diff --git "a/competition/00a_r2_InternLM2.5_Llama3_GLM4_Results.ipynb" "b/competition/00a_r2_InternLM2.5_Llama3_GLM4_Results.ipynb"
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+++ "b/competition/00a_r2_InternLM2.5_Llama3_GLM4_Results.ipynb"
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+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n"," from google.colab import drive\n"," drive.mount('/content/drive')\n"," workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","except ModuleNotFoundError:\n"," workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/projects/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["P1 = \"\"\"你是一个逻辑游戏的主持人。游戏规则如下:\n","\n","1. 参与者会得到一个谜题。\n","2. 参与者可以通过提问来获取线索,尝试解开谜题。\n","3. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。\n","4. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n","5. 参与者需要根据回答来推理,并最终找出谜题的正确答案。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","谜题: {}\n","\n","实际情况: {}\n","\n","参与者提出的问题: {}\n","\"\"\""]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["P2 = \"\"\"你是一个情景猜谜游戏的主持人。游戏规则如下:\n","\n","1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底,谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n"," - 若谜面和谜底能找到问题的答案,回答:是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n"," - 若参与者提问基本还原了谜底真相,回答:回答正确\n","5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","**谜面:** {}\n","\n","**谜底:** {}\n","\n","**参与者提出的问题:** {}\n","\"\"\""]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.759667 | \n"," 0.741854 | \n"," 0.781014 | \n"," 0.758887 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-44 | \n"," 0.761667 | \n"," 0.810873 | \n"," 0.761667 | \n"," 0.780018 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-88 | \n"," 0.741333 | \n"," 0.816182 | \n"," 0.741333 | \n"," 0.769524 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-132 | \n"," 0.755000 | \n"," 0.809829 | \n"," 0.755000 | \n"," 0.775657 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-176 | \n"," 0.719000 | \n"," 0.803307 | \n"," 0.719000 | \n"," 0.750319 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 internlm/internlm2_5-7b-chat-1m 0.759667 0.741854 \n","1 1 internlm/internlm2_5-7b-chat-1m_checkpoint-44 0.761667 0.810873 \n","2 2 internlm/internlm2_5-7b-chat-1m_checkpoint-88 0.741333 0.816182 \n","3 3 internlm/internlm2_5-7b-chat-1m_checkpoint-132 0.755000 0.809829 \n","4 4 internlm/internlm2_5-7b-chat-1m_checkpoint-176 0.719000 0.803307 \n","\n"," recall f1 \n","0 0.781014 0.758887 \n","1 0.761667 0.780018 \n","2 0.741333 0.769524 \n","3 0.755000 0.775657 \n","4 0.719000 0.750319 "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df_p1 = pd.read_csv(\"results/mgtv-results_p1_full_metrics.csv\")\n","df_p1"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.766000 | \n"," 0.747969 | \n"," 0.787526 | \n"," 0.764922 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-175 | \n"," 0.812000 | \n"," 0.812286 | \n"," 0.812000 | \n"," 0.810234 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-350 | \n"," 0.765333 | \n"," 0.806889 | \n"," 0.765333 | \n"," 0.779998 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-525 | \n"," 0.747667 | \n"," 0.812033 | \n"," 0.747667 | \n"," 0.773122 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-700 | \n"," 0.717000 | \n"," 0.804642 | \n"," 0.717000 | \n"," 0.751034 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 internlm/internlm2_5-7b-chat-1m 0.766000 0.747969 \n","1 1 internlm/internlm2_5-7b-chat-1m_checkpoint-175 0.812000 0.812286 \n","2 2 internlm/internlm2_5-7b-chat-1m_checkpoint-350 0.765333 0.806889 \n","3 3 internlm/internlm2_5-7b-chat-1m_checkpoint-525 0.747667 0.812033 \n","4 4 internlm/internlm2_5-7b-chat-1m_checkpoint-700 0.717000 0.804642 \n","\n"," recall f1 \n","0 0.787526 0.764922 \n","1 0.812000 0.810234 \n","2 0.765333 0.779998 \n","3 0.747667 0.773122 \n","4 0.717000 0.751034 "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = pd.read_csv(\"results/mgtv-results_p2_r2_full_metrics.csv\")\n","df_p2"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["df_p2 = df_p2[: len(df_p1)]"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_torch.bflo... | \n"," 0.783667 | \n"," 0.766712 | \n"," 0.792917 | \n"," 0.767940 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.770667 | \n"," 0.807275 | \n"," 0.770667 | \n"," 0.783572 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.724000 | \n"," 0.811805 | \n"," 0.724000 | \n"," 0.756227 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.675667 | \n"," 0.781176 | \n"," 0.675667 | \n"," 0.710846 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.649667 | \n"," 0.779897 | \n"," 0.649667 | \n"," 0.693184 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy \\\n","0 0 shenzhi-wang/Llama3-8B-Chinese-Chat_torch.bflo... 0.783667 \n","1 1 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.770667 \n","2 2 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.724000 \n","3 3 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.675667 \n","4 4 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.649667 \n","\n"," precision recall f1 \n","0 0.766712 0.792917 0.767940 \n","1 0.807275 0.770667 0.783572 \n","2 0.811805 0.724000 0.756227 \n","3 0.781176 0.675667 0.710846 \n","4 0.779897 0.649667 0.693184 "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_p1_llama3 = pd.read_csv(\"results/mgtv-llama3_p1_r2_full_metrics.csv\")\n","df_p1_llama3"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_torch.bflo... | \n"," 0.730000 | \n"," 0.770974 | \n"," 0.730000 | \n"," 0.746291 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.718000 | \n"," 0.811309 | \n"," 0.718000 | \n"," 0.750106 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.727333 | \n"," 0.802512 | \n"," 0.727333 | \n"," 0.754982 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.688333 | \n"," 0.781617 | \n"," 0.688333 | \n"," 0.716763 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... | \n"," 0.640667 | \n"," 0.763630 | \n"," 0.640667 | \n"," 0.680793 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy \\\n","0 0 shenzhi-wang/Llama3-8B-Chinese-Chat_torch.bflo... 0.730000 \n","1 1 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.718000 \n","2 2 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.727333 \n","3 3 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.688333 \n","4 4 shenzhi-wang/Llama3-8B-Chinese-Chat/checkpoint... 0.640667 \n","\n"," precision recall f1 \n","0 0.770974 0.730000 0.746291 \n","1 0.811309 0.718000 0.750106 \n","2 0.802512 0.727333 0.754982 \n","3 0.781617 0.688333 0.716763 \n","4 0.763630 0.640667 0.680793 "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_llama3 = pd.read_csv(\"results/mgtv-llama3_p2_r2_full_metrics.csv\")\n","df_p2_llama3"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," THUDM/glm-4-9b-chat-1m | \n"," 0.581000 | \n"," 0.703006 | \n"," 0.581000 | \n"," 0.616915 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-175 | \n"," 0.465000 | \n"," 0.462698 | \n"," 0.478067 | \n"," 0.452823 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-350 | \n"," 0.579000 | \n"," 0.677205 | \n"," 0.579000 | \n"," 0.607769 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-525 | \n"," 0.605333 | \n"," 0.722023 | \n"," 0.605333 | \n"," 0.637907 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-700 | \n"," 0.593000 | \n"," 0.720287 | \n"," 0.593000 | \n"," 0.631179 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 THUDM/glm-4-9b-chat-1m 0.581000 0.703006 \n","1 1 THUDM/glm-4-9b-chat-1m_checkpoint-175 0.465000 0.462698 \n","2 2 THUDM/glm-4-9b-chat-1m_checkpoint-350 0.579000 0.677205 \n","3 3 THUDM/glm-4-9b-chat-1m_checkpoint-525 0.605333 0.722023 \n","4 4 THUDM/glm-4-9b-chat-1m_checkpoint-700 0.593000 0.720287 \n","\n"," recall f1 \n","0 0.581000 0.616915 \n","1 0.478067 0.452823 \n","2 0.579000 0.607769 \n","3 0.605333 0.637907 \n","4 0.593000 0.631179 "]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df_p1_glm_4 = pd.read_csv(\"results/mgtv-glm-4-9b_p1_full_metrics.csv\")\n","df_p1_glm_4"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," THUDM/glm-4-9b-chat-1m | \n"," 0.395000 | \n"," 0.667648 | \n"," 0.395000 | \n"," 0.458390 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-175 | \n"," 0.594667 | \n"," 0.705625 | \n"," 0.594667 | \n"," 0.631524 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-350 | \n"," 0.549000 | \n"," 0.700654 | \n"," 0.549000 | \n"," 0.595640 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-525 | \n"," 0.598667 | \n"," 0.715051 | \n"," 0.598667 | \n"," 0.625357 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-700 | \n"," 0.584333 | \n"," 0.730090 | \n"," 0.584333 | \n"," 0.619578 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 THUDM/glm-4-9b-chat-1m 0.395000 0.667648 \n","1 1 THUDM/glm-4-9b-chat-1m_checkpoint-175 0.594667 0.705625 \n","2 2 THUDM/glm-4-9b-chat-1m_checkpoint-350 0.549000 0.700654 \n","3 3 THUDM/glm-4-9b-chat-1m_checkpoint-525 0.598667 0.715051 \n","4 4 THUDM/glm-4-9b-chat-1m_checkpoint-700 0.584333 0.730090 \n","\n"," recall f1 \n","0 0.395000 0.458390 \n","1 0.594667 0.631524 \n","2 0.549000 0.595640 \n","3 0.598667 0.625357 \n","4 0.584333 0.619578 "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_glm_4 = pd.read_csv(\"results/mgtv-glm-4-9b_p2_full_metrics.csv\")\n","df_p2_glm_4"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[],"source":["def plot_results(df_p1, df_p2, best_p1, best_p2, color_p1=\"red\", color_p2=\"blue\", model_name=\"InternLM2.5_7b\"):\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"accuracy\",\n"," data=df_p1,\n"," ax=ax[0],\n"," color=color_p1,\n"," label=f\"{model_name}: P1\",\n"," )\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"accuracy\",\n"," data=df_p2,\n"," ax=ax[0],\n"," color=color_p2,\n"," label=f\"{model_name}: P2\",\n"," )\n"," sns.scatterplot(\n"," x=\"epoch\", y=\"accuracy\", data=best_p1, ax=ax[0], color=color_p1, s=50\n"," )\n"," sns.scatterplot(\n"," x=\"epoch\", y=\"accuracy\", data=best_p2, ax=ax[0], color=color_p2, s=50\n"," )\n","\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"f1\",\n"," data=df_p1,\n"," ax=ax[1],\n"," color=color_p1,\n"," label=f\"{model_name}: P1\",\n"," )\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"f1\",\n"," data=df_p2,\n"," ax=ax[1],\n"," color=color_p2,\n"," label=f\"{model_name}: P2\",\n"," )\n"," sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p1, ax=ax[1], color=color_p1, s=50)\n"," sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p2, ax=ax[1], color=color_p2, s=50)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","def plot_model_results(model_name, df_p1, df_p2, ax):\n"," print(f\"Model: {model_name}\")\n"," sns.set_theme(style=\"whitegrid\")\n","\n"," # print the best results\n"," best_p1 = df_p1[df_p1[\"accuracy\"] == df_p1[\"accuracy\"].max()]\n"," best_p2 = df_p2[df_p2[\"accuracy\"] == df_p2[\"accuracy\"].max()]\n","\n"," print(\"Best P1 accuracy:\")\n"," print(best_p1[\"accuracy\"].values[0])\n"," print(\"Best P2 accuracy:\")\n"," print(best_p2[\"accuracy\"].values[0])\n","\n"," plot_results(df_p1, df_p2, best_p1, best_p2, model_name=model_name)\n","\n"," for a in ax:\n"," for line_index, line in enumerate(a.lines):\n"," # Get the data\n"," line_color = line.get_color()\n"," xdata, ydata = line.get_data()\n"," for index in range(xdata.size):\n"," a.annotate( # Use 'a' instead of 'ax' to refer to the current subplot\n"," f\"{ydata[index]:.3f}\",\n"," xy=(xdata[index], ydata[index]),\n"," textcoords=\"offset points\",\n"," xytext=(\n"," 0,\n"," 1,\n"," # -10 if line_index % 2 == 0 else 10,\n"," ), # Adjusted for better visibility\n"," ha=\"center\",\n"," color=line_color,\n"," )\n","\n"," ax[0].set_title(\"Accuracy\")\n"," ax[1].set_title(\"F1\")\n","\n"," # After plotting your data and before plt.show(), add these lines\n"," ax[0].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n"," ax[1].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","\n"," plt.show()"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: InternLM_2_5-7b\n","Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.812\n"]},{"data":{"image/png":"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","text/plain":["