diff --git "a/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb" "b/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb"
--- "a/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb"
+++ "b/competition/00a_InternLM_2.5_and_Llama_3_Results.ipynb"
@@ -1 +1 @@
-{"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":["<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>0.292000</td>\n","      <td>0.724450</td>\n","      <td>0.292000</td>\n","      <td>0.396036</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>0.314000</td>\n","      <td>0.823098</td>\n","      <td>0.314000</td>\n","      <td>0.444607</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.550667</td>\n","      <td>0.720997</td>\n","      <td>0.550667</td>\n","      <td>0.601762</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...  0.292000   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.314000   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","4      4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.000000   \n","5      5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6      6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.550667   \n","\n","   precision    recall        f1  \n","0   0.766712  0.792917  0.767940  \n","1   0.724450  0.292000  0.396036  \n","2   0.823098  0.314000  0.444607  \n","3        NaN       NaN       NaN  \n","4   0.000000  0.000000  0.000000  \n","5        NaN       NaN       NaN  \n","6   0.720997  0.550667  0.601762  "]},"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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.665667</td>\n","      <td>0.792412</td>\n","      <td>0.665667</td>\n","      <td>0.712431</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...       NaN   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","4      4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","5      5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6      6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.665667   \n","\n","   precision    recall        f1  \n","0   0.770974  0.730000  0.746291  \n","1        NaN       NaN       NaN  \n","2        NaN       NaN       NaN  \n","3        NaN       NaN       NaN  \n","4        NaN       NaN       NaN  \n","5        NaN       NaN       NaN  \n","6   0.792412  0.665667  0.712431  "]},"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":["5"]},"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":13,"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":14,"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","0.73\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","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}
+{"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":["<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>0.292000</td>\n","      <td>0.724450</td>\n","      <td>0.292000</td>\n","      <td>0.396036</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>0.314000</td>\n","      <td>0.823098</td>\n","      <td>0.314000</td>\n","      <td>0.444607</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.550667</td>\n","      <td>0.720997</td>\n","      <td>0.550667</td>\n","      <td>0.601762</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.290667</td>\n","      <td>0.696844</td>\n","      <td>0.290667</td>\n","      <td>0.396871</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.574667</td>\n","      <td>0.749095</td>\n","      <td>0.574667</td>\n","      <td>0.626323</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>9</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>10</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</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...  0.292000   \n","2       2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.314000   \n","3       3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","4       4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.000000   \n","5       5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6       6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.550667   \n","7       7  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.290667   \n","8       8  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.574667   \n","9       9  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","10     10  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","\n","    precision    recall        f1  \n","0    0.766712  0.792917  0.767940  \n","1    0.724450  0.292000  0.396036  \n","2    0.823098  0.314000  0.444607  \n","3         NaN       NaN       NaN  \n","4    0.000000  0.000000  0.000000  \n","5         NaN       NaN       NaN  \n","6    0.720997  0.550667  0.601762  \n","7    0.696844  0.290667  0.396871  \n","8    0.749095  0.574667  0.626323  \n","9         NaN       NaN       NaN  \n","10        NaN       NaN       NaN  "]},"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>0.730333</td>\n","      <td>0.771041</td>\n","      <td>0.730333</td>\n","      <td>0.746484</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</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>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.665667</td>\n","      <td>0.792412</td>\n","      <td>0.665667</td>\n","      <td>0.712431</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>0.642333</td>\n","      <td>0.764460</td>\n","      <td>0.642333</td>\n","      <td>0.678280</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   epoch                                              model  accuracy  \\\n","0      0                shenzhi-wang/Llama3-8B-Chinese-Chat  0.730333   \n","1      1  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","2      2  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","3      3  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","4      4  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","5      5  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","6      6  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.665667   \n","7      7  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...  0.642333   \n","8      8  shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint...       NaN   \n","\n","   precision    recall        f1  \n","0   0.771041  0.730333  0.746484  \n","1        NaN       NaN       NaN  \n","2        NaN       NaN       NaN  \n","3        NaN       NaN       NaN  \n","4        NaN       NaN       NaN  \n","5        NaN       NaN       NaN  \n","6   0.792412  0.665667  0.712431  \n","7   0.764460  0.642333  0.678280  \n","8        NaN       NaN       NaN  "]},"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":["5"]},"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":13,"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":24,"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":25,"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","0.7963333333333333\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["_, ax = plt.subplots(1, 2, figsize=(15, 5))\n","plot_model_results(\"InternLM_2_5-7b\", df_p1, df_p2, ax)"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: Llama3-8b\n","Best P1 accuracy:\n","0.7836666666666666\n","Best P2 accuracy:\n","0.7303333333333333\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["_, ax = plt.subplots(1, 2, figsize=(15, 5))\n","plot_model_results(\"Llama3-8b\", df_p1_llama3, df_p2_llama3, ax)"]}],"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}