DCAgent2/bfcl_parity_Qwen2_5_Coder_32B_Instruct_20260430_044332

该数据集包含364个训练示例，主要记录多轮对话数据，涉及代理、模型、任务和工具使用等元信息。每个示例包含对话内容（conversations，其中每条消息有角色和内容）、代理标识（agent）、模型信息（model和model_provider）、日期（date）、任务类型（task）、剧集（episode）、运行ID（run_id）、试验名称（trial_name）、工具定义（tool_definitions，包括函数描述和参数结构）、结果（result）和验证器输出（verifier_output）。工具定义部分详细描述了函数参数，如代码、命令、文件文本、插入行、输入状态、消息、新旧字符串、路径、安全风险、任务列表、思考、超时和视图范围等。数据集适用于自然语言处理任务，如对话系统分析、工具调用评估和模型性能验证。

This dataset contains 364 training examples, primarily recording multi-turn conversation data with metadata such as agent, model, task, and tool usage. Each example includes conversation content (conversations, with each message having a role and content), agent identifier (agent), model information (model and model_provider), date (date), task type (task), episode (episode), run ID (run_id), trial name (trial_name), tool definitions (tool_definitions, including function descriptions and parameter structures), result (result), and verifier output (verifier_output). The tool definitions section details function parameters such as code, command, file text, insert line, input status, message, new/old strings, path, security risk, task list, thought, timeout, and view range. The dataset is suitable for natural language processing tasks, such as dialogue system analysis, tool invocation evaluation, and model performance verification.