datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Chinese-DeepSeek-V3.2-Exp-chat-example
deepseek/deepseek-v3.2-exp (6.6K) 中文数据集样本
一、前言
本报告基于 deepseek/deepseek-v3.2-exp 模型（官方 API，8K 上下文窗口）进行数据集评测与可视化展示。测试数据集共包含 6,655 轮对话，语言覆盖以中文为主，辅以部分混合语种及非中文输入。本次报告旨在总结模型的对话特征、输入输出长度分布及上下文预算消耗情况，并为后续应用和优化提供参考。
二、数据与方法
数据来源：用户构建的 6,655 轮真实中文对话样本。
估算方法：
中文字符近似为 1 Token；
英文 4 字符 ≈ 1 Token；
用于规模与上下文预算对比，而非精确 Token 计数。
统计维度：
平均 Prompt/Output 长度（字符与估算 Token）；
总 Token 占上下文窗口比例；
语言分布（Prompt 语言类型）；
对话长度分布（用户提问、助手回答、总对话长度）。
三、总体结果
1. 样本概况… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Chinese-DeepSeek-V3.2-Exp-chat-example.DeepSeek-V3.2-Exp-reasoning-example
🐳 DeepSeek-V3.2-Exp-reasoning vs DeepSeek-R1-0528: Math Reasoning Comparison 🍎
Note: DeepSeek-R1-0528 has no explicit chain-of-thought, while deepseek-ai/DeepSeek-V3.2-Exp (abbrev. V3.2-Exp) produces answers with structured derivations. This report was analyzed by GPT-5-Extended-Thinking. The sample size is small; conclusions are for reference only.
Author: Soren
1. Executive Summary
Sample size: 208 problems (mixed types).
Average steps (reasoning… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/DeepSeek-V3.2-Exp-reasoning-example.OpenVerification1_aux_adaptation_examples
Dataset Card for ReexpressAI/OpenVerification1_aux_adaptation_examples
This is additional data as part of ReexpressAI/OpenVerification1. The data fields are slightly different for this data source, so we include this as a separate dataset.
This is example output from the Reexpress MCP Server when using the ReexpressAddTrue, ReexpressAddFalse, or ReexpressAddOOD tools. These are the lines that get saved to the adaptation/running_updates.jsonl file in the model directory.
Refer to… See the full description on the dataset page: https://huggingface.co/datasets/ReexpressAI/OpenVerification1_aux_adaptation_examples.example_ml