AbstractPhil/json-coco-format

JSON COCO格式——任务区分SFT数据是一个多任务监督微调数据集，旨在训练模型将图像合成字幕提示转换为JSON格式，且JSON结构因任务而异。该数据集基于MS-COCO字幕（Karpathy分割）构建，使用Claude Sonnet 4.6作为教师模型生成，专为在Qwen/Qwen3.5-0.8B模型上训练每个任务的LoRA而设计。数据集包含三个任务：task_1（幻觉减少）要求从字幕中逐字提取主题/动作/属性值，禁止风格和情绪；task_2（有用泛化）鼓励分类抽象，每个开放词汇字符串都是带括号的规范通用词；task_3（通用符号化）使用纯位置占位符，每个槽有其类型前缀和基于1的单调索引。数据集包含64,385个接受行和2,993个拒绝行，使用CC-BY-4.0许可证。

JSON COCO Format — task-differentiated SFT data is a multi-task supervised fine-tuning dataset that teaches a model to convert image-synthesis caption prompts into JSON whose structure varies by task. Built from MS-COCO captions (Karpathy split) with Claude Sonnet 4.6 as the teacher; designed for training per-task LoRAs on Qwen/Qwen3.5-0.8B. The dataset includes three tasks: task_1 (hallucination_reduction) requires grounded literal extraction from captions; task_2 (useful_generalization) encourages categorical abstraction with canonical generic terms; task_3 (generic_symbolism) uses pure positional placeholders. It contains 64,385 accepted rows and 2,993 rejected rows, licensed under CC-BY-4.0.