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":1,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"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":3,"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":4,"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":5,"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":5,"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":6,"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":10,"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":9,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
epochmodelaccuracyprecisionrecallf1
00.0internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf0.5106670.7432140.5106670.535733
10.2internlm/internlm2_5-7b-chat-1m/checkpoint-35_...0.7843330.7977650.7843330.786494
20.4internlm/internlm2_5-7b-chat-1m/checkpoint-70_...0.7836670.7996980.7836670.788688
30.6internlm/internlm2_5-7b-chat-1m/checkpoint-105...0.7243330.8171170.7243330.756580
40.8internlm/internlm2_5-7b-chat-1m/checkpoint-140...0.8030000.8031410.8030000.802806
51.0internlm/internlm2_5-7b-chat-1m/checkpoint-175...0.7676670.8108440.7676670.784319
61.2internlm/internlm2_5-7b-chat-1m/checkpoint-210...0.7736670.8091670.7736670.787687
71.4internlm/internlm2_5-7b-chat-1m/checkpoint-245...0.7623330.8062290.7623330.777669
81.6internlm/internlm2_5-7b-chat-1m/checkpoint-280...0.7553330.8086200.7553330.775559
91.8internlm/internlm2_5-7b-chat-1m/checkpoint-315...0.7480000.8172000.7480000.773991
102.0internlm/internlm2_5-7b-chat-1m/checkpoint-350...0.7560000.8126880.7560000.777781
00.0Qwen/Qwen2-7B-Instruct_torch.float16_lf0.6193330.7555700.6193330.672630
10.2Qwen/Qwen2-7B-Instruct/checkpoint-35_torch.flo...0.7250000.7840170.7250000.748995
20.4Qwen/Qwen2-7B-Instruct/checkpoint-70_torch.flo...0.7590000.8005300.7590000.774875
30.6Qwen/Qwen2-7B-Instruct/checkpoint-105_torch.fl...0.6926670.8039180.6926670.733248
40.8Qwen/Qwen2-7B-Instruct/checkpoint-140_torch.fl...0.7250000.7952720.7250000.747624
51.0Qwen/Qwen2-7B-Instruct/checkpoint-175_torch.fl...0.6756670.7810150.6756670.708654
61.2Qwen/Qwen2-7B-Instruct/checkpoint-210_torch.fl...0.7013330.7969560.7013330.736268
71.4Qwen/Qwen2-7B-Instruct/checkpoint-245_torch.fl...0.7326670.7922540.7326670.755402
81.6Qwen/Qwen2-7B-Instruct/checkpoint-280_torch.fl...0.6983330.7851270.6983330.729225
91.8Qwen/Qwen2-7B-Instruct/checkpoint-315_torch.fl...0.6783330.7853910.6783330.716413
102.0Qwen/Qwen2-7B-Instruct/checkpoint-350_torch.fl...0.6890000.7929720.6890000.725999
00.0shenzhi-wang/Llama3.1-8B-Chinese-Chat_torch.fl...0.2366670.7457180.2366670.339624
10.2shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.6256670.8274140.6256670.693570
20.4shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.7620000.7899460.7620000.766701
30.6shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.6803330.7980300.6803330.721244
40.8shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.7523330.8074260.7523330.773644
51.0shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.7370000.8090590.7370000.763784
00.0Qwen/Qwen2-72B-Instruct_torch.bfloat16_4bit_lf0.7486670.8038990.7486670.761587
\n","
"],"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 0.748667 \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 0.803899 0.748667 0.761587 "]},"execution_count":9,"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":37,"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":38,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"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":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":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
epochmodelaccuracyprecisionrecallf1
00.0internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf0.5106670.7432140.5106670.535733
10.2internlm/internlm2_5-7b-chat-1m/checkpoint-35_...0.7843330.7977650.7843330.786494
20.4internlm/internlm2_5-7b-chat-1m/checkpoint-70_...0.7836670.7996980.7836670.788688
30.6internlm/internlm2_5-7b-chat-1m/checkpoint-105...0.7243330.8171170.7243330.756580
40.8internlm/internlm2_5-7b-chat-1m/checkpoint-140...0.8030000.8031410.8030000.802806
51.0internlm/internlm2_5-7b-chat-1m/checkpoint-175...0.7676670.8108440.7676670.784319
61.2internlm/internlm2_5-7b-chat-1m/checkpoint-210...0.7736670.8091670.7736670.787687
71.4internlm/internlm2_5-7b-chat-1m/checkpoint-245...0.7623330.8062290.7623330.777669
81.6internlm/internlm2_5-7b-chat-1m/checkpoint-280...0.7553330.8086200.7553330.775559
91.8internlm/internlm2_5-7b-chat-1m/checkpoint-315...0.7480000.8172000.7480000.773991
102.0internlm/internlm2_5-7b-chat-1m/checkpoint-350...0.7560000.8126880.7560000.777781
00.0Qwen/Qwen2-7B-Instruct_torch.float16_lf0.6193330.7555700.6193330.672630
10.2Qwen/Qwen2-7B-Instruct/checkpoint-35_torch.flo...0.7250000.7840170.7250000.748995
20.4Qwen/Qwen2-7B-Instruct/checkpoint-70_torch.flo...0.7590000.8005300.7590000.774875
30.6Qwen/Qwen2-7B-Instruct/checkpoint-105_torch.fl...0.6926670.8039180.6926670.733248
40.8Qwen/Qwen2-7B-Instruct/checkpoint-140_torch.fl...0.7250000.7952720.7250000.747624
51.0Qwen/Qwen2-7B-Instruct/checkpoint-175_torch.fl...0.6756670.7810150.6756670.708654
61.2Qwen/Qwen2-7B-Instruct/checkpoint-210_torch.fl...0.7013330.7969560.7013330.736268
71.4Qwen/Qwen2-7B-Instruct/checkpoint-245_torch.fl...0.7326670.7922540.7326670.755402
81.6Qwen/Qwen2-7B-Instruct/checkpoint-280_torch.fl...0.6983330.7851270.6983330.729225
91.8Qwen/Qwen2-7B-Instruct/checkpoint-315_torch.fl...0.6783330.7853910.6783330.716413
102.0Qwen/Qwen2-7B-Instruct/checkpoint-350_torch.fl...0.6890000.7929720.6890000.725999
00.0shenzhi-wang/Llama3.1-8B-Chinese-Chat_torch.fl...0.2366670.7457180.2366670.339624
10.2shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.6256670.8274140.6256670.693570
20.4shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.7620000.7899460.7620000.766701
30.6shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.6803330.7980300.6803330.721244
40.8shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.7523330.8074260.7523330.773644
51.0shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...0.7370000.8090590.7370000.763784
00.0Qwen/Qwen2-72B-Instruct_torch.bfloat16_4bit_lf0.7473330.8041220.7473330.760783
10.2Qwen/Qwen2-72B-Instruct/checkpoint-35_torch.bf...0.7583330.8199930.7583330.782751
20.4Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...0.7366670.8224870.7366670.770063
30.6Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...0.7570000.8253820.7570000.784000
40.8Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...0.7893330.8229100.7893330.803312
51.0Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...0.7376670.8243650.7376670.769962
61.2Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...0.7630000.8318880.7630000.790108
\n","
"],"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":["
"]},"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}