 SHARP HOME CARE, INC. февруари 8, 2019 - Медицинска стражарка в САЩ 000758720. Помогнете на хората да живеят по-добре в своята среда.\n",
            "8 TOTAL CARE HOME HEALTH CARE, INC. sierp 2011 – marzec 2012 (9 miesięcy)\n",
            "Koordinatorka w domowej opiece medycznej\n",
            "• Monitorowanie i ocenianie stanu zdrowia klientów oraz opracowywanie ich programów terapii.• Ocenę wartości klientów.• Planowanie i nad\n",
            "9 GOLDEN ANGELS HOME HEALTH CARE INC. kwietnikowski.com 24 HR HOME HEALTH CARE SERVICE INC. 22414 W VALERO LN COTTAGE HOME HEALTH CARE, INC. 8584 E CARMEL LN HOME HEALTH SERVICE INC. 3208 W HOLLINGSWORTH\n",
            "10 QUALITY CLINICIANS CARE HOME HEALTH SERVICES LLC 3011 BARRINGTON RD PHILADELPHIA PA 19154 ABINGTON HEALTHCARE SERVICES INC 3011 BARRINGTON RD PHILADELPHIA PA 19154\n",
            "11 SENIOR HOME CARE INC. Hinweis: Wenn Sie keine UHR tragen, werden Sie nicht berechtigt, auf dieser Webseite zu handeln. Die NJHCCS ist nicht in der HOLDERNHEIMHEIMSCHAFT.\n",
            "Die Webseite ist eine WHOLLY OWNED SUBSIDIARY OF NJHCCS HOLDINGS, INC. Hinweis: Wenn Sie keine U\n",
            "12 VISITING NURSES OF NEVADA, INC. Begriffe: Visiting Nurse Service of New York, Inc. New York, NY; 1992.\n",
            "54. WOOLFITCH PHARMACAL SERVICES INC. Visiting Nurse Service of New York, Inc. New York, NY; 1993.\n",
            "55. CANADIAN INTERNATIONAL\n",
            "13 LIVINGSTONE HOME HEALTH SERVICES, INC 1618 W 12TH ST, JOHNSTOWN, PA 15219\n",
            " Hinweis: Die Angabe von EHEC 1618 W 12TH ST, JOHNSTOWN, PA 15219 ist nur VORTEILHAUSHALTEN DER BESTIMMENDE BE\n",
            "14 ALLGREEN HOME HEALTH CARE, INC. Unterscheidung of Health Care Professionals in Texas - Page 2 of 294 - NPI HUB Home Care Services in Texas - HOME CARE AGENCY LICENSE RENEWAL TEXAS HOME CARE PROFESSIONALS' ASSOCIATION, INC., AUSTIN, TX. 7034 S. FIVE F\n",
            "15 AMEDISYS HOME HEALTH OF MARYLAND INC. Hinweis: Bitte geben Sie eine gültige Postleitzahl an. Medicaid Certified Home Health Agency (CHHA) Licensed by the Maryland HEALTHCARE COMMISSION 4177 RIVER ROAD OWINGS MILLS, MD 21117 Phone: 410-878-1928 F\n",
            "16 ST JOSEPH HOME HEALTH SERVICES, LTD. февруари 2009 - февруари 2011 г.\n",
            "Д-Р САЩ ВОЕННОГОМОДАРСТВО ВОЙНИЧИ ЗЕЛЕНОВО ГОРОДСКО ВОЕННОМОДАРСТВ\n",
            "17 HOME LIFE HEALTHCARE, CORPORATION 2008 - 2020 YEARLY INC. Einzelnes Materialeigentum. Our Services. HOMELIFE HEALTH SERVICES, INC. is corporation registered with the CA Department of Consumer Affairs. HomeLife Health Services, Inc. 5801 W 3RD N ST STE 170, SAN\n",
            "18 IDEAL CARE HOME HEALTH, INC. kwieta 2010 – sierpień 2012 (2 lata i 6 mies.)\n",
            "• Wizytowanki domowe: 50+\n",
            "• Pogotowie nocne: 6+\n",
            "• Odpoczynek: 15+\n",
            "• Zdrowie i opieka nad nim: 50+\n",
            "• Utrz\n",
            "19 VILLAGECARE HOME CARE SERVICES INC. Hinweis: Bitte geben Sie nur IHSA-Mitarbeiter als Kontaktpersonen an! Bitte geben Sie nur IHSA-Mitarbeiter als Kontaktpersonen an! Bitte geben Sie nur IHSA-Mitarbeiter als Kontaktpersonen an! Home care providers are available 24/7 so patients can receive care whenever they need it. The\n",
            "20 BAYHEALTH, INC. październ 1999.\n",
            "\n",
            "## Zobacz też\n",
            "\n",
            "* KOREAN AMERICAN HOME CARE, INC.\n",
            "21 EASTWEST HOME HEALTH CARE LLC, 90-6875556 HOME HEALTH INC, ACCORD HOME HEALTH PROVIDERS INC., ACCORD HOME CARE INC., ACCORD HOME HEALTH SERVICES, INC., ALPHA HOME HEALTH INC, BEST CHOICE HEALTHCARE SERVICES INC\n",
            "22 NORTH STAR HEALTHCARE, INC. październik 2019 - Obecnie używam Microsoft Health Viewer na Androidie. Wynalazek HealthViewer umożliwia przechowywać i przekazywać dane pacjentów z różnych miejsc na równe. Oferta HealthViewer jest dostępna dla wszystkich pracowników, którzy ma\n",
            "23 CENTURA HEALTH HOME HEALTH CARE CORPORATION 1050 WEST MADISON STREET, FLOOR 2 CHICAGO, IL 60612\n",
            " październ 2002 CERTIFICATE OF NEED (CON) APPROVAL\n",
            "APPROVAL OF A CERTIFICATE OF NEED FOR A HOME HEALTH SERVICES AG\n",
            "24 SOUTHEASTERN HEALTH SERVICES OF PA, 2001 2ND STREET NW, WASHINGTON TWP, OH 43957 (2011)\n",
            " Hinweis auf eine andere Bezeichnung: WASHINGTON HEALTH CARE CTR INC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cUQFjhUcDSZy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f7cabce-56ff-4cf8-e3de-9f66ed39b946"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEDCARE HOME HEALTH SERVICES INC. październopad 5, 2017\n",
            "We are very pleased with the care given to our Mom.\n",
            "Kimberly L. mazowiecki, WI 03.03.2017\n",
            "Our caregiver was amazing, caring, and very attentive to my mother's needs. I highly recommend Medcare Home Health Services\n",
            "ELITE HOME HEALTH CARE, INC. październiana 2001 - luty 2003 (7 mies.) (Została zatrudniona przez Elite Home Health Care).\n",
            "* ELITE HOME HEALTH CARE, INC. marzec 1997 - październik 2001 (4 lata i 7 mies.) (Została zatrudniona przez\n",
            "HOME HEALTH PROVIDERS, PC 1985 CHICAGO AVE SPRINGFIELD, MA 01108 PROVIDENCE CHOICE HOME HEALTH SERVICES, INC 1215 BROADWAY PROVIDENCE, RI 02908 PROVIDENT CARE AT HOME, LLC PO BOX 27795 SACRAMENT\n",
            "GUARDIAN HOME HEALTH CARE & HOSPICE, INC. февруари 2017 – февруари 2018 1 год. 3 мес.\n",
            "I am a CNA and i have worked in healthcare for 10 years. I would like to continue my career in healthcare but i would love to have a career in home health care. I am very dedicated to my patients and i\n",
            "EVERWELL HEALTH AGENCY LLC\n",
            " sierpniem 2015 – marcie 2019 (3 lat) - New Haven, Connecticut\n",
            "Assists with the provision of personal care services, including, where appropriate, assisting with the activities of daily living (ADL), assisting with healthcare related procedures. Provides for supervised care for persons who are chronically ill, infirm, and/or disabled. Provides\n"
          ]
        }
      ],
      "source": [
        "for seq in sequences[:5]:  # Print the first 5 examples\n",
        "    print(seq[0]['generated_text'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extracting Generated Text from Model Predictions"
      ],
      "metadata": {
        "id": "5aqvmamlG8o9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def correct_answer(ans):\n",
        "  return ans  # Temporarily return the entire text\n",
        "\n",
        "answers = []\n",
        "for ix, seq in enumerate(sequences):\n",
        "    answers.append(correct_answer(seq[0]['generated_text']))\n",
        "\n",
        "print(answers[:5])  # Print the first 5 answers\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ch9iLzU2ucTY",
        "outputId": "051ed310-e03b-4f9a-c6d5-cf40258322c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"MEDCARE HOME HEALTH SERVICES INC. październopad 5, 2017\\nWe are very pleased with the care given to our Mom.\\nKimberly L. mazowiecki, WI 03.03.2017\\nOur caregiver was amazing, caring, and very attentive to my mother's needs. I highly recommend Medcare Home Health Services\", 'ELITE HOME HEALTH CARE, INC. październiana 2001 - luty 2003 (7 mies.) (Została zatrudniona przez Elite Home Health Care).\\n* ELITE HOME HEALTH CARE, INC. marzec 1997 - październik 2001 (4 lata i 7 mies.) (Została zatrudniona przez', 'HOME HEALTH PROVIDERS, PC 1985 CHICAGO AVE SPRINGFIELD, MA 01108 PROVIDENCE CHOICE HOME HEALTH SERVICES, INC 1215 BROADWAY PROVIDENCE, RI 02908 PROVIDENT CARE AT HOME, LLC PO BOX 27795 SACRAMENT', 'GUARDIAN HOME HEALTH CARE & HOSPICE, INC. февруари 2017 – февруари 2018 1 год. 3 мес.\\nI am a CNA and i have worked in healthcare for 10 years. I would like to continue my career in healthcare but i would love to have a career in home health care. I am very dedicated to my patients and i', 'EVERWELL HEALTH AGENCY LLC\\n sierpniem 2015 – marcie 2019 (3 lat) - New Haven, Connecticut\\nAssists with the provision of personal care services, including, where appropriate, assisting with the activities of daily living (ADL), assisting with healthcare related procedures. Provides for supervised care for persons who are chronically ill, infirm, and/or disabled. Provides']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_evaluate = test_df.iloc[:sample_size][['agency_name','performance_tier']]\n",
        "df_evaluate = df_evaluate.reset_index(drop=True)\n",
        "df_evaluate['performance_tier_predicted'] = answers\n",
        "df_evaluate\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1024
        },
        "id": "wzDG4gojQqYv",
        "outputId": "c11b9fa3-dec8-4721-ba2c-b36a799243e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         agency_name performance_tier  \\\n",
              "0                  MEDCARE HOME HEALTH SERVICES INC.    Above Average   \n",
              "1                        ELITE HOME HEALTH CARE, INC    Above Average   \n",
              "2                          HOME HEALTH PROVIDERS, PC    Above Average   \n",
              "3           GUARDIAN HOME HEALTH CARE & HOSPICE, INC    Above Average   \n",
              "4                         EVERWELL HEALTH AGENCY LLC    Above Average   \n",
              "5                                  REX HOME SERVICES    Above Average   \n",
              "6                     AMERICAN HOME CARE EXPRESS INC    Above Average   \n",
              "7                                    SHARP HOME CARE    Above Average   \n",
              "8                   TOTAL CARE HOME HEALTH CARE, INC    Above Average   \n",
              "9                     GOLDEN ANGELS HOME HEALTH CARE    Top Performer   \n",
              "10  QUALITY CLINICIANS CARE HOME HEALTH SERVICES LLC    Above Average   \n",
              "11                              SENIOR HOME CARE INC    Above Average   \n",
              "12                    VISITING NURSES OF NEVADA, INC    Above Average   \n",
              "13             LIVINGSTONE HOME HEALTH SERVICES, INC    Above Average   \n",
              "14                    ALLGREEN HOME HEALTH CARE, INC    Above Average   \n",
              "15                  AMEDISYS HOME HEALTH OF MARYLAND    Above Average   \n",
              "16                             ST JOSEPH HOME HEALTH    Top Performer   \n",
              "17                        HOME LIFE HEALTHCARE, CORP    Above Average   \n",
              "18                       IDEAL CARE HOME HEALTH, INC    Above Average   \n",
              "19                             VILLAGECARE HOME CARE    Above Average   \n",
              "20                                    BAYHEALTH, INC    Above Average   \n",
              "21                              EASTWEST HOME HEALTH    Above Average   \n",
              "22                        NORTH STAR HEALTHCARE, INC    Above Average   \n",
              "23                   CENTURA HEALTH HOME HEALTH CARE    Above Average   \n",
              "24                SOUTHEASTERN HEALTH SERVICES OF PA    Above Average   \n",
              "\n",
              "                           performance_tier_predicted  \n",
              "0   MEDCARE HOME HEALTH SERVICES INC. październopa...  \n",
              "1   ELITE HOME HEALTH CARE, INC. październiana 200...  \n",
              "2   HOME HEALTH PROVIDERS, PC 1985 CHICAGO AVE SPR...  \n",
              "3   GUARDIAN HOME HEALTH CARE & HOSPICE, INC. февр...  \n",
              "4   EVERWELL HEALTH AGENCY LLC\\n sierpniem 2015 – ...  \n",
              "5   REX HOME SERVICES, INC. kwieta 2021 – nadal Ra...  \n",
              "6   AMERICAN HOME CARE EXPRESS INC HHA HHAV 181936...  \n",
              "7   SHARP HOME CARE, INC. февруари 8, 2019 - Медиц...  \n",
              "8   TOTAL CARE HOME HEALTH CARE, INC. sierp 2011 –...  \n",
              "9   GOLDEN ANGELS HOME HEALTH CARE INC. kwietnikow...  \n",
              "10  QUALITY CLINICIANS CARE HOME HEALTH SERVICES L...  \n",
              "11  SENIOR HOME CARE INC. Hinweis: Wenn Sie keine ...  \n",
              "12  VISITING NURSES OF NEVADA, INC. Begriffe: Visi...  \n",
              "13  LIVINGSTONE HOME HEALTH SERVICES, INC 1618 W 1...  \n",
              "14  ALLGREEN HOME HEALTH CARE, INC. Unterscheidung...  \n",
              "15  AMEDISYS HOME HEALTH OF MARYLAND INC. Hinweis:...  \n",
              "16  ST JOSEPH HOME HEALTH SERVICES, LTD. февруари ...  \n",
              "17  HOME LIFE HEALTHCARE, CORPORATION 2008 - 2020 ...  \n",
              "18  IDEAL CARE HOME HEALTH, INC. kwieta 2010 – sie...  \n",
              "19  VILLAGECARE HOME CARE SERVICES INC. Hinweis: B...  \n",
              "20  BAYHEALTH, INC. październ 1999.\\n\\n## Zobacz t...  \n",
              "21  EASTWEST HOME HEALTH CARE LLC, 90-6875556 HOME...  \n",
              "22  NORTH STAR HEALTHCARE, INC. październik 2019 -...  \n",
              "23  CENTURA HEALTH HOME HEALTH CARE CORPORATION 10...  \n",
              "24  SOUTHEASTERN HEALTH SERVICES OF PA, 2001 2ND S...  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-cb83b61d-b7d6-4970-99cd-3cebe98c0542\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>agency_name</th>\n",
              "      <th>performance_tier</th>\n",
              "      <th>performance_tier_predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MEDCARE HOME HEALTH SERVICES INC.</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>MEDCARE HOME HEALTH SERVICES INC. październopa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ELITE HOME HEALTH CARE, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>ELITE HOME HEALTH CARE, INC. październiana 200...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HOME HEALTH PROVIDERS, PC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>HOME HEALTH PROVIDERS, PC 1985 CHICAGO AVE SPR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GUARDIAN HOME HEALTH CARE &amp; HOSPICE, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>GUARDIAN HOME HEALTH CARE &amp; HOSPICE, INC. февр...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>EVERWELL HEALTH AGENCY LLC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>EVERWELL HEALTH AGENCY LLC\\n sierpniem 2015 – ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>REX HOME SERVICES</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>REX HOME SERVICES, INC. kwieta 2021 – nadal Ra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AMERICAN HOME CARE EXPRESS INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>AMERICAN HOME CARE EXPRESS INC HHA HHAV 181936...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SHARP HOME CARE</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>SHARP HOME CARE, INC. февруари 8, 2019 - Медиц...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>TOTAL CARE HOME HEALTH CARE, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>TOTAL CARE HOME HEALTH CARE, INC. sierp 2011 –...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GOLDEN ANGELS HOME HEALTH CARE</td>\n",
              "      <td>Top Performer</td>\n",
              "      <td>GOLDEN ANGELS HOME HEALTH CARE INC. kwietnikow...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>QUALITY CLINICIANS CARE HOME HEALTH SERVICES LLC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>QUALITY CLINICIANS CARE HOME HEALTH SERVICES L...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>SENIOR HOME CARE INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>SENIOR HOME CARE INC. Hinweis: Wenn Sie keine ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>VISITING NURSES OF NEVADA, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>VISITING NURSES OF NEVADA, INC. Begriffe: Visi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LIVINGSTONE HOME HEALTH SERVICES, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>LIVINGSTONE HOME HEALTH SERVICES, INC 1618 W 1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>ALLGREEN HOME HEALTH CARE, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>ALLGREEN HOME HEALTH CARE, INC. Unterscheidung...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>AMEDISYS HOME HEALTH OF MARYLAND</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>AMEDISYS HOME HEALTH OF MARYLAND INC. Hinweis:...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ST JOSEPH HOME HEALTH</td>\n",
              "      <td>Top Performer</td>\n",
              "      <td>ST JOSEPH HOME HEALTH SERVICES, LTD. февруари ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>HOME LIFE HEALTHCARE, CORP</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>HOME LIFE HEALTHCARE, CORPORATION 2008 - 2020 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>IDEAL CARE HOME HEALTH, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>IDEAL CARE HOME HEALTH, INC. kwieta 2010 – sie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>VILLAGECARE HOME CARE</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>VILLAGECARE HOME CARE SERVICES INC. Hinweis: B...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>BAYHEALTH, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>BAYHEALTH, INC. październ 1999.\\n\\n## Zobacz t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>EASTWEST HOME HEALTH</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>EASTWEST HOME HEALTH CARE LLC, 90-6875556 HOME...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NORTH STAR HEALTHCARE, INC</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>NORTH STAR HEALTHCARE, INC. październik 2019 -...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>CENTURA HEALTH HOME HEALTH CARE</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>CENTURA HEALTH HOME HEALTH CARE CORPORATION 10...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>SOUTHEASTERN HEALTH SERVICES OF PA</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>SOUTHEASTERN HEALTH SERVICES OF PA, 2001 2ND S...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb83b61d-b7d6-4970-99cd-3cebe98c0542')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3c430804-f866-45f6-803a-83e9c80afccd\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c430804-f866-45f6-803a-83e9c80afccd')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3c430804-f866-45f6-803a-83e9c80afccd button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      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",
              "    [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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb83b61d-b7d6-4970-99cd-3cebe98c0542 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-cb83b61d-b7d6-4970-99cd-3cebe98c0542');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's evaluate these results"
      ],
      "metadata": {
        "id": "jq-0V5rVwkEJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb\n",
        "\n",
        "wandb.init(project=\"model_comparison\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983,
          "referenced_widgets": [
            "b02d095685134729b9bcfb1f01dcaec7",
            "aa5bace7190845e98c29132cb3046d78",
            "ffc5c05044c14740921d491c033557b1",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/learning_rate</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/loss</td><td>▂▆▄█▃▅▂▃▇▄▆▃▅▂▃▂▃▂▂▂▃▂▃▃▃▂▂▂▂▁▂▂▂▁▁▁▁▁▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>4.8</td></tr><tr><td>train/global_step</td><td>120</td></tr><tr><td>train/learning_rate</td><td>0.0002</td></tr><tr><td>train/loss</td><td>0.7262</td></tr><tr><td>train/total_flos</td><td>566296234229760.0</td></tr><tr><td>train/train_loss</td><td>1.60637</td></tr><tr><td>train/train_runtime</td><td>591.5764</td></tr><tr><td>train/train_samples_per_second</td><td>3.246</td></tr><tr><td>train/train_steps_per_second</td><td>0.203</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">dashing-river-1</strong> at: <a href='https://wandb.ai/noble-ackerson/huggingface/runs/md14o9v8' target=\"_blank\">https://wandb.ai/noble-ackerson/huggingface/runs/md14o9v8</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "Syncing run <strong><a href='https://wandb.ai/noble-ackerson/model_comparison/runs/9949ci19' target=\"_blank\">treasured-dream-1</a></strong> to <a href='https://wandb.ai/noble-ackerson/model_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/noble-ackerson/model_comparison' target=\"_blank\">https://wandb.ai/noble-ackerson/model_comparison</a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating the base model's pipeline\n"
      ],
      "metadata": {
        "id": "yQQ3nfH_DsOc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model, # The base model before fine-tuning\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "test_prompts = [\n",
        "    'Average Visits: 25.9, COPD Percentage: 33, Location: VA',\n",
        "    'Average Visits: 18.7, COPD Percentage: 28, Location: MD',\n",
        "    'Average Visits: 14.5, COPD Percentage: 22, Location: NC',\n",
        "    'Average Visits: 22.0, COPD Percentage: 26, Location: NY',\n",
        "    'Average Visits: 19.3, COPD Percentage: 36, Location: NJ',\n",
        "    'Average Visits: 16.2, COPD Percentage: 31, Location: GA',\n",
        "    'Average Visits: 17.4, COPD Percentage: 30, Location: SC',\n",
        "    'Average Visits: 20.8, COPD Percentage: 28, Location: MA',\n",
        "    'Average Visits: 18.1, COPD Percentage: 24, Location: CT',\n",
        "    'Average Visits: 15.9, COPD Percentage: 32, Location: RI',\n",
        "    'Average Visits: 19.7, COPD Percentage: 19, Location: PA',\n",
        "    'Average Visits: 16.5, COPD Percentage: 39, Location: DE',\n",
        "]\n",
        "\n",
        "\n",
        "ground_truth = [\n",
        "    \"Performance Tier: Tier 2; Virginia's COPD care reflects moderate performance.\",\n",
        "    \"Performance Tier: Tier 1; Maryland leads in COPD care with frequent visits.\",\n",
        "    \"Performance Tier: Tier 3; North Carolina needs to enhance COPD care.\",\n",
        "    \"Performance Tier: Tier 2; New York's healthcare provides moderate COPD care.\",\n",
        "    \"Performance Tier: Tier 1; New Jersey excels in COPD care.\",\n",
        "    \"Performance Tier: Tier 3; Georgia shows room for improvement in COPD care.\",\n",
        "    \"Performance Tier: Tier 2; South Carolina's COPD care is satisfactory.\",\n",
        "    \"Performance Tier: Tier 1; Massachusetts demonstrates excellent COPD care.\",\n",
        "    \"Performance Tier: Tier 2; Connecticut provides balanced COPD care.\",\n",
        "    \"Performance Tier: Tier 3; Rhode Island's COPD care requires improvement.\",\n",
        "    \"Performance Tier: Tier 1; Pennsylvania's healthcare system delivers top-tier COPD care.\",\n",
        "    \"Performance Tier: Tier 2; Delaware shows moderate performance in COPD care.\",\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "# Generate raw responses using the base model\n",
        "raw_base_model_responses = base_model_pipeline(\n",
        "    test_prompts,\n",
        "    max_length=100,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "# Extract the 'generated_text' from each response\n",
        "base_model_responses = [resp[0]['generated_text'] for resp in raw_base_model_responses]\n",
        "\n",
        "# Define evaluation function\n",
        "def evaluate_accuracy(predictions, ground_truth):\n",
        "    correct_count = 0\n",
        "    for pred, truth in zip(predictions, ground_truth):\n",
        "        if pred == truth: # Adjust this comparison based on your specific task\n",
        "            correct_count += 1\n",
        "\n",
        "    accuracy = correct_count / len(predictions)\n",
        "    return accuracy\n",
        "\n",
        "# Evaluate the base model's accuracy\n",
        "base_model_accuracy = evaluate_accuracy(base_model_responses, ground_truth)\n",
        "\n"
      ],
      "metadata": {
        "id": "3C1zS6IN6q34"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating the fine-tuned model's pipeline"
      ],
      "metadata": {
        "id": "FhOyKGA4DnyY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the evaluation function\n",
        "def evaluate_accuracy(predictions, ground_truth):\n",
        "    correct_count = 0\n",
        "    for pred, truth in zip(predictions, ground_truth):\n",
        "        if pred == truth:\n",
        "            correct_count += 1\n",
        "\n",
        "    accuracy = correct_count / len(predictions)\n",
        "    return accuracy\n",
        "\n",
        "# Base Model Pipeline\n",
        "base_model_pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model, # The base model before fine-tuning\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "# Fine-Tuned Model Pipeline\n",
        "fine_tuned_model_pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=trainer.model, # The fine-tuned model\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "# Generate responses for the base model\n",
        "base_model_responses = base_model_pipeline(\n",
        "    test_prompts,\n",
        "    max_length=100,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "# Extract the generated text from the base model responses\n",
        "base_model_predictions = [seq[0]['generated_text'] for seq in base_model_responses]\n",
        "\n",
        "# Generate responses for the fine-tuned model\n",
        "fine_tuned_model_responses = fine_tuned_model_pipeline(\n",
        "    test_prompts,\n",
        "    max_length=100,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "# Extract the generated text from the fine-tuned model responses\n",
        "fine_tuned_model_predictions = [seq[0]['generated_text'] for seq in fine_tuned_model_responses]\n",
        "\n",
        "\n",
        "# Extract the generated text from the responses\n",
        "fine_tuned_model_predictions = [seq[0]['generated_text'] for seq in fine_tuned_model_responses]\n",
        "\n",
        "# Evaluate the base model's accuracy\n",
        "base_model_accuracy = evaluate_accuracy(base_model_predictions, ground_truth)\n",
        "\n",
        "# Evaluate the fine-tuned model's accuracy\n",
        "fine_tuned_model_accuracy = evaluate_accuracy(fine_tuned_model_predictions, ground_truth)\n",
        "\n",
        "# Log to wandb\n",
        "wandb.log({\"Base Model Accuracy\": base_model_accuracy, \"Fine-Tuned Model Accuracy\": fine_tuned_model_accuracy})\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLotDkky6uEh",
        "outputId": "76b4cb14-2a28-4443-a339-64d98b4100f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
          ]
        }
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      "source": [],
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        "id": "R7UTdaLexumM"
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