diff --git "a/competition/00a_InternLM_2.5_Results.ipynb" "b/competition/00a_InternLM_2.5_Results.ipynb"
--- "a/competition/00a_InternLM_2.5_Results.ipynb"
+++ "b/competition/00a_InternLM_2.5_Results.ipynb"
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":2,"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":3,"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":4,"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":5,"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.example\n"]},{"data":{"text/plain":["True"]},"execution_count":5,"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":6,"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":7,"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":9,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.759667</td>\n","      <td>0.741854</td>\n","      <td>0.781014</td>\n","      <td>0.758887</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-44</td>\n","      <td>0.761667</td>\n","      <td>0.810873</td>\n","      <td>0.761667</td>\n","      <td>0.780018</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.741333</td>\n","      <td>0.816182</td>\n","      <td>0.741333</td>\n","      <td>0.769524</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-132</td>\n","      <td>0.755000</td>\n","      <td>0.809829</td>\n","      <td>0.755000</td>\n","      <td>0.775657</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.719000</td>\n","      <td>0.803307</td>\n","      <td>0.719000</td>\n","      <td>0.750319</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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":9,"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":10,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.766000</td>\n","      <td>0.747969</td>\n","      <td>0.787526</td>\n","      <td>0.764922</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.796333</td>\n","      <td>0.808232</td>\n","      <td>0.796333</td>\n","      <td>0.798160</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.781333</td>\n","      <td>0.804716</td>\n","      <td>0.781333</td>\n","      <td>0.788581</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-264</td>\n","      <td>0.759000</td>\n","      <td>0.805502</td>\n","      <td>0.759000</td>\n","      <td>0.777237</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-352</td>\n","      <td>0.730333</td>\n","      <td>0.790676</td>\n","      <td>0.730333</td>\n","      <td>0.753716</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-440</td>\n","      <td>0.730333</td>\n","      <td>0.790420</td>\n","      <td>0.730333</td>\n","      <td>0.753750</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-528</td>\n","      <td>0.716000</td>\n","      <td>0.789892</td>\n","      <td>0.716000</td>\n","      <td>0.744833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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-88  0.796333   0.808232   \n","2      2  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.781333   0.804716   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-264  0.759000   0.805502   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-352  0.730333   0.790676   \n","5      5  internlm/internlm2_5-7b-chat-1m_checkpoint-440  0.730333   0.790420   \n","6      6  internlm/internlm2_5-7b-chat-1m_checkpoint-528  0.716000   0.789892   \n","\n","     recall        f1  \n","0  0.787526  0.764922  \n","1  0.796333  0.798160  \n","2  0.781333  0.788581  \n","3  0.759000  0.777237  \n","4  0.730333  0.753716  \n","5  0.730333  0.753750  \n","6  0.716000  0.744833  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = pd.read_csv(\"results/mgtv-results_p2_full_metrics.csv\")\n","df_p2"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.766000</td>\n","      <td>0.747969</td>\n","      <td>0.787526</td>\n","      <td>0.764922</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.796333</td>\n","      <td>0.808232</td>\n","      <td>0.796333</td>\n","      <td>0.798160</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.781333</td>\n","      <td>0.804716</td>\n","      <td>0.781333</td>\n","      <td>0.788581</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-264</td>\n","      <td>0.759000</td>\n","      <td>0.805502</td>\n","      <td>0.759000</td>\n","      <td>0.777237</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-352</td>\n","      <td>0.730333</td>\n","      <td>0.790676</td>\n","      <td>0.730333</td>\n","      <td>0.753716</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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-88  0.796333   0.808232   \n","2      2  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.781333   0.804716   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-264  0.759000   0.805502   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-352  0.730333   0.790676   \n","\n","     recall        f1  \n","0  0.787526  0.764922  \n","1  0.796333  0.798160  \n","2  0.781333  0.788581  \n","3  0.759000  0.777237  \n","4  0.730333  0.753716  "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = df_p2[:len(df_p1)]\n","df_p2"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\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 the best results\n","fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n","\n","sns.lineplot(x=\"epoch\", y=\"accuracy\", data=df_p1, ax=ax[0], color=\"red\", label=\"P1\")\n","sns.lineplot(x=\"epoch\", y=\"accuracy\", data=df_p2, ax=ax[0], color=\"blue\", label=\"P2\")\n","sns.scatterplot(x=\"epoch\", y=\"accuracy\", data=best_p1, ax=ax[0], color=\"red\", s=50)\n","sns.scatterplot(x=\"epoch\", y=\"accuracy\", data=best_p2, ax=ax[0], color=\"blue\", s=50)\n","\n","sns.lineplot(x=\"epoch\", y=\"f1\", data=df_p1, ax=ax[1], color=\"red\", label=\"P1\")\n","sns.lineplot(x=\"epoch\", y=\"f1\", data=df_p2, ax=ax[1], color=\"blue\", label=\"P2\")\n","sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p1, ax=ax[1], color=\"red\", s=50)\n","sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p2, ax=ax[1], color=\"blue\", s=50)\n","\n","# plot values on the best results\n","ax[0].text(best_p1[\"epoch\"].values[0], best_p1[\"accuracy\"].values[0], round(best_p1[\"accuracy\"].values[0], 3), ha='left')\n","ax[0].text(best_p2[\"epoch\"].values[0], best_p2[\"accuracy\"].values[0], round(best_p2[\"accuracy\"].values[0], 3), ha='left')\n","ax[1].text(best_p1[\"epoch\"].values[0], best_p1[\"f1\"].values[0], round(best_p1[\"f1\"].values[0], 3), ha='left')\n","ax[1].text(best_p2[\"epoch\"].values[0], best_p2[\"f1\"].values[0], round(best_p2[\"f1\"].values[0], 3), ha='left')\n","\n","# plot values on epoch 0\n","ax[0].text(0, df_p1[\"accuracy\"].values[0], round(df_p1[\"accuracy\"].values[0], 3), ha='left')\n","ax[0].text(0, df_p2[\"accuracy\"].values[0], round(df_p2[\"accuracy\"].values[0], 3), ha='left')\n","ax[1].text(0, df_p1[\"f1\"].values[0], round(df_p1[\"f1\"].values[0], 3), ha='left')\n","ax[1].text(0, df_p2[\"f1\"].values[0], round(df_p2[\"f1\"].values[0], 3), ha='left')\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()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}
+{"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.example\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":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.759667</td>\n","      <td>0.741854</td>\n","      <td>0.781014</td>\n","      <td>0.758887</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-44</td>\n","      <td>0.761667</td>\n","      <td>0.810873</td>\n","      <td>0.761667</td>\n","      <td>0.780018</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.741333</td>\n","      <td>0.816182</td>\n","      <td>0.741333</td>\n","      <td>0.769524</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-132</td>\n","      <td>0.755000</td>\n","      <td>0.809829</td>\n","      <td>0.755000</td>\n","      <td>0.775657</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.719000</td>\n","      <td>0.803307</td>\n","      <td>0.719000</td>\n","      <td>0.750319</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.766000</td>\n","      <td>0.747969</td>\n","      <td>0.787526</td>\n","      <td>0.764922</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-88</td>\n","      <td>0.796333</td>\n","      <td>0.808232</td>\n","      <td>0.796333</td>\n","      <td>0.798160</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-176</td>\n","      <td>0.781333</td>\n","      <td>0.804716</td>\n","      <td>0.781333</td>\n","      <td>0.788581</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-264</td>\n","      <td>0.759000</td>\n","      <td>0.805502</td>\n","      <td>0.759000</td>\n","      <td>0.777237</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-352</td>\n","      <td>0.730333</td>\n","      <td>0.790676</td>\n","      <td>0.730333</td>\n","      <td>0.753716</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-440</td>\n","      <td>0.730333</td>\n","      <td>0.790420</td>\n","      <td>0.730333</td>\n","      <td>0.753750</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-528</td>\n","      <td>0.716000</td>\n","      <td>0.789892</td>\n","      <td>0.716000</td>\n","      <td>0.744833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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-88  0.796333   0.808232   \n","2      2  internlm/internlm2_5-7b-chat-1m_checkpoint-176  0.781333   0.804716   \n","3      3  internlm/internlm2_5-7b-chat-1m_checkpoint-264  0.759000   0.805502   \n","4      4  internlm/internlm2_5-7b-chat-1m_checkpoint-352  0.730333   0.790676   \n","5      5  internlm/internlm2_5-7b-chat-1m_checkpoint-440  0.730333   0.790420   \n","6      6  internlm/internlm2_5-7b-chat-1m_checkpoint-528  0.716000   0.789892   \n","\n","     recall        f1  \n","0  0.787526  0.764922  \n","1  0.796333  0.798160  \n","2  0.781333  0.788581  \n","3  0.759000  0.777237  \n","4  0.730333  0.753716  \n","5  0.730333  0.753750  \n","6  0.716000  0.744833  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = pd.read_csv(\"results/mgtv-results_p2_full_metrics.csv\")\n","df_p2"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat</td>\n","      <td>0.783667</td>\n","      <td>0.766712</td>\n","      <td>0.792917</td>\n","      <td>0.767940</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.568667</td>\n","      <td>0.807123</td>\n","      <td>0.568667</td>\n","      <td>0.625399</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.704333</td>\n","      <td>0.810817</td>\n","      <td>0.704333</td>\n","      <td>0.742186</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.783667   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.568667   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.704333   \n","\n","   precision    recall        f1  \n","0   0.766712  0.792917  0.767940  \n","1   0.807123  0.568667  0.625399  \n","2   0.810817  0.704333  0.742186  "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df_p1_llama3 = pd.read_csv(\"results/mgtv-llama3_p1_full_metrics.csv\")\n","df_p1_llama3"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat</td>\n","      <td>0.730000</td>\n","      <td>0.770974</td>\n","      <td>0.730000</td>\n","      <td>0.746291</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.773000</td>\n","      <td>0.773916</td>\n","      <td>0.773000</td>\n","      <td>0.764280</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.704667</td>\n","      <td>0.814516</td>\n","      <td>0.704667</td>\n","      <td>0.745365</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.679333</td>\n","      <td>0.803070</td>\n","      <td>0.679333</td>\n","      <td>0.724637</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.730000   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.773000   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.704667   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.679333   \n","\n","   precision    recall        f1  \n","0   0.770974  0.730000  0.746291  \n","1   0.773916  0.773000  0.764280  \n","2   0.814516  0.704667  0.745365  \n","3   0.803070  0.679333  0.724637  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_llama3 = pd.read_csv(\"results/mgtv-llama3_p2_full_metrics.csv\")\n","df_p2_llama3"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/plain":["3"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["new_len = min(len(df_p1), len(df_p2), len(df_p1_llama3), len(df_p2_llama3))\n","new_len"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["# df_p1 = df_p1[:new_len]\n","# df_p2 = df_p2[:new_len]\n","# df_p1_llama3 = df_p1_llama3[:new_len]\n","# df_p2_llama3 = df_p2_llama3[:new_len]"]},{"cell_type":"code","execution_count":25,"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":26,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n","Best P1 accuracy llama3:\n","0.7836666666666666\n","Best P2 accuracy llama3:\n","0.773\n"]},{"data":{"image/png":"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size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\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","best_llama3_p1 = df_p1_llama3[df_p1_llama3[\"accuracy\"] == df_p1_llama3[\"accuracy\"].max()]\n","best_llama3_p2 = df_p2_llama3[df_p2_llama3[\"accuracy\"] == df_p2_llama3[\"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","print(\"Best P1 accuracy llama3:\")\n","print(best_llama3_p1[\"accuracy\"].values[0])\n","print(\"Best P2 accuracy llama3:\")\n","print(best_llama3_p2[\"accuracy\"].values[0])\n","\n","# plot the best results\n","fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n","\n","plot_results(df_p1, df_p2, best_p1, best_p2)\n","plot_results(df_p1_llama3, df_p2_llama3, best_llama3_p1, best_llama3_p2, color_p1=\"green\", color_p2=\"purple\", model_name=\"Llama3-8b\")\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()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}