{ "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, "referenced_widgets": [ "9cc2bac65bce4700a5ef55d371122860", "c22bc73b6dd34ad5a0d271043f94da5e", "f3a6549e81914af58e3e2f9ab78a44b2", "8fbc37aec6dc48599759211538b229bb", "9094111dac3e493abd69f64e43b91263", "204393cae74d4d4d920cdfa9299869c7", "0ff36c27bdca43e28e24eaaca6f8b9e2", "b794dbdcaa2c40dd94e9b65f179f3f24", "549918f3f42d4e2baa279d3da03fe5af", "bb30c9141eb64d8fa3919f1547ad9c3a", 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"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", " <td>37.99</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>\n", " <div class=\"colab-df-buttons\">\n", "\n", " <div class=\"colab-df-container\">\n", " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7434b277-cc7d-4a83-b15f-d9b7400c29fd')\"\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 -960 960 960\">\n", " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", " </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", 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