{"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":["import re\n","\n","\n","def clean_up(df, model_name):\n","    df[model_name] = df[model_name].apply(\n","        lambda x: re.sub(r\"回答.*是\", \"是\", x)\n","        .replace(\"是男孩\", \"是\")\n","        .replace(\"。\", \"\")\n","        .strip()\n","    )\n","    return df"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"W2QyVreqhOGM","outputId":"68b9590e-1ac6-4c6f-e0c4-e273ec816419"},"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>text</th>\n","      <th>label</th>\n","      <th>title</th>\n","      <th>puzzle</th>\n","      <th>truth</th>\n","      <th>internlm/internlm2_5-7b-chat-1m</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-562</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-1124</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-1686</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-2248</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>甄加索是自杀吗</td>\n","      <td>不是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>甄加索有身体上的疾病吗</td>\n","      <td>是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>画作是甄的</td>\n","      <td>是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>不是</td>\n","      <td>是</td>\n","      <td>不重要</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>甄有心脏病吗</td>\n","      <td>是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>车轮是凶手留下的</td>\n","      <td>不是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>不是</td>\n","      <td>不重要</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2995</th>\n","      <td>哭泣者必须在晚上祭奠吗</td>\n","      <td>是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>不是</td>\n","      <td>不重要</td>\n","      <td>不重要</td>\n","      <td>不重要</td>\n","      <td>不重要</td>\n","    </tr>\n","    <tr>\n","      <th>2996</th>\n","      <td>尸体在湖里吗</td>\n","      <td>不是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>不是</td>\n","      <td>不重要</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>2997</th>\n","      <td>哭泣者和死者有特殊关系吗</td>\n","      <td>是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>2998</th>\n","      <td>是帽子的主人去世了吗</td>\n","      <td>不是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>2999</th>\n","      <td>死者受伤了吗</td>\n","      <td>不是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>不是</td>\n","      <td>不重要</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3000 rows × 10 columns</p>\n","</div>"],"text/plain":["              text label title  \\\n","0          甄加索是自杀吗    不是  海岸之谜   \n","1      甄加索有身体上的疾病吗     是  海岸之谜   \n","2            画作是甄的     是  海岸之谜   \n","3           甄有心脏病吗     是  海岸之谜   \n","4         车轮是凶手留下的    不是  海岸之谜   \n","...            ...   ...   ...   \n","2995   哭泣者必须在晚上祭奠吗     是  甄庄哭声   \n","2996        尸体在湖里吗    不是  甄庄哭声   \n","2997  哭泣者和死者有特殊关系吗     是  甄庄哭声   \n","2998    是帽子的主人去世了吗    不是  甄庄哭声   \n","2999        死者受伤了吗    不是  甄庄哭声   \n","\n","                                                 puzzle  \\\n","0     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","1     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","2     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","3     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","4     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","...                                                 ...   \n","2995  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2996  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2997  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2998  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2999  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","\n","                                                  truth  \\\n","0     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","1     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","2     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","3     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","4     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","...                                                 ...   \n","2995  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2996  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2997  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2998  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2999  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","\n","     internlm/internlm2_5-7b-chat-1m  \\\n","0                                 不是   \n","1                                  是   \n","2                                 不是   \n","3                                  是   \n","4                                 不是   \n","...                              ...   \n","2995                              不是   \n","2996                              不是   \n","2997                               是   \n","2998                               是   \n","2999                              不是   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-562  \\\n","0                                                不是   \n","1                                                 是   \n","2                                                 是   \n","3                                                 是   \n","4                                               不重要   \n","...                                             ...   \n","2995                                            不重要   \n","2996                                            不重要   \n","2997                                              是   \n","2998                                              是   \n","2999                                            不重要   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-1124  \\\n","0                                                 不是   \n","1                                                  是   \n","2                                                不重要   \n","3                                                  是   \n","4                                                 不是   \n","...                                              ...   \n","2995                                             不重要   \n","2996                                              不是   \n","2997                                               是   \n","2998                                               是   \n","2999                                              不是   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-1686  \\\n","0                                                 不是   \n","1                                                  是   \n","2                                                  是   \n","3                                                  是   \n","4                                                 不是   \n","...                                              ...   \n","2995                                             不重要   \n","2996                                              不是   \n","2997                                               是   \n","2998                                               是   \n","2999                                              不是   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-2248  \n","0                                                 不是  \n","1                                                  是  \n","2                                                  是  \n","3                                                  是  \n","4                                                 不是  \n","...                                              ...  \n","2995                                             不重要  \n","2996                                              不是  \n","2997                                               是  \n","2998                                               是  \n","2999                                              不是  \n","\n","[3000 rows x 10 columns]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df = pd.read_csv(\"results/mgtv-results_bf16.csv\")\n","df = clean_up(df, \"internlm/internlm2_5-7b-chat-1m\")\n","df"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from matplotlib import rcParams\n","\n","def plot_value_counts(df, column):\n","    font_family = rcParams[\"font.family\"]\n","    # Set the font to SimHei to support Chinese characters\n","    rcParams[\"font.family\"] = \"STHeiti\"\n","    rcParams[\"axes.unicode_minus\"] = False  # This is to support the minus sign in Chinese.\n","\n","    plt.figure(figsize=(12, 6))\n","    df[column].value_counts().plot(kind=\"bar\")\n","    # add values on top of bars\n","    for i, v in enumerate(df[column].value_counts()):\n","        plt.text(i, v + 0.1, str(v), ha=\"center\")\n","    plt.show()\n","    \n","    rcParams[\"font.family\"] = font_family\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["['text',\n"," 'label',\n"," 'title',\n"," 'puzzle',\n"," 'truth',\n"," 'internlm/internlm2_5-7b-chat-1m',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-562',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-1124',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-1686',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-2248']"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.columns.to_list()"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m **********\n","internlm/internlm2_5-7b-chat-1m\n","不是     1670\n","是      1284\n","不重要      46\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-562 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-562\n","不重要     1939\n","是        917\n","不是       104\n","回答正确      33\n","问法错误       7\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-1124 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-1124\n","不是      1290\n","不重要      829\n","是        811\n","问法错误      49\n","回答正确      21\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-1686 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-1686\n","不是      1599\n","是       1124\n","不重要      215\n","问法错误      43\n","回答正确      19\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-2248 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-2248\n","不是      1325\n","是       1056\n","不重要      506\n","问法错误      78\n","回答正确      35\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["for col in df.columns[5:]:\n","    print(\"*\" * 10, col, \"*\" * 10)\n","    print(df[col].value_counts())\n","    plot_value_counts(df, col)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","\n","\n","def calc_metrics_for_col(df, col):\n","    y_true = df[\"label\"]\n","    y_pred = df[col]\n","\n","    accuracy = accuracy_score(y_true, y_pred)\n","    precision = precision_score(y_true, y_pred, average=\"weighted\", labels=np.unique(y_pred))\n","    recall = recall_score(y_true, y_pred, average=\"weighted\", labels=np.unique(y_pred))\n","    f1 = f1_score(y_true, y_pred, average=\"weighted\", labels=np.unique(y_pred))\n","\n","    return accuracy, float(precision), float(recall), float(f1)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_36964/961288552.py:9: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n","  perf_df = pd.concat([perf_df, pd.DataFrame([new_model_metrics])], ignore_index=True)\n"]},{"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-562</td>\n","      <td>0.359000</td>\n","      <td>0.869958</td>\n","      <td>0.359000</td>\n","      <td>0.392009</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-1124</td>\n","      <td>0.604333</td>\n","      <td>0.769154</td>\n","      <td>0.604333</td>\n","      <td>0.660067</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-1686</td>\n","      <td>0.759667</td>\n","      <td>0.775269</td>\n","      <td>0.759667</td>\n","      <td>0.764930</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-2248</td>\n","      <td>0.680000</td>\n","      <td>0.769638</td>\n","      <td>0.680000</td>\n","      <td>0.715708</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-562  0.359000   0.869958   \n","2     2  internlm/internlm2_5-7b-chat-1m_checkpoint-1124  0.604333   0.769154   \n","3     3  internlm/internlm2_5-7b-chat-1m_checkpoint-1686  0.759667   0.775269   \n","4     4  internlm/internlm2_5-7b-chat-1m_checkpoint-2248  0.680000   0.769638   \n","\n","     recall        f1  \n","0  0.781014  0.758887  \n","1  0.359000  0.392009  \n","2  0.604333  0.660067  \n","3  0.759667  0.764930  \n","4  0.680000  0.715708  "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = pd.DataFrame(columns=[\"epoch\", \"model\", \"accuracy\", \"precision\", \"recall\", \"f1\"])\n","for i, col in enumerate(df.columns[5:]):\n","    accuracy, precision, recall, f1 = calc_metrics_for_col(df, col)\n","    new_model_metrics = {\"epoch\": i, \"model\": col, \"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}\n","\n","    # Convert the dictionary to a DataFrame and concatenate it with the existing DataFrame\n","    perf_df = pd.concat([perf_df, pd.DataFrame([new_model_metrics])], ignore_index=True)\n","\n","perf_df"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot metrics for each model\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n","\n","perf_df.plot(x=\"epoch\", y=[\"accuracy\", \"precision\", \"recall\", \"f1\"], kind=\"bar\", ax=ax)\n","\n","# add values on top of bars\n","for p in ax.patches:\n","    ax.annotate(\n","        f\"{p.get_height():.2f}\",\n","        (p.get_x() + p.get_width() / 2, p.get_height()),\n","        ha=\"center\",\n","        va=\"bottom\",\n","        fontsize=10,\n","    )\n","\n","# add title and labels\n","# ax.set_title(\"Metrics for different settings\")\n","# ax.set_ylabel(\"Value\")\n","ax.set_xlabel(\"Epoch (0: base model, 1-4: fine-tuned models)\")\n","# rotate x labels\n","plt.xticks(rotation=0)\n","\n","# set legend at the right to avoid overlapping with bars\n","plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n","# plt.tight_layout()\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}