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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/segmed/gemma_mistral/blob/main/notebooks/datasets_example.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ],
      "metadata": {
        "id": "WIOpkckR9nb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlYlaSulf9U9",
        "outputId": "e74a4904-b9be-4fe8-fabe-e6e732e30a3d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Feb 24 17:26:57 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   53C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# if you are using google colab\n",
        "\n",
        "import os\n",
        "from google.colab import userdata\n",
        "os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN')\n",
        "if os.environ[\"HF_TOKEN\"] == None:\n",
        "    from huggingface_hub import notebook_login\n",
        "    notebook_login()"
      ],
      "metadata": {
        "id": "bfJ1OoRaSavz"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
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      "execution_count": 3,
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        "id": "jwzAJeZKSRVy",
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        "outputId": "c5fb0ec5-6932-48e1-a197-bf50b1b7ea56"
      },
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m167.9/167.9 MB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m83.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -qq \"transformers==4.38.0\" --upgrade\n",
        "!pip -qq install bitsandbytes accelerate datasets pandas peft trl tqdm evaluate\n",
        "!pip -qq install \"torch>=2.1.1\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset('medmcqa', split='train').shuffle(seed=42).train_test_split(test_size=0.01)\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449,
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        "id": "B1y1u-WLhGEY",
        "outputId": "fbf1c2c6-a74b-4a8c-baa9-a119a6834579"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/10.7k [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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              "Downloading data:   0%|          | 0.00/85.9M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2749e98ff5e34f42ba33ed206c3e3935"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/936k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f46f685af5c4cc0b015052570e8f3e7"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.48M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6aa8cb53918b4fff8004830edd6b0c65"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/182822 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c4d9f8261e448748bb075bd6904c8cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/6150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c97a817b15db44b3a11894f9d4ff4ccb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/4183 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39e8e3497a6c45828267ec65364eaec9"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'question', 'opa', 'opb', 'opc', 'opd', 'cop', 'choice_type', 'exp', 'subject_name', 'topic_name'],\n",
              "        num_rows: 180993\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'question', 'opa', 'opb', 'opc', 'opd', 'cop', 'choice_type', 'exp', 'subject_name', 'topic_name'],\n",
              "        num_rows: 1829\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df_train = pd.DataFrame(dataset['train'])\n",
        "df_train.head(1)"
      ],
      "metadata": {
        "id": "7pWHLGxT3LPn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
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        "outputId": "9f5c07ef-32a9-4cbb-8f68-4ea602ba4029"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                     id  \\\n",
              "0  0114fd9e-52e5-40ca-a0b4-fb3c146d275f   \n",
              "\n",
              "                                            question  opa  opb  opc  opd  cop  \\\n",
              "0  A herd immunity of over .........is considered...  50%  55%  60%  70%    3   \n",
              "\n",
              "  choice_type  exp                  subject_name topic_name  \n",
              "0      single  70%  Social & Preventive Medicine       None  "
            ],
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              "        document.querySelector('#df-a0b281b5-bc86-4a00-af01-740bf3aca4ab button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-a0b281b5-bc86-4a00-af01-740bf3aca4ab');\n",
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              "          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",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df_test = pd.DataFrame(dataset['test'])\n",
        "df_test.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "id": "AcYKf8lii4nk",
        "outputId": "5888d54d-d1ce-4aef-fab6-33b11402188f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id  \\\n",
              "0  acd5220c-7b55-4bd4-bdd4-4cffe23eefb7   \n",
              "\n",
              "                                        question        opa   opb     opc  \\\n",
              "0  Which mineral causes skin allergy: March 2013  Magnesium  Zinc  Copper   \n",
              "\n",
              "      opd  cop choice_type                 exp subject_name topic_name  \n",
              "0  Nickel    3       multi  Ans. D i.e. Nickel         Skin       None  "
            ],
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              "      <td>None</td>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-60d386a5-92d4-4ace-b454-6310c53dd430 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-60d386a5-92d4-4ace-b454-6310c53dd430');\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",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 1829,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1829,\n        \"samples\": [\n          \"31347b3e-3b52-4e27-af9b-5c664a6afa07\",\n          \"2c59514f-d16f-4d78-979f-716029e19f06\",\n          \"11221293-ecc0-45ed-b030-4f5534264993\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1829,\n        \"samples\": [\n          \"A 5yr old boy presents with sore throat for the last two days On examination he has a greyish white pseudomembrane around his tonsils Diphtheria is suspected His brother has recieved last booster 22 months back What prophylactic measure should be adopted for the brother\",\n          \"Recently according to ACOG and society for maternal-fetal medicine, Active labor is redefined to sta at\",\n          \"Most common organism causing fungal infection of oral cavity -\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1727,\n        \"samples\": [\n          \"Glottic cancer\",\n          \"Nitroglycerine\",\n          \"Bowmann's membrane\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opb\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1746,\n        \"samples\": [\n          \"Pulse oximetry.\",\n          \"IV\",\n          \"Only multiplication\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opc\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1744,\n        \"samples\": [\n          \"Mercuric poisoning\",\n          \"Transference manifestations\",\n          \"Mother is carrier\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1650,\n        \"samples\": [\n          \"Thymus\",\n          \"Eczema\",\n          \"IV DIGOXIN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"choice_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"single\",\n          \"multi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exp\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1631,\n        \"samples\": [\n          \"Pneumonia with pneumatoceles (multiple thin-walled breaths of air containing cysts) is s/o staphylococcal pneumonia.\\n\\nClinical features of staphylococcal pneumonia\\n\\nThe illness usually follows upper respiratory tract infection, pyoderma or another associated purulent disease.\\nBesides the usual features of pneumonia child has fever and anorexia and is listless and irritable.\\nThe abdomen is usually distended due to septicemia and ileus.\\nCyanosis may be present\\nProgression of the symptoms and signs is rapid.\\n\\nDiagnosis-\\n\\nThe diagnosis of staphylococcal pneumonia is suspected in a newborn or an infant with respiratory infection who has evidence of staphylococcal infection elsewhere in the body (here it is pyoderma).\\nComplications such as pyopneumothorax and pericarditis are highly suggestive of diagnosis (Empyeina in a child below 2 yrs of age is nearly always staphylococcal in etiology)\\nPresence of Pneumatoceles is considered pathognomic for staphylococcal pneumonia (It can also be seen in pneumonia due to Klebsiella)\\n\\nNote: Pyoderma or impetigo is a superficial infection of the skin caused primarily by group A streptococci and occasionally by other streptococci and staphylococci.\\n\\nTachypnea is the most consistent clinical manifestation of pneumonia.\\u00a0 - Nelson 18th /e 1797\",\n          \"The initial presentation of thoracic outlet syndrome(eg.,in cervical rib)is dependent on whether the compression is primarily vascular,neurogenic or a combination of both.Vascular thoracic outlet syndrome can involve subclan aery or vein presenting with colour changes of affected upper extremity,claudication,or diffuse pain.Neurogenic syndrome involves brachial plexus presenting as sensory loss,pain or paraesthesia. Reference:Bailey & Love's sho practise of surgery,25th edition,pageno:895.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subject_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"Skin\",\n          \"Psychiatry\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"topic_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 436,\n        \"samples\": [\n          \"Neuro-Ophthalmology\",\n          \"Carcinoma Cervix\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['subject_name'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1rXVIvMFnUF",
        "outputId": "c4736c95-c7c5-47ec-8669-129ab1d9b294"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Medicine                        17718\n",
              "Surgery                         16660\n",
              "Pathology                       14740\n",
              "Anatomy                         14417\n",
              "Pharmacology                    13609\n",
              "Social & Preventive Medicine    11772\n",
              "Microbiology                    11200\n",
              "Gynaecology & Obstetrics         9903\n",
              "Dental                           8852\n",
              "Physiology                       8741\n",
              "Biochemistry                     8194\n",
              "Pediatrics                       7959\n",
              "Ophthalmology                    6881\n",
              "Forensic Medicine                5837\n",
              "ENT                              4869\n",
              "Psychiatry                       4403\n",
              "Radiology                        4355\n",
              "Anaesthesia                      3134\n",
              "Unknown                          3025\n",
              "Orthopaedics                     2969\n",
              "Skin                             1755\n",
              "Name: subject_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['question'].apply(len).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlVcCUQnFw3C",
        "outputId": "48583cbf-8bab-42c7-ee77-0c5cc4f53d3a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78.73387921079821"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
        "model_name = \"google/gemma-7b-it\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map=\"auto\",\n",
        "                                             quantization_config=quantization_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
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        "id": "uYh86qCEXHg6",
        "outputId": "f4850edb-4ba0-4476-f650-046a89f9b5ac"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/888 [00:00<?, ?B/s]"
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
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            "application/vnd.jupyter.widget-view+json": {
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            }
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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        {
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          },
          "metadata": {}
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            }
          },
          "metadata": {}
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        {
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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        {
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            ],
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "5c824c6fa05e491a8af98f7d39561e8f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "choice_map = np.array([\"(a)\", \"(b)\", \"(c)\", \"(d)\"])\n",
        "\n",
        "def get_content(row):\n",
        "    question = row['question']\n",
        "    cop = row['cop']\n",
        "    opa = row['opa']\n",
        "    opb = row['opb']\n",
        "    opc = row['opc']\n",
        "    opd = row['opd']\n",
        "    exp = row['exp']\n",
        "    topic_name = row['topic_name']\n",
        "    optiona = f\"(a) {opa}\"\n",
        "    optionb = f\"(b) {opb}\"\n",
        "    optionc = f\"(c) {opc}\"\n",
        "    optiond = f\"(d) {opd}\"\n",
        "    exp = row['exp']\n",
        "    answer = choice_map[cop]\n",
        "\n",
        "    system_prompt_choice = \"\"\"Analyze the question and determine which choice is the correct one and return the answer as one or more of the following \"(a)\", \"(b)\", \"(c)\", \"(d)\" followed by an explanation:\"\"\"\n",
        "    return f\"{system_prompt_choice}\\n{question}\\n{optiona} {optionb} {optionc} {optiond}\", answer, exp\n",
        "\n",
        "def get_test_sample(row):\n",
        "    content, answer, exp = get_content(row)\n",
        "    chat = [\n",
        "        { \"role\": \"user\", \"content\": content },\n",
        "    ]\n",
        "    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True, add_special_tokens=False)\n",
        "    return prompt\n",
        "\n",
        "def get_test_sample_answer(row):\n",
        "    content, answer, exp = get_content(row)\n",
        "    return answer, exp\n",
        "\n",
        "def get_training_sample(row):\n",
        "    content, answer, exp = get_content(row)\n",
        "    chat = [\n",
        "        { \"role\": \"user\", \"content\": content },\n",
        "        { \"role\": \"model\", \"content\": f\"{answer}\\n{exp}\" },\n",
        "    ]\n",
        "    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False, add_special_tokens=False)\n",
        "    return prompt"
      ],
      "metadata": {
        "id": "9FlHIPInGQSa"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in df_train.loc[:1].iterrows():\n",
        "    sample = get_training_sample(row)\n",
        "    display(sample)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "IFK5iuXXXA9M",
        "outputId": "eb9087d7-d127-4685-ae5c-d41ecd495257"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'<bos><start_of_turn>user\\nAnalyze the question and determine which choice is the correct one and return the answer as one or more of the following \"(a)\", \"(b)\", \"(c)\", \"(d)\" followed by an explanation:\\nA herd immunity of over .........is considered necessary to prevent epidemic spread of diphtheria:\\n(a) 50% (b) 55% (c) 60% (d) 70%<end_of_turn>\\n<start_of_turn>model\\n(d)\\n70%<end_of_turn>\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'<bos><start_of_turn>user\\nAnalyze the question and determine which choice is the correct one and return the answer as one or more of the following \"(a)\", \"(b)\", \"(c)\", \"(d)\" followed by an explanation:\\nThe mother of a one and a half year old child gives history of a white reflex from one eye for the past 1 month. On computed tomography scan of the orbit, there is calcification seen within the globe. The most likely diagnosis is –\\n(a) Congenital cataract (b) Retinoblastoma (c) Endophthalmitis (d) Coats disease<end_of_turn>\\n<start_of_turn>model\\n(b)\\nWhile pupillary reflex (leukocoria) with calcification in tumor mass is characteristic of retinoblastoma.<end_of_turn>\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in df_test.loc[:1].iterrows():\n",
        "    sample = get_test_sample(row)\n",
        "    display(sample)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "oddw0jwGXX55",
        "outputId": "572ca802-a0e3-4bd2-f0a9-463167a60956"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'<bos><start_of_turn>user\\nAnalyze the question and determine which choice is the correct one and return the answer as one or more of the following \"(a)\", \"(b)\", \"(c)\", \"(d)\" followed by an explanation:\\nWhich mineral causes skin allergy: March 2013\\n(a) Magnesium (b) Zinc (c) Copper (d) Nickel<end_of_turn>\\n<start_of_turn>model\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'<bos><start_of_turn>user\\nAnalyze the question and determine which choice is the correct one and return the answer as one or more of the following \"(a)\", \"(b)\", \"(c)\", \"(d)\" followed by an explanation:\\nA completed denture patient complains that he has pain in his throat during swallowing. This indicates:\\n(a) Over extended PPS (b) Over extended lingual flange (c) Over extended Lingual flange in retromylohyoid region (d) His tonsils are inflammed<end_of_turn>\\n<start_of_turn>model\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['text'] = df_train.apply(get_training_sample, axis=1)\n",
        "df_test['text'] = df_test.apply(get_test_sample, axis=1)"
      ],
      "metadata": {
        "id": "bA6XAVT3iZID"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(row, model, tokenizer):\n",
        "    y_pred = None\n",
        "    prompt = get_test_sample(row)\n",
        "    input_ids = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "    outputs = model.generate(**input_ids, max_new_tokens=64, do_sample=True, top_k=0, num_return_sequences=1, temperature=0.01, eos_token_id=tokenizer.eos_token_id)\n",
        "    result = tokenizer.decode(outputs[0])\n",
        "    answer = result.split(\"<start_of_turn>model\")[-1].lower()\n",
        "    y_pred = \"none\"\n",
        "    for index, choice in enumerate(choice_map):\n",
        "        if choice in answer:\n",
        "            y_pred = choice\n",
        "\n",
        "    return y_pred, input_ids['input_ids'].shape[1]"
      ],
      "metadata": {
        "id": "ewp5Q85unvT2"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in df_test.loc[:3].iterrows():\n",
        "    choice, explanation = get_test_sample_answer(row)\n",
        "    prediction, num_tokens = predict(row, model, tokenizer)\n",
        "    display(f\"prediction - {prediction}, reference {choice}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "vyQYOQ14ppsH",
        "outputId": "12e0c18b-bcfd-4f5b-8cf6-e10db0a6608d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'prediction - (d), reference (d)'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'prediction - (c), reference (c)'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'prediction - (b), reference (b)'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'prediction - (d), reference (c)'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "references = []\n",
        "predictions = []\n",
        "times = []\n",
        "num_tokens = []\n",
        "\n",
        "for index, row in tqdm(df_test.loc[:100].iterrows(), total=df_test.shape[0]):\n",
        "    seconds = time.time()\n",
        "    choice, explanation = get_test_sample_answer(row)\n",
        "    reference_index = np.where(choice_map == choice)[0][0]\n",
        "    references.append(reference_index)\n",
        "    prediction, num_token = predict(row, model, tokenizer)\n",
        "    prediction_index = np.where(choice_map == prediction)[0][0]\n",
        "    predictions.append(prediction_index)\n",
        "    times.append(time.time() - seconds)\n",
        "    num_tokens.append(num_token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikf3YH08v7Rt",
        "outputId": "a2305c03-a84a-4f18-dbd6-eb034d744f4f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  6%|▌         | 101/1829 [11:46<3:21:21,  6.99s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy_metric = evaluate.load(\"accuracy\")\n",
        "results = accuracy_metric.compute(predictions=predictions, references=references)\n",
        "display(\"accuracy:\", results[\"accuracy\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "_6u-_CP84cCu",
        "outputId": "ca48cf97-0034-43ee-b709-11f95f4c83fa"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'accuracy:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0.44554455445544555"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average = sum(times) / len(times)\n",
        "display(\"duration per sample:\", round(average, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "52JPMs_JCJXp",
        "outputId": "180966ac-d128-499b-e79d-51508e83cd88"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'duration per sample:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "6.99"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average = sum(num_tokens) / len(num_tokens)\n",
        "display(\"avg tokens per sample:\", round(average, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "8Ma-yEJBLhzP",
        "outputId": "f15d7dea-da68-4bbc-a71c-e9fe65617183"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'avg tokens per sample:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "94.09"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
        "model_name = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map=\"auto\",\n",
        "                                             quantization_config=quantization_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
          "referenced_widgets": [
            "dee50b5dd647439d9cda8aa7d5860b35",
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            "cec23dcb95e44fe991b0a618b241a9a4",
            "f3a3642606c045039a01bdd08f315911",
            "29837c3413004735a8cadbe56bd4d1f3",
            "5403629ade71423292048d68efe642f6",
            "815635ac59f2498fbcb393745aa93e37",
            "36371ed69a2b4e8bbe77ad99b522276b",
            "0e51ad399d9b4153865dc34fa57346f6",
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            "aefd093918334c9a92578d9b227664bf",
            "1db43a73f599469da368db60106e3ab5",
            "ca84798a244646119c1ae97e97039cd1",
            "e45cd8cee995492cae89d7430b146910",
            "385b414e3a344209a74e17f3712355c8",
            "3056cfbf380e450daa17d07d7c3bb708",
            "1e4081ef2c524323b113e89e86945069",
            "18e26ae30c5741d58bf00b7448d55965",
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        "id": "KScvzKZxiJqi",
        "outputId": "edb1e955-01ce-4c15-e42e-54bcdd75f648"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
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            }
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          "metadata": {}
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          "metadata": {}
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          },
          "metadata": {}
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
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            }
          },
          "metadata": {}
        },
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              "model_id": "cc2328aee8d148b2a8ce52c574a90c82"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "references = []\n",
        "predictions = []\n",
        "times = []\n",
        "num_tokens = []\n",
        "\n",
        "for index, row in tqdm(df_test.loc[:100].iterrows(), total=df_test.shape[0]):\n",
        "    seconds = time.time()\n",
        "    choice, explanation = get_test_sample_answer(row)\n",
        "    reference_index = np.where(choice_map == choice)[0][0]\n",
        "    references.append(reference_index)\n",
        "    prediction, num_token = predict(row, model, tokenizer)\n",
        "    prediction_index = np.where(choice_map == prediction)[0][0]\n",
        "    predictions.append(prediction_index)\n",
        "    times.append(time.time() - seconds)\n",
        "    num_tokens.append(num_token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3maKZkmpvSfB",
        "outputId": "cc9b5aea-f3fe-4e71-f7d4-d2578003d683"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
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            "  4%|▎         | 67/1829 [07:15<3:12:25,  6.55s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▎         | 68/1829 [07:21<3:11:19,  6.52s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 69/1829 [07:28<3:10:25,  6.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 70/1829 [07:34<3:09:47,  6.47s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 71/1829 [07:40<3:09:18,  6.46s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 72/1829 [07:47<3:09:46,  6.48s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 73/1829 [07:53<3:09:29,  6.47s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 74/1829 [08:00<3:09:13,  6.47s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 75/1829 [08:06<3:08:48,  6.46s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 76/1829 [08:13<3:08:25,  6.45s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 77/1829 [08:19<3:08:17,  6.45s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 78/1829 [08:26<3:08:00,  6.44s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 79/1829 [08:32<3:08:26,  6.46s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 80/1829 [08:39<3:07:59,  6.45s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 81/1829 [08:45<3:08:31,  6.47s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  4%|▍         | 82/1829 [08:52<3:08:51,  6.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 83/1829 [08:58<3:08:25,  6.48s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 84/1829 [09:05<3:08:01,  6.46s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 85/1829 [09:11<3:08:40,  6.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 86/1829 [09:18<3:08:14,  6.48s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 87/1829 [09:24<3:08:40,  6.50s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 88/1829 [09:31<3:08:58,  6.51s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 89/1829 [09:37<3:09:11,  6.52s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 90/1829 [09:44<3:09:12,  6.53s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▍         | 91/1829 [09:50<3:08:18,  6.50s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 92/1829 [09:57<3:08:32,  6.51s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 93/1829 [10:03<3:07:46,  6.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 94/1829 [10:10<3:07:58,  6.50s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 95/1829 [10:16<3:07:15,  6.48s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 96/1829 [10:23<3:07:36,  6.50s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 97/1829 [10:29<3:09:28,  6.56s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 98/1829 [10:36<3:08:03,  6.52s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 99/1829 [10:43<3:10:40,  6.61s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  5%|▌         | 100/1829 [10:49<3:09:03,  6.56s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "  6%|▌         | 101/1829 [10:56<3:07:08,  6.50s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy_metric = evaluate.load(\"accuracy\")\n",
        "results = accuracy_metric.compute(predictions=predictions, references=references)\n",
        "display(\"accuracy:\", results[\"accuracy\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "ZymFGAwlvSc2",
        "outputId": "e5d23213-14aa-4a86-a94f-c1a320c81d3a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'accuracy:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0.2079207920792079"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average = sum(times) / len(times)\n",
        "display(\"duration per sample:\", round(average, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "mAtJgaPgEFu4",
        "outputId": "e1423134-6abd-4a38-916f-cb58d2e812bc"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'duration per sample:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "6.5"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average = sum(num_tokens) / len(num_tokens)\n",
        "display(\"avg tokens per sample:\", round(average, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "V3WL0f_HLrfS",
        "outputId": "1518357a-5a05-4be6-d6fa-b9a6e213d100"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'avg tokens per sample:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "104.23"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import runtime\n",
        "runtime.unassign()"
      ],
      "metadata": {
        "id": "DDOPJOC5vSZx"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LGhXuNA_RxYF"
      },
      "execution_count": 23,
      "outputs": []
    }
  ]
}