xinyuran/RLRefine-Dataset

RLRefine Dataset

中文说明 | English

中文说明

RLRefine Dataset 是用于中文电商评论结构化关键词抽取的合成后训练数据集。数据通过 LLM-assisted 合成流水线生成，围绕同一批任务 Prompt 派生出 SFT、DPO 和 GRPO 三种 训练格式。该数据集由 RLRefine 项目产出。

本仓库保留 RLRefine checkpoint-924 后训练流程所使用的数据表示。发布文件保持原始训练 分布，没有去重、重排或重新生成，不应作为独立评测集使用。

数据配置

Config	文件	行数	用途
sft	data/checkpoint_924/sft.jsonl	3,696	监督微调示范数据
dpo	data/checkpoint_924/dpo.jsonl	3,696	chosen/rejected 偏好对
grpo	data/checkpoint_924/grpo.jsonl	3,698	GRPO prompt-only 数据

数据格式

SFT

每行包含一个 messages 数组，由 system、user 和 assistant 三条消息组成。assistant 回复包含任务分析与结构化 JSON 结果。

{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}

DPO

messages 中的 assistant 回复作为 chosen，rejected_response 保存低质量拒绝答案。两者 均包含任务分析和结构化结果。

{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ],
  "rejected_response": "..."
}

GRPO

每行只保留 system 和 user 消息。模型在训练时生成 completion，奖励由外部 Reward 函数计算。

{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."}
  ]
}

加载方式

from datasets import load_dataset

sft = load_dataset("xinyuran/RLRefine-Dataset", "sft", split="train")
dpo = load_dataset("xinyuran/RLRefine-Dataset", "dpo", split="train")
grpo = load_dataset("xinyuran/RLRefine-Dataset", "grpo", split="train")

文件校验

文件	SHA256
sft.jsonl	5c12f7ae57cef0939c4b427525d5466e7226b63fbe52d988955ec772ba63f15d
dpo.jsonl	8918ead692a6c9e5bb74b4a167aa19c1284943955c4e72ca19e81d508a52e2dc
grpo.jsonl	9f27d40861ca65a279a2c1c04a23f3bae041c4127797b0050c333edd7cb4078d

使用边界

• 数据聚焦中文电商评论关键词抽取，未验证其他领域或语言的效果。
• 数据与回复均为合成内容，可能包含重复 Prompt、不一致解释或其他生成误差。
• SFT、DPO 和 GRPO 数据复用相同或高度重叠的输入，不能互相作为独立测试集。
• 数据包含较长的任务分析文本，使用者应根据自己的模型模板与上下文预算决定是否保留。
• 本数据集不代表真实用户流量、真实客户需求或生产环境分布。

English

RLRefine Dataset is a synthetic post-training dataset for structured keyword extraction from Chinese e-commerce reviews. An LLM-assisted synthesis pipeline derives SFT, DPO, and GRPO training formats from a shared set of task prompts.

The repository preserves the data representations used in the RLRefine checkpoint-924 post-training pipeline. Files retain the original training distribution without deduplication, reordering, or regeneration and must not be treated as independent evaluation sets.

Configurations

Config	File	Rows	Purpose
sft	data/checkpoint_924/sft.jsonl	3,696	Supervised fine-tuning demonstrations
dpo	data/checkpoint_924/dpo.jsonl	3,696	Chosen/rejected preference pairs
grpo	data/checkpoint_924/grpo.jsonl	3,698	Prompt-only GRPO data

The sft config stores system, user, and assistant messages. In dpo, the assistant message is the chosen response and rejected_response contains the rejected answer. The grpo config stores system and user messages only; an external reward function scores generated completions during training.

Load an individual configuration with:

from datasets import load_dataset

dataset = load_dataset("xinyuran/RLRefine-Dataset", "sft", split="train")

Limitations

• The dataset focuses on Chinese e-commerce review keyword extraction; other domains and languages have not been validated.
• Reviews and responses are synthetic and may contain duplicate prompts, inconsistent rationales, or other generation artifacts.
• SFT, DPO, and GRPO reuse the same or substantially overlapping inputs and cannot serve as independent evaluation sets for one another.
• Responses may contain long task-analysis text. Users should decide whether to retain it based on their model template and context budget.
• The dataset does not represent real user traffic, customer requirements, or a production distribution.

License

Released under the MIT License. The license applies only to material for which the dataset publisher has the right to grant permission and does not supersede applicable law or third-party terms.