diff --git "a/competition/11b_Llama-3_8b_p2_en_analysis.ipynb" "b/competition/11b_Llama-3_8b_p2_en_analysis.ipynb"
new file mode 100644--- /dev/null
+++ "b/competition/11b_Llama-3_8b_p2_en_analysis.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":{"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>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf</th>\n","      <th>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf</th>\n","      <th>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Was Zhen Zhesuo suicide?</td>\n","      <td>No</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Was Zhen Zhesuo sickly?</td>\n","      <td>Yes</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>The painting was by Zhen.</td>\n","      <td>Yes</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Was Zhen with a heart condition?</td>\n","      <td>Yes</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>The wheel was the murderer's weapon.</td>\n","      <td>No</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</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","    </tr>\n","    <tr>\n","      <th>2995</th>\n","      <td>Did the weeping person have to make a sacrific...</td>\n","      <td>Yes</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>Unimportant</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2996</th>\n","      <td>Was the body in the lake?</td>\n","      <td>No</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2997</th>\n","      <td>Do mourners have a special relationship with t...</td>\n","      <td>Yes</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2998</th>\n","      <td>Was the owner of the hat dead?</td>\n","      <td>No</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2999</th>\n","      <td>Was the dead person wounded?</td>\n","      <td>No</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3000 rows × 8 columns</p>\n","</div>"],"text/plain":["                                                   text label  \\\n","0                              Was Zhen Zhesuo suicide?    No   \n","1                               Was Zhen Zhesuo sickly?   Yes   \n","2                             The painting was by Zhen.   Yes   \n","3                      Was Zhen with a heart condition?   Yes   \n","4                  The wheel was the murderer's weapon.    No   \n","...                                                 ...   ...   \n","2995  Did the weeping person have to make a sacrific...   Yes   \n","2996                          Was the body in the lake?    No   \n","2997  Do mourners have a special relationship with t...   Yes   \n","2998                     Was the owner of the hat dead?    No   \n","2999                       Was the dead person wounded?    No   \n","\n","                         title  \\\n","0     The Mystery of the Coast   \n","1     The Mystery of the Coast   \n","2     The Mystery of the Coast   \n","3     The Mystery of the Coast   \n","4     The Mystery of the Coast   \n","...                        ...   \n","2995         Zhen Zhuo's wails   \n","2996         Zhen Zhuo's wails   \n","2997         Zhen Zhuo's wails   \n","2998         Zhen Zhuo's wails   \n","2999         Zhen Zhuo's wails   \n","\n","                                                 puzzle  \\\n","0     In the quiet seaside cottage of a neighbor, a ...   \n","1     In the quiet seaside cottage of a neighbor, a ...   \n","2     In the quiet seaside cottage of a neighbor, a ...   \n","3     In the quiet seaside cottage of a neighbor, a ...   \n","4     In the quiet seaside cottage of a neighbor, a ...   \n","...                                                 ...   \n","2995  One night, in a quiet village, a weeping sound...   \n","2996  One night, in a quiet village, a weeping sound...   \n","2997  One night, in a quiet village, a weeping sound...   \n","2998  One night, in a quiet village, a weeping sound...   \n","2999  One night, in a quiet village, a weeping sound...   \n","\n","                                                  truth  \\\n","0     Zhen Zhesao was a nature-loving painter who ca...   \n","1     Zhen Zhesao was a nature-loving painter who ca...   \n","2     Zhen Zhesao was a nature-loving painter who ca...   \n","3     Zhen Zhesao was a nature-loving painter who ca...   \n","4     Zhen Zhesao was a nature-loving painter who ca...   \n","...                                                 ...   \n","2995  It turned out that the old hat belonged to a l...   \n","2996  It turned out that the old hat belonged to a l...   \n","2997  It turned out that the old hat belonged to a l...   \n","2998  It turned out that the old hat belonged to a l...   \n","2999  It turned out that the old hat belonged to a l...   \n","\n","     meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf  \\\n","0                                                    No                     \n","1                                                   Yes                     \n","2                                                   Yes                     \n","3                                                   Yes                     \n","4                                                    No                     \n","...                                                 ...                     \n","2995                                        Unimportant                     \n","2996                                                 No                     \n","2997                                                Yes                     \n","2998                                                Yes                     \n","2999                                                 No                     \n","\n","     meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf  \\\n","0                                                    No                     \n","1                                                   Yes                     \n","2                                                   Yes                     \n","3                                                   Yes                     \n","4                                                    No                     \n","...                                                 ...                     \n","2995                                                 No                     \n","2996                                                 No                     \n","2997                                                Yes                     \n","2998                                                Yes                     \n","2999                                                 No                     \n","\n","     meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf  \n","0                                                    No                    \n","1                                                   Yes                    \n","2                                                   Yes                    \n","3                                                   Yes                    \n","4                                                    No                    \n","...                                                 ...                    \n","2995                                                 No                    \n","2996                                                 No                    \n","2997                                                Yes                    \n","2998                                                 No                    \n","2999                                                 No                    \n","\n","[3000 rows x 8 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df = pd.read_csv(\"results/llama3-8b_lora_sft_bf16-p2_en.csv\")\n","df"]},{"cell_type":"code","execution_count":6,"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":7,"metadata":{},"outputs":[{"data":{"text/plain":["['text',\n"," 'label',\n"," 'title',\n"," 'puzzle',\n"," 'truth',\n"," 'meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf',\n"," 'meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf',\n"," 'meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf']"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df.columns.to_list()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["********** meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf **********\n","meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf\n","No                       1564\n","Yes                      1012\n","Unimportant               353\n","Incorrect questioning      50\n","Correct answer             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":["********** meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf **********\n","meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf\n","No                       1210\n","Yes                       902\n","Unimportant               746\n","Incorrect questioning      88\n","Correct answer             54\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":["********** meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf **********\n","meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf\n","No                       1589\n","Yes                       895\n","Unimportant               434\n","Incorrect questioning      45\n","Correct answer             37\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":9,"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","    try:\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","    except Exception as e:\n","        print(e)\n","        accuracy = precision = recall = f1 = np.nan\n","\n","    return accuracy, float(precision), float(recall), float(f1)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_84419/3667033001.py:18: 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>0.333333</td>\n","      <td>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...</td>\n","      <td>0.620333</td>\n","      <td>0.663582</td>\n","      <td>0.620333</td>\n","      <td>0.636363</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.666667</td>\n","      <td>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...</td>\n","      <td>0.561333</td>\n","      <td>0.700051</td>\n","      <td>0.561333</td>\n","      <td>0.611304</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.000000</td>\n","      <td>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...</td>\n","      <td>0.620333</td>\n","      <td>0.681920</td>\n","      <td>0.620333</td>\n","      <td>0.640515</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      epoch                                              model  accuracy  \\\n","0  0.333333  meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...  0.620333   \n","1  0.666667  meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...  0.561333   \n","2  1.000000  meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...  0.620333   \n","\n","   precision    recall        f1  \n","0   0.663582  0.620333  0.636363  \n","1   0.700051  0.561333  0.611304  \n","2   0.681920  0.620333  0.640515  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = pd.DataFrame(\n","    columns=[\"epoch\", \"model\", \"accuracy\", \"precision\", \"recall\", \"f1\"]\n",")\n","for i, col in enumerate(df.columns[5:]):\n","    accuracy, precision, recall, f1 = calc_metrics_for_col(df, col)\n","    new_model_metrics = {\n","        \"epoch\": (i + 1) / 3,\n","        \"model\": col,\n","        \"accuracy\": accuracy,\n","        \"precision\": precision,\n","        \"recall\": recall,\n","        \"f1\": f1,\n","    }\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":11,"metadata":{},"outputs":[{"data":{"image/png":"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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():.3f}\",\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\")\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()"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["perf_df.to_csv(\"results/mgtv-llama3_p2_en_full_metrics.csv\", index=False)"]}],"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}