diff --git "a/competition/00e_InternLM2.5_Results.ipynb" "b/competition/00e_InternLM2.5_Results.ipynb"
new file mode 100644--- /dev/null
+++ "b/competition/00e_InternLM2.5_Results.ipynb"
@@ -0,0 +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":["
\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.759667 | \n"," 0.741854 | \n"," 0.781014 | \n"," 0.758887 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-44 | \n"," 0.761667 | \n"," 0.810873 | \n"," 0.761667 | \n"," 0.780018 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-88 | \n"," 0.741333 | \n"," 0.816182 | \n"," 0.741333 | \n"," 0.769524 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-132 | \n"," 0.755000 | \n"," 0.809829 | \n"," 0.755000 | \n"," 0.775657 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-176 | \n"," 0.719000 | \n"," 0.803307 | \n"," 0.719000 | \n"," 0.750319 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 internlm/internlm2_5-7b-chat-1m 0.759667 0.741854 \n","1 1 internlm/internlm2_5-7b-chat-1m_checkpoint-44 0.761667 0.810873 \n","2 2 internlm/internlm2_5-7b-chat-1m_checkpoint-88 0.741333 0.816182 \n","3 3 internlm/internlm2_5-7b-chat-1m_checkpoint-132 0.755000 0.809829 \n","4 4 internlm/internlm2_5-7b-chat-1m_checkpoint-176 0.719000 0.803307 \n","\n"," recall f1 \n","0 0.781014 0.758887 \n","1 0.761667 0.780018 \n","2 0.741333 0.769524 \n","3 0.755000 0.775657 \n","4 0.719000 0.750319 "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df_p1 = pd.read_csv(\"results/mgtv-results_p1_full_metrics.csv\")\n","df_p1"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.766000 | \n"," 0.747969 | \n"," 0.787526 | \n"," 0.764922 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-88 | \n"," 0.796333 | \n"," 0.808232 | \n"," 0.796333 | \n"," 0.798160 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-176 | \n"," 0.781333 | \n"," 0.804716 | \n"," 0.781333 | \n"," 0.788581 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-264 | \n"," 0.759000 | \n"," 0.805502 | \n"," 0.759000 | \n"," 0.777237 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-352 | \n"," 0.730333 | \n"," 0.790676 | \n"," 0.730333 | \n"," 0.753716 | \n","
\n"," \n"," 5 | \n"," 5 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-440 | \n"," 0.730333 | \n"," 0.790420 | \n"," 0.730333 | \n"," 0.753750 | \n","
\n"," \n"," 6 | \n"," 6 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-528 | \n"," 0.716000 | \n"," 0.789892 | \n"," 0.716000 | \n"," 0.744833 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 internlm/internlm2_5-7b-chat-1m 0.766000 0.747969 \n","1 1 internlm/internlm2_5-7b-chat-1m_checkpoint-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":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.766000 | \n"," 0.747969 | \n"," 0.787526 | \n"," 0.764922 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-175 | \n"," 0.812000 | \n"," 0.812286 | \n"," 0.812000 | \n"," 0.810234 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-350 | \n"," 0.765333 | \n"," 0.806889 | \n"," 0.765333 | \n"," 0.779998 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-525 | \n"," 0.747667 | \n"," 0.812033 | \n"," 0.747667 | \n"," 0.773122 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-700 | \n"," 0.717000 | \n"," 0.804642 | \n"," 0.717000 | \n"," 0.751034 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 internlm/internlm2_5-7b-chat-1m 0.766000 0.747969 \n","1 1 internlm/internlm2_5-7b-chat-1m_checkpoint-175 0.812000 0.812286 \n","2 2 internlm/internlm2_5-7b-chat-1m_checkpoint-350 0.765333 0.806889 \n","3 3 internlm/internlm2_5-7b-chat-1m_checkpoint-525 0.747667 0.812033 \n","4 4 internlm/internlm2_5-7b-chat-1m_checkpoint-700 0.717000 0.804642 \n","\n"," recall f1 \n","0 0.787526 0.764922 \n","1 0.812000 0.810234 \n","2 0.765333 0.779998 \n","3 0.747667 0.773122 \n","4 0.717000 0.751034 "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_r2 = pd.read_csv(\"results/mgtv-results_p2_r2_full_metrics.csv\")\n","df_p2_r2"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat | \n"," 0.730333 | \n"," 0.771041 | \n"," 0.730333 | \n"," 0.746484 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.767333 | \n"," 0.795680 | \n"," 0.767333 | \n"," 0.770909 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.728667 | \n"," 0.820787 | \n"," 0.728667 | \n"," 0.763019 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.698000 | \n"," 0.795399 | \n"," 0.698000 | \n"," 0.733230 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.709333 | \n"," 0.793851 | \n"," 0.709333 | \n"," 0.740102 | \n","
\n"," \n"," 5 | \n"," 5 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.679333 | \n"," 0.791366 | \n"," 0.679333 | \n"," 0.720183 | \n","
\n"," \n"," 6 | \n"," 6 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.667000 | \n"," 0.787851 | \n"," 0.667000 | \n"," 0.710294 | \n","
\n"," \n"," 7 | \n"," 7 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.645667 | \n"," 0.764853 | \n"," 0.645667 | \n"," 0.680705 | \n","
\n"," \n"," 8 | \n"," 8 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.654667 | \n"," 0.769380 | \n"," 0.654667 | \n"," 0.687044 | \n","
\n"," \n"," 9 | \n"," 9 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.643000 | \n"," 0.766252 | \n"," 0.643000 | \n"," 0.678544 | \n","
\n"," \n"," 10 | \n"," 10 | \n"," shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... | \n"," 0.647667 | \n"," 0.766725 | \n"," 0.647667 | \n"," 0.684851 | \n","
\n"," \n","
\n","
"],"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... 0.767333 \n","2 2 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.728667 \n","3 3 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.698000 \n","4 4 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.709333 \n","5 5 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.679333 \n","6 6 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.667000 \n","7 7 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.645667 \n","8 8 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.654667 \n","9 9 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.643000 \n","10 10 shenzhi-wang/Llama3-8B-Chinese-Chat_checkpoint... 0.647667 \n","\n"," precision recall f1 \n","0 0.771041 0.730333 0.746484 \n","1 0.795680 0.767333 0.770909 \n","2 0.820787 0.728667 0.763019 \n","3 0.795399 0.698000 0.733230 \n","4 0.793851 0.709333 0.740102 \n","5 0.791366 0.679333 0.720183 \n","6 0.787851 0.667000 0.710294 \n","7 0.764853 0.645667 0.680705 \n","8 0.769380 0.654667 0.687044 \n","9 0.766252 0.643000 0.678544 \n","10 0.766725 0.647667 0.684851 "]},"execution_count":11,"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":12,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," THUDM/glm-4-9b-chat-1m | \n"," 0.581000 | \n"," 0.703006 | \n"," 0.581000 | \n"," 0.616915 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-175 | \n"," 0.465000 | \n"," 0.462698 | \n"," 0.478067 | \n"," 0.452823 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-350 | \n"," 0.579000 | \n"," 0.677205 | \n"," 0.579000 | \n"," 0.607769 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-525 | \n"," 0.605333 | \n"," 0.722023 | \n"," 0.605333 | \n"," 0.637907 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-700 | \n"," 0.593000 | \n"," 0.720287 | \n"," 0.593000 | \n"," 0.631179 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 THUDM/glm-4-9b-chat-1m 0.581000 0.703006 \n","1 1 THUDM/glm-4-9b-chat-1m_checkpoint-175 0.465000 0.462698 \n","2 2 THUDM/glm-4-9b-chat-1m_checkpoint-350 0.579000 0.677205 \n","3 3 THUDM/glm-4-9b-chat-1m_checkpoint-525 0.605333 0.722023 \n","4 4 THUDM/glm-4-9b-chat-1m_checkpoint-700 0.593000 0.720287 \n","\n"," recall f1 \n","0 0.581000 0.616915 \n","1 0.478067 0.452823 \n","2 0.579000 0.607769 \n","3 0.605333 0.637907 \n","4 0.593000 0.631179 "]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df_p1_glm_4 = pd.read_csv(\"results/mgtv-glm-4-9b_p1_full_metrics.csv\")\n","df_p1_glm_4"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," THUDM/glm-4-9b-chat-1m | \n"," 0.395000 | \n"," 0.667648 | \n"," 0.395000 | \n"," 0.458390 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-175 | \n"," 0.594667 | \n"," 0.705625 | \n"," 0.594667 | \n"," 0.631524 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-350 | \n"," 0.549000 | \n"," 0.700654 | \n"," 0.549000 | \n"," 0.595640 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-525 | \n"," 0.598667 | \n"," 0.715051 | \n"," 0.598667 | \n"," 0.625357 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," THUDM/glm-4-9b-chat-1m_checkpoint-700 | \n"," 0.584333 | \n"," 0.730090 | \n"," 0.584333 | \n"," 0.619578 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 THUDM/glm-4-9b-chat-1m 0.395000 0.667648 \n","1 1 THUDM/glm-4-9b-chat-1m_checkpoint-175 0.594667 0.705625 \n","2 2 THUDM/glm-4-9b-chat-1m_checkpoint-350 0.549000 0.700654 \n","3 3 THUDM/glm-4-9b-chat-1m_checkpoint-525 0.598667 0.715051 \n","4 4 THUDM/glm-4-9b-chat-1m_checkpoint-700 0.584333 0.730090 \n","\n"," recall f1 \n","0 0.395000 0.458390 \n","1 0.594667 0.631524 \n","2 0.549000 0.595640 \n","3 0.598667 0.625357 \n","4 0.584333 0.619578 "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_glm_4 = pd.read_csv(\"results/mgtv-glm-4-9b_p2_full_metrics.csv\")\n","df_p2_glm_4"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[],"source":["def plot_results(df_p1, df_p2, best_p1, best_p2, color_p1=\"red\", color_p2=\"blue\", model_name=\"InternLM2.5_7b\"):\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"accuracy\",\n"," data=df_p1,\n"," ax=ax[0],\n"," color=color_p1,\n"," label=f\"{model_name}: P1\",\n"," )\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"accuracy\",\n"," data=df_p2,\n"," ax=ax[0],\n"," color=color_p2,\n"," label=f\"{model_name}: P2\",\n"," )\n"," sns.scatterplot(\n"," x=\"epoch\", y=\"accuracy\", data=best_p1, ax=ax[0], color=color_p1, s=50\n"," )\n"," sns.scatterplot(\n"," x=\"epoch\", y=\"accuracy\", data=best_p2, ax=ax[0], color=color_p2, s=50\n"," )\n","\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"f1\",\n"," data=df_p1,\n"," ax=ax[1],\n"," color=color_p1,\n"," label=f\"{model_name}: P1\",\n"," )\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"f1\",\n"," data=df_p2,\n"," ax=ax[1],\n"," color=color_p2,\n"," label=f\"{model_name}: P2\",\n"," )\n"," sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p1, ax=ax[1], color=color_p1, s=50)\n"," sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p2, ax=ax[1], color=color_p2, s=50)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","def plot_model_results(model_name, df_p1, df_p2, ax):\n"," print(f\"Model: {model_name}\")\n"," sns.set_theme(style=\"whitegrid\")\n","\n"," # print the best results\n"," best_p1 = df_p1[df_p1[\"accuracy\"] == df_p1[\"accuracy\"].max()]\n"," best_p2 = df_p2[df_p2[\"accuracy\"] == df_p2[\"accuracy\"].max()]\n","\n"," print(\"Best P1 accuracy:\")\n"," print(best_p1[\"accuracy\"].values[0])\n"," print(\"Best P2 accuracy:\")\n"," print(best_p2[\"accuracy\"].values[0])\n","\n"," plot_results(df_p1, df_p2, best_p1, best_p2, model_name=model_name)\n","\n"," for a in ax:\n"," for line_index, line in enumerate(a.lines):\n"," # Get the data\n"," line_color = line.get_color()\n"," xdata, ydata = line.get_data()\n"," for index in range(xdata.size):\n"," a.annotate( # Use 'a' instead of 'ax' to refer to the current subplot\n"," f\"{ydata[index]:.3f}\",\n"," xy=(xdata[index], ydata[index]),\n"," textcoords=\"offset points\",\n"," xytext=(\n"," 0,\n"," 1,\n"," # -10 if line_index % 2 == 0 else 10,\n"," ), # Adjusted for better visibility\n"," ha=\"center\",\n"," color=line_color,\n"," )\n","\n"," ax[0].set_title(\"Accuracy\")\n"," ax[1].set_title(\"F1\")\n","\n"," # After plotting your data and before plt.show(), add these lines\n"," ax[0].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n"," ax[1].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","\n"," plt.show()"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: InternLM_2_5-7b\n","Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n"]},{"data":{"image/png":"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","text/plain":["