{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8RTc2PmnX-v"
      },
      "source": [
        "Initial setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "kGW7vfRkrqHe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b28824a9-de8c-4f8c-da38-b00f8388c23b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.28.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (3.17.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2024.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.67.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2025.1.31)\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2I850FIsCVNw"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import login, upload_folder\n",
        "from google.colab import userdata\n",
        "\n",
        "HF_TOKEN = userdata.get('HF_TOKEN')\n",
        "login(HF_TOKEN, add_to_git_credential=True)\n",
        "REPO_ID='flunardelli/llm-metaeval'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import snapshot_download\n",
        "snapshot_download(repo_id=REPO_ID, repo_type=\"dataset\", allow_patterns=\"*.json\", local_dir=\"./results\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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              "(…)Fresults_2024-11-25T03-24-06.031245.json:   0%|          | 0.00/6.79k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "(…)Fresults_2024-11-25T03-21-54.085274.json:   0%|          | 0.00/6.81k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "(…)Fresults_2024-11-25T03-22-57.001476.json:   0%|          | 0.00/6.81k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "021ab4d9d8af41a0981ef9034f334d3c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/results'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "from datetime import datetime\n",
        "import re\n",
        "import pandas as pd\n",
        "from natsort import natsorted\n",
        "\n",
        "def parse_json(file_path):\n",
        "    try:\n",
        "        with open(file_path, 'r') as file:\n",
        "            data = json.load(file)\n",
        "\n",
        "        results = data.get(\"results\", {})\n",
        "        acc_value = None\n",
        "        for key, value in results.items():\n",
        "            if \"acc,none\" in value:\n",
        "                acc_value = value[\"acc,none\"]\n",
        "                break\n",
        "\n",
        "        model_name = data.get(\"model_name\")\n",
        "\n",
        "        return {\n",
        "            \"model_name\": model_name,\n",
        "            \"task\": key,\n",
        "            \"acc_value\": acc_value\n",
        "        }\n",
        "\n",
        "    except (json.JSONDecodeError, KeyError) as e:\n",
        "        print(f\"Error parsing {file_path}: {e}\")\n",
        "        return None\n",
        "\n",
        "\n",
        "def extract_datetime(file_name):\n",
        "    pattern = r\"results_(\\d{4}-\\d{2}-\\d{2}T\\d{2}-\\d{2}-\\d{2}\\.\\d+)\\.json\"\n",
        "    match = re.search(pattern, file_name)\n",
        "    if match:\n",
        "        return match.group(1)  # Extract the first capturing group\n",
        "    return None\n",
        "\n",
        "def process_json_files(base_directory):\n",
        "    results = []\n",
        "    for root, _, files in os.walk(base_directory):\n",
        "        for file_name in files:\n",
        "            if file_name.endswith(\".json\"):\n",
        "                file_path = os.path.join(root, file_name)\n",
        "                parsed_data = parse_json(file_path)\n",
        "                parsed_data['datetime'] = extract_datetime(file_name)\n",
        "                if parsed_data:\n",
        "                    results.append(parsed_data)\n",
        "\n",
        "    return results\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    directory_path = \"results/evals/output\"\n",
        "    extracted_data = process_json_files(directory_path)\n",
        "\n",
        "    extracted_data_sorted = sorted(\n",
        "        extracted_data,\n",
        "        key=lambda x: (x['model_name'], x['task'], x['datetime'])\n",
        "    )\n",
        "\n",
        "    # Group by (model_name, task) and keep only the latest entry\n",
        "    latest_entries = {}\n",
        "    for entry in extracted_data_sorted:\n",
        "        key = (entry['model_name'], entry['task'])  # Group by model_name and task\n",
        "        # Replace existing entry if current entry is newer\n",
        "        if key not in latest_entries or entry['datetime'] > latest_entries[key]['datetime']:\n",
        "            latest_entries[key] = entry\n",
        "    latest_entries_result = []\n",
        "    for key, entry in latest_entries.items():\n",
        "        entry['acc_value'] = \"%.2f\" % round(entry['acc_value']*100, 2)\n",
        "        latest_entries_result.append(entry)\n",
        "\n",
        "\n",
        "    df = pd.DataFrame(latest_entries_result)\n",
        "    pivot_table = df.pivot(index=\"model_name\", columns=\"task\", values=\"acc_value\")\n",
        "    pivot_table = pivot_table[natsorted(pivot_table.columns)]\n",
        "    # Display the latest entries\n",
        "    for key, entry in latest_entries.items():\n",
        "        print(f\"Model Name: {entry['model_name']}, Task: {entry['task']}, Date: {entry['datetime']}, Acc Value: {entry['acc_value']}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2DMzhWCUR4h",
        "outputId": "2cbfec61-4612-4d81-f53e-49bf51847dbe"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: mmlu_all, Date: 2024-12-05T10-07-32.229420, Acc Value: 34.13\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_1, Date: 2024-12-04T09-46-13.895610, Acc Value: 58.60\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_10, Date: 2024-12-04T10-27-24.033942, Acc Value: 78.10\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_11, Date: 2024-12-04T10-30-42.581549, Acc Value: 31.56\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_12, Date: 2024-12-04T10-34-08.691174, Acc Value: 84.12\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_13, Date: 2024-12-04T10-36-54.246366, Acc Value: 48.40\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_14, Date: 2024-12-04T10-37-56.290613, Acc Value: 33.62\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_2, Date: 2024-12-04T09-50-36.416792, Acc Value: 51.20\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_3, Date: 2024-12-04T09-55-52.575686, Acc Value: 57.42\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_4, Date: 2024-12-04T10-08-54.537419, Acc Value: 24.70\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_5, Date: 2024-12-04T10-10-41.137500, Acc Value: 49.90\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_6, Date: 2024-12-04T10-12-31.411026, Acc Value: 50.05\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_7, Date: 2024-12-04T10-15-47.808656, Acc Value: 51.98\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_8, Date: 2024-12-04T10-19-48.272257, Acc Value: 55.31\n",
            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_9, Date: 2024-12-04T10-23-50.268393, Acc Value: 52.20\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: mmlu_all, Date: 2024-12-05T10-28-27.223863, Acc Value: 53.23\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_1, Date: 2024-12-04T11-03-01.529235, Acc Value: 81.04\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_10, Date: 2024-12-04T12-31-04.414803, Acc Value: 75.81\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_11, Date: 2024-12-04T12-37-54.346037, Acc Value: 33.67\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_12, Date: 2024-12-04T12-44-45.900074, Acc Value: 53.30\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_13, Date: 2024-12-04T12-50-17.737269, Acc Value: 70.00\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_14, Date: 2024-12-04T12-52-04.030761, Acc Value: 59.83\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_2, Date: 2024-12-04T11-11-51.908847, Acc Value: 75.30\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_3, Date: 2024-12-04T11-22-49.948783, Acc Value: 77.69\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_4, Date: 2024-12-04T11-52-43.868109, Acc Value: 53.65\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_5, Date: 2024-12-04T11-56-03.314161, Acc Value: 69.05\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_6, Date: 2024-12-04T11-59-30.170935, Acc Value: 63.55\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_7, Date: 2024-12-04T12-06-20.816022, Acc Value: 78.64\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_8, Date: 2024-12-04T12-15-00.410767, Acc Value: 92.54\n",
            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_9, Date: 2024-12-04T12-23-42.320725, Acc Value: 59.94\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: mmlu_all, Date: 2024-12-05T11-08-04.943164, Acc Value: 57.54\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_1, Date: 2024-12-04T13-39-12.867724, Acc Value: 79.84\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_10, Date: 2024-12-04T16-30-25.978146, Acc Value: 78.00\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_11, Date: 2024-12-04T16-43-34.533561, Acc Value: 41.67\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_12, Date: 2024-12-04T16-56-04.873961, Acc Value: 73.39\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_13, Date: 2024-12-04T17-05-59.454197, Acc Value: 77.50\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_14, Date: 2024-12-04T17-08-59.676663, Acc Value: 62.88\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_2, Date: 2024-12-04T13-56-19.017336, Acc Value: 79.61\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_3, Date: 2024-12-04T14-17-13.903279, Acc Value: 76.90\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_4, Date: 2024-12-04T15-17-10.760422, Acc Value: 50.00\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_5, Date: 2024-12-04T15-23-12.253601, Acc Value: 69.75\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_6, Date: 2024-12-04T15-29-24.543280, Acc Value: 52.45\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_7, Date: 2024-12-04T15-42-32.945279, Acc Value: 82.43\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_8, Date: 2024-12-04T15-59-19.291670, Acc Value: 95.14\n",
            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_9, Date: 2024-12-04T16-16-07.277328, Acc Value: 67.68\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: mmlu_all, Date: 2024-11-24T15-20-49.697273, Acc Value: 34.47\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_1, Date: 2024-11-25T03-10-25.443130, Acc Value: 86.92\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_10, Date: 2024-11-25T03-21-54.085274, Acc Value: 78.10\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_11, Date: 2024-11-25T03-22-57.001476, Acc Value: 38.33\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_12, Date: 2024-11-25T03-24-06.031245, Acc Value: 83.69\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_13, Date: 2024-11-25T03-24-54.408538, Acc Value: 46.90\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_14, Date: 2024-11-25T03-25-37.703112, Acc Value: 37.99\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_2, Date: 2024-11-25T03-13-13.183813, Acc Value: 55.91\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_3, Date: 2024-11-25T03-14-39.515145, Acc Value: 60.97\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_4, Date: 2024-11-25T03-15-48.048668, Acc Value: 28.50\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_5, Date: 2024-11-25T03-16-49.842533, Acc Value: 50.15\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_6, Date: 2024-11-25T03-17-51.307160, Acc Value: 50.10\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_7, Date: 2024-11-25T03-18-50.302136, Acc Value: 54.63\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_8, Date: 2024-11-25T03-19-48.639957, Acc Value: 53.16\n",
            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_9, Date: 2024-11-25T03-20-46.585027, Acc Value: 54.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivot_table\n",
        "#pivot_table.to_html()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "j9t2Oj7-Fgb3",
        "outputId": "44fea3e2-6a6a-4ad8-c2c4-4cb3f8aafe7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "task                                 mmlu_all pub_task_1 pub_task_2  \\\n",
              "model_name                                                            \n",
              "meta-llama/Llama-3.2-1B-Instruct        34.13      58.60      51.20   \n",
              "meta-llama/Llama-3.2-3B-Instruct        53.23      81.04      75.30   \n",
              "meta-llama/Meta-Llama-3-8B              57.54      79.84      79.61   \n",
              "mistralai/Mixtral-8x7B-Instruct-v0.1    34.47      86.92      55.91   \n",
              "\n",
              "task                                 pub_task_3 pub_task_4 pub_task_5  \\\n",
              "model_name                                                              \n",
              "meta-llama/Llama-3.2-1B-Instruct          57.42      24.70      49.90   \n",
              "meta-llama/Llama-3.2-3B-Instruct          77.69      53.65      69.05   \n",
              "meta-llama/Meta-Llama-3-8B                76.90      50.00      69.75   \n",
              "mistralai/Mixtral-8x7B-Instruct-v0.1      60.97      28.50      50.15   \n",
              "\n",
              "task                                 pub_task_6 pub_task_7 pub_task_8  \\\n",
              "model_name                                                              \n",
              "meta-llama/Llama-3.2-1B-Instruct          50.05      51.98      55.31   \n",
              "meta-llama/Llama-3.2-3B-Instruct          63.55      78.64      92.54   \n",
              "meta-llama/Meta-Llama-3-8B                52.45      82.43      95.14   \n",
              "mistralai/Mixtral-8x7B-Instruct-v0.1      50.10      54.63      53.16   \n",
              "\n",
              "task                                 pub_task_9 pub_task_10 pub_task_11  \\\n",
              "model_name                                                                \n",
              "meta-llama/Llama-3.2-1B-Instruct          52.20       78.10       31.56   \n",
              "meta-llama/Llama-3.2-3B-Instruct          59.94       75.81       33.67   \n",
              "meta-llama/Meta-Llama-3-8B                67.68       78.00       41.67   \n",
              "mistralai/Mixtral-8x7B-Instruct-v0.1      54.75       78.10       38.33   \n",
              "\n",
              "task                                 pub_task_12 pub_task_13 pub_task_14  \n",
              "model_name                                                                \n",
              "meta-llama/Llama-3.2-1B-Instruct           84.12       48.40       33.62  \n",
              "meta-llama/Llama-3.2-3B-Instruct           53.30       70.00       59.83  \n",
              "meta-llama/Meta-Llama-3-8B                 73.39       77.50       62.88  \n",
              "mistralai/Mixtral-8x7B-Instruct-v0.1       83.69       46.90       37.99  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7434b277-cc7d-4a83-b15f-d9b7400c29fd\" 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>task</th>\n",
              "      <th>mmlu_all</th>\n",
              "      <th>pub_task_1</th>\n",
              "      <th>pub_task_2</th>\n",
              "      <th>pub_task_3</th>\n",
              "      <th>pub_task_4</th>\n",
              "      <th>pub_task_5</th>\n",
              "      <th>pub_task_6</th>\n",
              "      <th>pub_task_7</th>\n",
              "      <th>pub_task_8</th>\n",
              "      <th>pub_task_9</th>\n",
              "      <th>pub_task_10</th>\n",
              "      <th>pub_task_11</th>\n",
              "      <th>pub_task_12</th>\n",
              "      <th>pub_task_13</th>\n",
              "      <th>pub_task_14</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>meta-llama/Llama-3.2-1B-Instruct</th>\n",
              "      <td>34.13</td>\n",
              "      <td>58.60</td>\n",
              "      <td>51.20</td>\n",
              "      <td>57.42</td>\n",
              "      <td>24.70</td>\n",
              "      <td>49.90</td>\n",
              "      <td>50.05</td>\n",
              "      <td>51.98</td>\n",
              "      <td>55.31</td>\n",
              "      <td>52.20</td>\n",
              "      <td>78.10</td>\n",
              "      <td>31.56</td>\n",
              "      <td>84.12</td>\n",
              "      <td>48.40</td>\n",
              "      <td>33.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meta-llama/Llama-3.2-3B-Instruct</th>\n",
              "      <td>53.23</td>\n",
              "      <td>81.04</td>\n",
              "      <td>75.30</td>\n",
              "      <td>77.69</td>\n",
              "      <td>53.65</td>\n",
              "      <td>69.05</td>\n",
              "      <td>63.55</td>\n",
              "      <td>78.64</td>\n",
              "      <td>92.54</td>\n",
              "      <td>59.94</td>\n",
              "      <td>75.81</td>\n",
              "      <td>33.67</td>\n",
              "      <td>53.30</td>\n",
              "      <td>70.00</td>\n",
              "      <td>59.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meta-llama/Meta-Llama-3-8B</th>\n",
              "      <td>57.54</td>\n",
              "      <td>79.84</td>\n",
              "      <td>79.61</td>\n",
              "      <td>76.90</td>\n",
              "      <td>50.00</td>\n",
              "      <td>69.75</td>\n",
              "      <td>52.45</td>\n",
              "      <td>82.43</td>\n",
              "      <td>95.14</td>\n",
              "      <td>67.68</td>\n",
              "      <td>78.00</td>\n",
              "      <td>41.67</td>\n",
              "      <td>73.39</td>\n",
              "      <td>77.50</td>\n",
              "      <td>62.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mistralai/Mixtral-8x7B-Instruct-v0.1</th>\n",
              "      <td>34.47</td>\n",
              "      <td>86.92</td>\n",
              "      <td>55.91</td>\n",
              "      <td>60.97</td>\n",
              "      <td>28.50</td>\n",
              "      <td>50.15</td>\n",
              "      <td>50.10</td>\n",
              "      <td>54.63</td>\n",
              "      <td>53.16</td>\n",
              "      <td>54.75</td>\n",
              "      <td>78.10</td>\n",
              "      <td>38.33</td>\n",
              "      <td>83.69</td>\n",
              "      <td>46.90</td>\n",
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