diff --git "a/notebooks/00_Data Analysis.ipynb" "b/notebooks/00_Data Analysis.ipynb"
--- "a/notebooks/00_Data Analysis.ipynb"
+++ "b/notebooks/00_Data Analysis.ipynb"
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":39,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":40,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"eb33b19f-1206-41ee-84e2-e6258a12eef7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2534,"status":"ok","timestamp":1720679529344,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"xwFh14uiZBrI","outputId":"d767799c-34c2-46a5-f052-378146a55321"},"outputs":[],"source":["from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n","    try:\n","        from google.colab import drive\n","\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":41,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["working dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["# haotian comp\n","import os\n","import sys\n","from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"working dir:\", workding_dir)"]},{"cell_type":"code","execution_count":43,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"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":43,"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":44,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[],"source":["model_orders = {\n","    \"internlm2_5-7b-chat-1m\": 10,\n","    \"Qwen2-7B-Instruct\": 20,\n","    \"Llama3.1-8B-Chinese-Chat\": 30,\n","    \"Llama3.1-70B-Chinese-Chat\": 40,\n","    \"Qwen2-72B-Instruct\": 50,\n","}"]},{"cell_type":"code","execution_count":45,"metadata":{},"outputs":[],"source":["markers = [\n","    \"o\",\n","    \"x\",\n","    \"^\",\n","    \"s\",\n","    \"d\",\n","    \"P\",\n","    \"X\",\n","    \"*\",\n","    \"v\",\n","    \">\",\n","    \"<\",\n","    \"p\",\n","    \"h\",\n","    \"H\",\n","    \"+\",\n","    \"|\",\n","    \"_\",\n","]\n","model_markers = {k: markers[i] for i, k in enumerate(model_orders.keys())}"]},{"cell_type":"code","execution_count":46,"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>0.0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf</td>\n","      <td>0.510667</td>\n","      <td>0.743214</td>\n","      <td>0.510667</td>\n","      <td>0.535733</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-35_...</td>\n","      <td>0.784333</td>\n","      <td>0.797765</td>\n","      <td>0.784333</td>\n","      <td>0.786494</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-70_...</td>\n","      <td>0.783667</td>\n","      <td>0.799698</td>\n","      <td>0.783667</td>\n","      <td>0.788688</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-105...</td>\n","      <td>0.724333</td>\n","      <td>0.817117</td>\n","      <td>0.724333</td>\n","      <td>0.756580</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-140...</td>\n","      <td>0.803000</td>\n","      <td>0.803141</td>\n","      <td>0.803000</td>\n","      <td>0.802806</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-175...</td>\n","      <td>0.767667</td>\n","      <td>0.810844</td>\n","      <td>0.767667</td>\n","      <td>0.784319</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-210...</td>\n","      <td>0.773667</td>\n","      <td>0.809167</td>\n","      <td>0.773667</td>\n","      <td>0.787687</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1.4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-245...</td>\n","      <td>0.762333</td>\n","      <td>0.806229</td>\n","      <td>0.762333</td>\n","      <td>0.777669</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-280...</td>\n","      <td>0.755333</td>\n","      <td>0.808620</td>\n","      <td>0.755333</td>\n","      <td>0.775559</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1.8</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-315...</td>\n","      <td>0.748000</td>\n","      <td>0.817200</td>\n","      <td>0.748000</td>\n","      <td>0.773991</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2.0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-350...</td>\n","      <td>0.756000</td>\n","      <td>0.812688</td>\n","      <td>0.756000</td>\n","      <td>0.777781</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>Qwen/Qwen2-7B-Instruct_torch.float16_lf</td>\n","      <td>0.619333</td>\n","      <td>0.755570</td>\n","      <td>0.619333</td>\n","      <td>0.672630</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-35_torch.flo...</td>\n","      <td>0.725000</td>\n","      <td>0.784017</td>\n","      <td>0.725000</td>\n","      <td>0.748995</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-70_torch.flo...</td>\n","      <td>0.759000</td>\n","      <td>0.800530</td>\n","      <td>0.759000</td>\n","      <td>0.774875</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-105_torch.fl...</td>\n","      <td>0.692667</td>\n","      <td>0.803918</td>\n","      <td>0.692667</td>\n","      <td>0.733248</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-140_torch.fl...</td>\n","      <td>0.725000</td>\n","      <td>0.795272</td>\n","      <td>0.725000</td>\n","      <td>0.747624</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-175_torch.fl...</td>\n","      <td>0.675667</td>\n","      <td>0.781015</td>\n","      <td>0.675667</td>\n","      <td>0.708654</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-210_torch.fl...</td>\n","      <td>0.701333</td>\n","      <td>0.796956</td>\n","      <td>0.701333</td>\n","      <td>0.736268</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1.4</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-245_torch.fl...</td>\n","      <td>0.732667</td>\n","      <td>0.792254</td>\n","      <td>0.732667</td>\n","      <td>0.755402</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.6</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-280_torch.fl...</td>\n","      <td>0.698333</td>\n","      <td>0.785127</td>\n","      <td>0.698333</td>\n","      <td>0.729225</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1.8</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-315_torch.fl...</td>\n","      <td>0.678333</td>\n","      <td>0.785391</td>\n","      <td>0.678333</td>\n","      <td>0.716413</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2.0</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-350_torch.fl...</td>\n","      <td>0.689000</td>\n","      <td>0.792972</td>\n","      <td>0.689000</td>\n","      <td>0.725999</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat_torch.fl...</td>\n","      <td>0.236667</td>\n","      <td>0.745718</td>\n","      <td>0.236667</td>\n","      <td>0.339624</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.625667</td>\n","      <td>0.827414</td>\n","      <td>0.625667</td>\n","      <td>0.693570</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.762000</td>\n","      <td>0.789946</td>\n","      <td>0.762000</td>\n","      <td>0.766701</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.680333</td>\n","      <td>0.798030</td>\n","      <td>0.680333</td>\n","      <td>0.721244</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.752333</td>\n","      <td>0.807426</td>\n","      <td>0.752333</td>\n","      <td>0.773644</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.737000</td>\n","      <td>0.809059</td>\n","      <td>0.737000</td>\n","      <td>0.763784</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>Qwen/Qwen2-72B-Instruct_torch.bfloat16_4bit_lf</td>\n","      <td>0.747333</td>\n","      <td>0.804122</td>\n","      <td>0.747333</td>\n","      <td>0.760783</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-35_torch.bf...</td>\n","      <td>0.758333</td>\n","      <td>0.819993</td>\n","      <td>0.758333</td>\n","      <td>0.782751</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...</td>\n","      <td>0.736667</td>\n","      <td>0.822487</td>\n","      <td>0.736667</td>\n","      <td>0.770063</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...</td>\n","      <td>0.757000</td>\n","      <td>0.825382</td>\n","      <td>0.757000</td>\n","      <td>0.784000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...</td>\n","      <td>0.789333</td>\n","      <td>0.822910</td>\n","      <td>0.789333</td>\n","      <td>0.803312</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...</td>\n","      <td>0.737667</td>\n","      <td>0.824365</td>\n","      <td>0.737667</td>\n","      <td>0.769962</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...</td>\n","      <td>0.763000</td>\n","      <td>0.831888</td>\n","      <td>0.763000</td>\n","      <td>0.790108</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    epoch                                              model  accuracy  \\\n","0     0.0  internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf  0.510667   \n","1     0.2  internlm/internlm2_5-7b-chat-1m/checkpoint-35_...  0.784333   \n","2     0.4  internlm/internlm2_5-7b-chat-1m/checkpoint-70_...  0.783667   \n","3     0.6  internlm/internlm2_5-7b-chat-1m/checkpoint-105...  0.724333   \n","4     0.8  internlm/internlm2_5-7b-chat-1m/checkpoint-140...  0.803000   \n","5     1.0  internlm/internlm2_5-7b-chat-1m/checkpoint-175...  0.767667   \n","6     1.2  internlm/internlm2_5-7b-chat-1m/checkpoint-210...  0.773667   \n","7     1.4  internlm/internlm2_5-7b-chat-1m/checkpoint-245...  0.762333   \n","8     1.6  internlm/internlm2_5-7b-chat-1m/checkpoint-280...  0.755333   \n","9     1.8  internlm/internlm2_5-7b-chat-1m/checkpoint-315...  0.748000   \n","10    2.0  internlm/internlm2_5-7b-chat-1m/checkpoint-350...  0.756000   \n","0     0.0            Qwen/Qwen2-7B-Instruct_torch.float16_lf  0.619333   \n","1     0.2  Qwen/Qwen2-7B-Instruct/checkpoint-35_torch.flo...  0.725000   \n","2     0.4  Qwen/Qwen2-7B-Instruct/checkpoint-70_torch.flo...  0.759000   \n","3     0.6  Qwen/Qwen2-7B-Instruct/checkpoint-105_torch.fl...  0.692667   \n","4     0.8  Qwen/Qwen2-7B-Instruct/checkpoint-140_torch.fl...  0.725000   \n","5     1.0  Qwen/Qwen2-7B-Instruct/checkpoint-175_torch.fl...  0.675667   \n","6     1.2  Qwen/Qwen2-7B-Instruct/checkpoint-210_torch.fl...  0.701333   \n","7     1.4  Qwen/Qwen2-7B-Instruct/checkpoint-245_torch.fl...  0.732667   \n","8     1.6  Qwen/Qwen2-7B-Instruct/checkpoint-280_torch.fl...  0.698333   \n","9     1.8  Qwen/Qwen2-7B-Instruct/checkpoint-315_torch.fl...  0.678333   \n","10    2.0  Qwen/Qwen2-7B-Instruct/checkpoint-350_torch.fl...  0.689000   \n","0     0.0  shenzhi-wang/Llama3.1-8B-Chinese-Chat_torch.fl...  0.236667   \n","1     0.2  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.625667   \n","2     0.4  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.762000   \n","3     0.6  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.680333   \n","4     0.8  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.752333   \n","5     1.0  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.737000   \n","0     0.0     Qwen/Qwen2-72B-Instruct_torch.bfloat16_4bit_lf  0.747333   \n","1     0.2  Qwen/Qwen2-72B-Instruct/checkpoint-35_torch.bf...  0.758333   \n","2     0.4  Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...  0.736667   \n","3     0.6  Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...  0.757000   \n","4     0.8  Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...  0.789333   \n","5     1.0  Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...  0.737667   \n","6     1.2  Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...  0.763000   \n","\n","    precision    recall        f1  \n","0    0.743214  0.510667  0.535733  \n","1    0.797765  0.784333  0.786494  \n","2    0.799698  0.783667  0.788688  \n","3    0.817117  0.724333  0.756580  \n","4    0.803141  0.803000  0.802806  \n","5    0.810844  0.767667  0.784319  \n","6    0.809167  0.773667  0.787687  \n","7    0.806229  0.762333  0.777669  \n","8    0.808620  0.755333  0.775559  \n","9    0.817200  0.748000  0.773991  \n","10   0.812688  0.756000  0.777781  \n","0    0.755570  0.619333  0.672630  \n","1    0.784017  0.725000  0.748995  \n","2    0.800530  0.759000  0.774875  \n","3    0.803918  0.692667  0.733248  \n","4    0.795272  0.725000  0.747624  \n","5    0.781015  0.675667  0.708654  \n","6    0.796956  0.701333  0.736268  \n","7    0.792254  0.732667  0.755402  \n","8    0.785127  0.698333  0.729225  \n","9    0.785391  0.678333  0.716413  \n","10   0.792972  0.689000  0.725999  \n","0    0.745718  0.236667  0.339624  \n","1    0.827414  0.625667  0.693570  \n","2    0.789946  0.762000  0.766701  \n","3    0.798030  0.680333  0.721244  \n","4    0.807426  0.752333  0.773644  \n","5    0.809059  0.737000  0.763784  \n","0    0.804122  0.747333  0.760783  \n","1    0.819993  0.758333  0.782751  \n","2    0.822487  0.736667  0.770063  \n","3    0.825382  0.757000  0.784000  \n","4    0.822910  0.789333  0.803312  \n","5    0.824365  0.737667  0.769962  \n","6    0.831888  0.763000  0.790108  "]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = None\n","model_perf_dfs = {}\n","for model_name in model_orders.keys():\n","    metrics_csv = f\"data/{model_name}_metrics.csv\"\n","    if not Path(metrics_csv).exists():\n","        continue\n","    df = pd.read_csv(metrics_csv)\n","    model_perf_dfs[model_name] = df\n","    perf_df = df if perf_df is None else pd.concat([perf_df, df])\n","\n","perf_df"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from matplotlib.ticker import MultipleLocator\n","\n","def plot_perf(\n","    model_perf_dfs,\n","    model_markers,\n","    x_major_locator=0.2,\n","    y_offset=0.05,\n","):\n","    fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n","\n","    for model_name, perf_df in model_perf_dfs.items():\n","        # Ensure the lengths of perf_df[\"epoch\"], perf_df[\"accuracy\"], and perf_df[\"f1\"] are the same\n","        min_length = min(len(perf_df[\"epoch\"]), len(perf_df[\"accuracy\"]), len(perf_df[\"f1\"]))\n","        perf_df = perf_df.iloc[:min_length]\n","\n","        ax.plot(\n","            perf_df[\"epoch\"], perf_df[\"f1\"], marker=model_markers[model_name], label=model_name\n","        )\n","\n","        best_f1 = perf_df[\"f1\"].idxmax()\n","        ax.annotate(\n","            f\"{perf_df['f1'].iloc[best_f1]*100:.2f}%\",\n","            (perf_df[\"epoch\"].iloc[best_f1], perf_df[\"f1\"].iloc[best_f1]),\n","            ha=\"center\",\n","            va=\"bottom\",\n","            xytext=(0, 0),\n","            textcoords=\"offset points\",\n","            fontsize=10,\n","        )\n","\n","    # Set y-axis limit\n","    y_scales = ax.get_ylim()\n","    ax.set_ylim(y_scales[0], y_scales[1] + y_offset)\n","\n","    # Add title and labels\n","    ax.set_xlabel(\"Epoch (0: base model, 0.2 - 2: fine-tuned models)\")\n","    ax.set_ylabel(\"F1 Score\")\n","\n","    # Set x-axis grid spacing to 0.2\n","    ax.xaxis.set_major_locator(MultipleLocator(x_major_locator))\n","    ax.set_title(\n","        \"Performance Analysis Across Checkpoints for Models\"\n","    )\n","\n","    # Rotate x labels\n","    plt.xticks(rotation=0)\n","    plt.grid(True)\n","    # plt.tight_layout()\n","\n","    # Set legend at the right to avoid overlapping with lines\n","    plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n","\n","    plt.show()"]},{"cell_type":"code","execution_count":48,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_perf(model_perf_dfs, model_markers)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","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":122,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":123,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"eb33b19f-1206-41ee-84e2-e6258a12eef7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2534,"status":"ok","timestamp":1720679529344,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"xwFh14uiZBrI","outputId":"d767799c-34c2-46a5-f052-378146a55321"},"outputs":[],"source":["from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n","    try:\n","        from google.colab import drive\n","\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":124,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":125,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["working dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["# haotian comp\n","import os\n","import sys\n","from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"working dir:\", workding_dir)"]},{"cell_type":"code","execution_count":126,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"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":126,"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":127,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[],"source":["model_orders = {\n","    \"Mistral-7B-v0.3-Chinese-Chat\": 5,\n","    \"internlm2_5-7b-chat-1m\": 10,\n","    \"Qwen2-7B-Instruct\": 20,\n","    \"Llama3.1-8B-Chinese-Chat\": 30,\n","    \"Llama3.1-70B-Chinese-Chat\": 40,\n","    \"Qwen2-72B-Instruct\": 50,\n","}"]},{"cell_type":"code","execution_count":128,"metadata":{},"outputs":[],"source":["markers = [\n","    \"o\",\n","    \"x\",\n","    \"^\",\n","    \"s\",\n","    \"d\",\n","    \"P\",\n","    \"X\",\n","    \"*\",\n","    \"v\",\n","    \">\",\n","    \"<\",\n","    \"p\",\n","    \"h\",\n","    \"H\",\n","    \"+\",\n","    \"|\",\n","    \"_\",\n","]\n","model_markers = {k: markers[i] for i, k in enumerate(model_orders.keys())}"]},{"cell_type":"code","execution_count":129,"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","      <th>ratio_valid_classifications</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat_torc...</td>\n","      <td>0.711333</td>\n","      <td>0.702205</td>\n","      <td>0.711333</td>\n","      <td>0.689497</td>\n","      <td>0.004</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...</td>\n","      <td>0.702000</td>\n","      <td>0.793273</td>\n","      <td>0.702000</td>\n","      <td>0.734271</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...</td>\n","      <td>0.742000</td>\n","      <td>0.789829</td>\n","      <td>0.742000</td>\n","      <td>0.753668</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...</td>\n","      <td>0.659667</td>\n","      <td>0.792340</td>\n","      <td>0.659667</td>\n","      <td>0.706754</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...</td>\n","      <td>0.714667</td>\n","      <td>0.786134</td>\n","      <td>0.714667</td>\n","      <td>0.740468</td>\n","      <td>1.000</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","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...</td>\n","      <td>0.736667</td>\n","      <td>0.822487</td>\n","      <td>0.736667</td>\n","      <td>0.770063</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...</td>\n","      <td>0.757000</td>\n","      <td>0.825382</td>\n","      <td>0.757000</td>\n","      <td>0.784000</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...</td>\n","      <td>0.789333</td>\n","      <td>0.822910</td>\n","      <td>0.789333</td>\n","      <td>0.803312</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...</td>\n","      <td>0.737667</td>\n","      <td>0.824365</td>\n","      <td>0.737667</td>\n","      <td>0.769962</td>\n","      <td>1.000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...</td>\n","      <td>0.763000</td>\n","      <td>0.831888</td>\n","      <td>0.763000</td>\n","      <td>0.790108</td>\n","      <td>1.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>62 rows × 7 columns</p>\n","</div>"],"text/plain":["    epoch                                              model  accuracy  \\\n","0     0.0  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat_torc...  0.711333   \n","1     0.2  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...  0.702000   \n","2     0.4  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...  0.742000   \n","3     0.6  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...  0.659667   \n","4     0.8  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/chec...  0.714667   \n","..    ...                                                ...       ...   \n","2     0.4  Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...  0.736667   \n","3     0.6  Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...  0.757000   \n","4     0.8  Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...  0.789333   \n","5     1.0  Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...  0.737667   \n","6     1.2  Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...  0.763000   \n","\n","    precision    recall        f1  ratio_valid_classifications  \n","0    0.702205  0.711333  0.689497                        0.004  \n","1    0.793273  0.702000  0.734271                        1.000  \n","2    0.789829  0.742000  0.753668                        1.000  \n","3    0.792340  0.659667  0.706754                        1.000  \n","4    0.786134  0.714667  0.740468                        1.000  \n","..        ...       ...       ...                          ...  \n","2    0.822487  0.736667  0.770063                        1.000  \n","3    0.825382  0.757000  0.784000                        1.000  \n","4    0.822910  0.789333  0.803312                        1.000  \n","5    0.824365  0.737667  0.769962                        1.000  \n","6    0.831888  0.763000  0.790108                        1.000  \n","\n","[62 rows x 7 columns]"]},"execution_count":129,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = None\n","model_perf_dfs = {}\n","for model_name in model_orders.keys():\n","    metrics_csv = f\"data/{model_name}_metrics.csv\"\n","    if not Path(metrics_csv).exists():\n","        continue\n","    df = pd.read_csv(metrics_csv)\n","    model_perf_dfs[model_name] = df\n","    perf_df = df if perf_df is None else pd.concat([perf_df, df])\n","\n","perf_df"]},{"cell_type":"code","execution_count":130,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from matplotlib.ticker import MultipleLocator\n","\n","def plot_perf(\n","    model_perf_dfs,\n","    model_markers,\n","    x_major_locator=0.2,\n","    y_offset=0.005,\n","):\n","    fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n","\n","    for model_name, perf_df in model_perf_dfs.items():\n","        # Ensure the lengths of perf_df[\"epoch\"], perf_df[\"accuracy\"], and perf_df[\"f1\"] are the same\n","        min_length = min(len(perf_df[\"epoch\"]), len(perf_df[\"accuracy\"]), len(perf_df[\"f1\"]))\n","        perf_df = perf_df.iloc[:min_length]\n","\n","        (line,) = ax.plot(\n","            perf_df[\"epoch\"],\n","            perf_df[\"f1\"],\n","            marker=model_markers[model_name],\n","            label=model_name,\n","        )\n","\n","        line_color = line.get_color()\n","\n","        best_f1 = perf_df[\"f1\"].idxmax()\n","        print(f\"Best F1 for {model_name}: {perf_df['f1'].iloc[best_f1]}\")\n","        ax.annotate(\n","            f\"{perf_df['f1'].iloc[best_f1]*100:.2f}%\",\n","            (perf_df[\"epoch\"].iloc[best_f1], perf_df[\"f1\"].iloc[best_f1]),\n","            ha=\"center\",\n","            va=\"bottom\",\n","            xytext=(0, 0),\n","            textcoords=\"offset points\",\n","            fontsize=10,\n","            color=line_color,\n","        )\n","\n","    # Set y-axis limit\n","    y_scales = ax.get_ylim()\n","    ax.set_ylim(y_scales[0], y_scales[1] + y_offset)\n","\n","    # Add title and labels\n","    ax.set_xlabel(\"Epoch (0: base model, 0.2 - 2: fine-tuned models)\")\n","    ax.set_ylabel(\"F1 Score\")\n","\n","    # Set x-axis grid spacing to 0.2\n","    ax.xaxis.set_major_locator(MultipleLocator(x_major_locator))\n","    ax.set_title(\n","        \"Performance Analysis Across Checkpoints for Models\"\n","    )\n","\n","    # Rotate x labels\n","    plt.xticks(rotation=0)\n","    plt.grid(True)\n","    # plt.tight_layout()\n","\n","    # Set legend at the right to avoid overlapping with lines\n","    plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n","\n","    plt.show()"]},{"cell_type":"code","execution_count":131,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_perf(model_perf_dfs, model_markers)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","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}