mirror of
https://github.com/OpenHands/OpenHands.git
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288 lines
8.0 KiB
Plaintext
Vendored
288 lines
8.0 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"import pandas as pd\n",
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"from tqdm import tqdm\n",
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"\n",
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"tqdm.pandas()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 1. Load raw data and convert to training data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gzip\n",
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"import json\n",
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"\n",
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"from tqdm import tqdm\n",
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"\n",
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"FILE_PATHS = [\n",
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" 'YOURPATH-no-hint-train-t05-run_1/output.with_completions.jsonl.gz',\n",
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" 'YOURPATH-no-hint-train-t05-run_2/output.with_completions.jsonl.gz',\n",
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"]\n",
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"\n",
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"# More memory efficient for large files\n",
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"# Initialize lists to store the data\n",
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"data = []\n",
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"\n",
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"\n",
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"# Read file line by line\n",
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"for FILE_PATH in FILE_PATHS:\n",
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" with gzip.open(FILE_PATH, 'rb') as f: # Use 'rb' for gzipped files\n",
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" for i, line in tqdm(\n",
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" enumerate(f), desc=f'Processing {FILE_PATH.split(\"/\")[-1]}'\n",
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" ):\n",
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" # Parse only the fields we need\n",
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" raw_data = json.loads(line)\n",
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" data.append(\n",
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" {\n",
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" 'resolved': raw_data['report']['resolved'],\n",
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" 'messages': raw_data['raw_completions']['messages']\n",
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" if raw_data['raw_completions'] is not None\n",
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" else None,\n",
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" 'git_patch': raw_data['test_result'].get('git_patch', ''),\n",
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" 'tools': raw_data['raw_completions']['tools']\n",
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" if raw_data['raw_completions'] is not None\n",
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" and 'tools' in raw_data['raw_completions']\n",
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" else None,\n",
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" }\n",
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" )\n",
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"\n",
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"# Convert to DataFrame after collecting all data\n",
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"df = pd.DataFrame(data)\n",
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"print(f'#total amount of data={len(df)}')\n",
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"df = df[~df['messages'].isna()]\n",
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"print(f'#total amount of data after removing nan={len(df)}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Filter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def _contains_multiple_tool_calls(messages: list[dict]) -> bool:\n",
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" return any(\n",
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" message.get('tool_calls') and len(message['tool_calls']) > 1\n",
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" for message in messages\n",
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" )\n",
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"\n",
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"\n",
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"df['contains_multiple_tool_calls'] = df['messages'].apply(_contains_multiple_tool_calls)\n",
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"display(df.groupby(['contains_multiple_tool_calls'])['resolved'].sum())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import copy\n",
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"\n",
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"# Convert function calling messages to non-function calling messages\n",
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"from openhands.llm.fn_call_converter import (\n",
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" FunctionCallConversionError,\n",
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" convert_fncall_messages_to_non_fncall_messages,\n",
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" convert_from_multiple_tool_calls_to_single_tool_call_messages,\n",
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")\n",
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"\n",
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"total_failed = 0\n",
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"\n",
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"\n",
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"def _convert_messages(messages: list[dict], tools: list[dict]) -> list[dict]:\n",
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" global total_failed\n",
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" message_copy = copy.deepcopy(messages)\n",
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" for message in message_copy:\n",
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" if message['content'] is None:\n",
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" message['content'] = ''\n",
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" try:\n",
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" return convert_fncall_messages_to_non_fncall_messages(\n",
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" message_copy, tools, add_in_context_learning_example=False\n",
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" )\n",
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" except FunctionCallConversionError:\n",
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" total_failed += 1\n",
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" # print(f'Failed to convert messages: {messages}\\nTools: {tools}')\n",
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" # traceback.print_exc()\n",
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" return None\n",
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"\n",
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"\n",
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"df['converted_messages'] = df.apply(\n",
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" lambda row: convert_from_multiple_tool_calls_to_single_tool_call_messages(\n",
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" row['messages'], ignore_final_tool_result=True\n",
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" ),\n",
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" axis=1,\n",
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")\n",
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"df['nonfncall_messages'] = df.apply(\n",
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" lambda row: _convert_messages(row['converted_messages'], row['tools']), axis=1\n",
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")\n",
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"print('total nan', df['nonfncall_messages'].isna().sum())\n",
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"df = df[~df['nonfncall_messages'].isna()]\n",
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"print(f'Total failed: {total_failed}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tokenization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pandarallel import pandarallel\n",
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"from transformers import AutoTokenizer\n",
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"\n",
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"os.environ['TOKENIZERS_PARALLELISM'] = 'false'\n",
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"pandarallel.initialize(progress_bar=True, verbose=1, nb_workers=16)\n",
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"tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-7B-Instruct')\n",
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"df['n_tokens'] = df['rm_conv'].parallel_apply(\n",
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" lambda x: len(tokenizer.apply_chat_template(x))\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(f'BEFORE: #total={len(df)}')\n",
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"df_selected = df[df['n_tokens'] < 131072]\n",
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"print(f'AFTER(truncated to 128k): #total={len(df_selected)}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_selected['n_tokens'].describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ecdf of n_tokens\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"display(df.groupby(['resolved'])['n_tokens'].describe())\n",
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"sns.ecdfplot(x='n_tokens', data=df, hue='resolved')\n",
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"plt.show()\n",
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"\n",
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"print(f'#total={len(df)}')\n",
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"df_selected = df[df['n_tokens'] < 131072]\n",
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"print(f'#selected={len(df_selected)}')\n",
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"display(df_selected.groupby(['resolved'])['n_tokens'].describe())\n",
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"sns.ecdfplot(x='n_tokens', data=df_selected, hue='resolved')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_selected[~df_selected['resolved']]['n_tokens'].describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_selected['resolved'].value_counts()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_selected.groupby(['resolved'])['n_tokens'].describe()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Save Resolved Messages for SFT"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_selected[df_selected['resolved']][['nonfncall_messages']].rename(\n",
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" columns={'nonfncall_messages': 'messages'}\n",
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").to_json(\n",
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" os.path.join(\n",
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" 'YOUR_OUTPUT_FOLDER',\n",
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" f'policy_traj_128k_swegym_{df_selected[\"resolved\"].value_counts()[True]}i.jsonl',\n",
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" ),\n",
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" lines=True,\n",
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" orient='records',\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "openhands-ai-CPy6G0pU-py3.12",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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