datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
casimedicos-exp
Antidote CasiMedicos Dataset - Possible Answers Explanations in Resident Medical Exams
We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments
for the correct answer but also arguments to explain why the remaining possible answers are incorrect.
This dataset can be used for various NLP tasks including: Medical Question Answering, Explanatory Argument Extraction or Explanation Generation.
The… See the full description on the dataset page: https://huggingface.co/datasets/HiTZ/casimedicos-exp.ExploreToM
Data sample for ExploreToM: Program-guided adversarial data generation for theory of mind reasoning
ExploreToM is the first framework to allow large-scale generation of diverse and challenging theory of mind data for robust training and evaluation.
Our approach leverages an A* search over a custom domain-specific language to produce complex story structures and novel, diverse, yet plausible scenarios to stress test the limits of LLMs.
Our A* search procedure aims to find… See the full description on the dataset page: https://huggingface.co/datasets/facebook/ExploreToM.balanced-copa-explanations
Dataset Card for "Balanced COPA"
Dataset Summary
Bala-COPA: An English language Dataset for Training Robust Commonsense Causal Reasoning Models
The Balanced Choice of Plausible Alternatives dataset is a benchmark for training machine learning models that are robust to superficial cues/spurious correlations. The dataset extends the COPA dataset(Roemmele et al. 2011) with mirrored instances that mitigate against token-level superficial cues in the original COPA answers. The… See the full description on the dataset page: https://huggingface.co/datasets/zuzannad1/balanced-copa-explanations.cli-commands-explained
Overview
This dataset is a collection of 16,098 command line instructions sourced from Commandlinefu and Cheatsheets. It includes an array of commands, each with an id, title, description, date, url to source, author, votes, and flag indicating if the description is AI generated. The descriptions are primarily authored by the original contributors, for entries where descriptions were absent, they have been generated using NeuralBeagle14-7B. Out of the total entries, 10,039… See the full description on the dataset page: https://huggingface.co/datasets/b-mc2/cli-commands-explained.ExploreToM
PNYX/ExploreToM
This is a enriched version of the facebook/ExploreToM.
This version is designed to be executed with the lm-eval package using the A-VERT methodology.
It contains the same data as the original dataset, but with additional columns to facilitate systematic evaluation of reasoning across different orders of theory of mind.
Note: In the A-VERT repository can be found the task definition in yaml format to be used with lm-evaluation-harness.
New Columns… See the full description on the dataset page: https://huggingface.co/datasets/PNYX/ExploreToM.stage3-real-expansion-agent-teacher-separated-pilot
Teacher-Separated Expansion Agent Pilot
A 10-task inspection batch generated by Qwen3-235B-A22B-Instruct-2507 from real
CLAPNQ, PubMedQA, MAUD, ContractNLI, and FinQA source tasks.
The teacher-only trajectory-generation system prompt is recorded in
metadata/generation-manifest.json for auditability, but is absent from every
saved training trajectory. Each final messages list begins with the real
memory-wrapped task user message, followed by native assistant expand calls,
exact… See the full description on the dataset page: https://huggingface.co/datasets/leonli66/stage3-real-expansion-agent-teacher-separated-pilot.MMLU_ExpertPrompt_RAGThis dataset contains a copy of the cais/mmlu HF dataset but without the auxiliary_train split that takes a long time to generate again each time when loading multiple subsets of the dataset.
Please visit https://huggingface.co/datasets/cais/mmlu for more information on the MMLU dataset.
stage3-real-expansion-agent
Stage 3 Real-Source Expansion Agents — Pilot
This inspection pilot converts pinned training examples from real legal,
financial, biomedical, and grounded-QA corpora into native selective-expansion
traces. It is not the final-scale mixture.
Each row contains eight positional seg_i blocks. Every initial segment holds
512–896 words of real source material wrapped in
<|memory_start|>...<|memory_end|>. Qwen3-235B-A22B-Instruct-2507 receives a
native expand({"segment_id": "seg_i"})… See the full description on the dataset page: https://huggingface.co/datasets/leonli66/stage3-real-expansion-agent.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.markdown-table-expert
Markdown Table Expert
A large-scale dataset for teaching language models to read, understand, and reason over markdown tables. Contains 44,000 samples (40,000 train + 4,000 validation) spanning 35 real-world domains with detailed step-by-step reasoning traces.
Why This Dataset
Markdown tables are everywhere — in documentation, reports, READMEs, financial statements, and web content. Yet most LLMs struggle with structured tabular data, especially when asked to perform… See the full description on the dataset page: https://huggingface.co/datasets/cetusian/markdown-table-expert.code-expropriation-utilite-publique
Code de l'expropriation pour cause d'utilité publique, non-instruct (2025-09-20)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-expropriation-utilite-publique.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.tot-cwq-plan-sft-outputs34-rule-full-pw4-expand-labels-v2
ToT CWQ Plan SFT - outputs34_rule_full_pw4_expand_labels_v2
Merged SFT output from local run outputs34_rule_full_pw4_expand_labels_v2.
Version ID
local output dir: tot/sft/outputs34_rule_full_pw4_expand_labels_v2
file: cwq_train_plan.no_mid.jsonl
dataset: CWQ
grouping backend: TOT_REL_GROUPING_BACKEND=rules
parallel workers: 4
strict expand parity: enabled
nested expand labels: enabled
Main difference from earlier runs
This version renders nested Expand… See the full description on the dataset page: https://huggingface.co/datasets/YF0808/tot-cwq-plan-sft-outputs34-rule-full-pw4-expand-labels-v2.