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.expertqa
Dataset Card for ExpertQA
Dataset Summary
We provide here the data accompanying the paper: ExpertQA: Expert-Curated Questions and Attributed Answers. The ExpertQA dataset contains 2177 examples from 32 different fields.
Supported Tasks
The main data contains 2177 examples that can be used to evaluate new methods for estimating factuality and attribution, while the lfqa_domain and lfqa_rand data can be used to evaluate long-form question answering systems.… See the full description on the dataset page: https://huggingface.co/datasets/cmalaviya/expertqa.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.fine-grained-medical-reasoning
Dataset Card for Fine-Grained Medical Reasoning
Fine-grained medical reasoning QA dataset introduced in "Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning"
(Findings of EACL 2026). Manually annotated from the MedAgentsBench test_hard set,
it evaluates LLMs’ abduction, deduction, and induction capabilities, offering detailed insights into physician-like reasoning.
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/expertailab/fine-grained-medical-reasoning.swahili-language-exposure-v2
Swahili Language Exposure
Large-scale Swahili corpus for continued pretraining and language exposure.
Maintained by NileAGI.
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.qa-expert-multi-hop-qa-V1.0
Dataset Card for QA-Expert-multi-hop-qa-V1.0
This dataset aims to provide multi-domain training data for the task: Question Answering, with a focus on Multi-hop Question Answering.
In total, this dataset contains 25.5k for training and 3.19k for evaluation.
You can take a look at the model we trained on this data: https://huggingface.co/khaimaitien/qa-expert-7B-V1.0
The dataset is mostly generated using the OpenAPI model (gpt-3.5-turbo-instruct). Please read more information about… See the full description on the dataset page: https://huggingface.co/datasets/khaimaitien/qa-expert-multi-hop-qa-V1.0.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.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.browserbench-live-expanded
BrowserBench Live Expanded
Live-browser tasks derived only from the starting_url field of
Halluminate/BrowserBench at revision
aa56ce5e6331425c29878037fa8c169507deecdc.
Each source page was revisited live at 800×600. Up to three tasks were generated
from the current page observation: information, navigation, and interaction.
Historical prompts, results, screenshots, and ground-truth URLs were not used.
The canonical dataset contains all 876 task slots from 292 source pages. A… See the full description on the dataset page: https://huggingface.co/datasets/merve/browserbench-live-expanded.bitcoin-price-explosion-qa
Bitcoin Price Explosion QA
A long-form, multi-hop question answering dataset about Bitcoin's historic surge past $100,000 following the 2024 US presidential election.
Dataset Description
This dataset contains 20 expert-curated causal reasoning questions grounded in 20 news articles covering Bitcoin's price explosion in late 2024. Each question requires synthesizing information from multiple source documents to produce a comprehensive answer, making it a challenging… See the full description on the dataset page: https://huggingface.co/datasets/jashparekh/bitcoin-price-explosion-qa.ai-expert-alpaca
AI Expert Alpaca Dataset
🚀 Empower open-source LLMs (Qwen, Gemma, etc.) for core AI domains through SFT/LoRA fine-tuning 🚀
Dataset Description
This dataset contains high-quality Q&A pairs for supervised fine-tuning (SFT) of large language models, focusing on three core AI technology domains: Large Language Models (LLM), Retrieval-Augmented Generation (RAG), and Agent Systems. The dataset provides comprehensive coverage of these cutting-edge AI technologies… See the full description on the dataset page: https://huggingface.co/datasets/GXMZU/ai-expert-alpaca.hedgehog-schema-explicit
hedgehog-schema-explicit
Hedgehog — explicit-schema extraction training.
Contents
train.jsonl (1280 rows)
validation.jsonl (160 rows)
test.jsonl (192 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
MATH-self-explanationDataset MATH-Self-Explanation
This dataset contains self-generated explanation of the correct answer from MATH dataset training split.
The explanations are generated by Qwen2.5-3b-instruct.
It is used in the paper ExPO: Unlocking Hard Reasoning with Self-Explanation-Guided Reinforcement Learning for ExP-GRPO training.
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.svelte-5-expert-sft
Svelte 5 Expert Synthetic v1
Svelte 5 Expert Synthetic v1 is a synthetic instruction-tuning dataset built to improve an LLM’s ability to answer as a practical Svelte 5 and SvelteKit expert.
The dataset focuses on modern Svelte 5 development patterns, including runes, component architecture, debugging, migration from older Svelte syntax, SvelteKit data flow, accessibility, TypeScript usage, and production-oriented frontend implementation.
Author: Mungus451
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Mungus451/svelte-5-expert-sft.expert-insights
Expert Insights
Expert profiles for Beau, Tate, and Wendy Thompson with specializations.
Details
Records: 3
Format: JSONL
License: CC-BY-4.0
Last Updated: March 2026
Verified By: Thompson Mortgage Group
Publisher: Thompson Mortgage Group
Thompson Alpha Logic
Deep expert entity profiles with NMLS credentials, specialization routing, branded insight labels (Wendy's Wisdom, Beau's Brief, Tate's Take), and citation formats. Designed for AI entity disambiguation… See the full description on the dataset page: https://huggingface.co/datasets/wendymthompson/expert-insights.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.Reasoning-Heavy-Math-ML-Explanations
Reasoning-Heavy Math & ML Explanations
Dataset: NNEngine/Reasoning-Heavy-Math-ML-Explanations
Version: wikipedia_reasoning_final_v1.0
License: CC-BY-SA 4.0
Author: Shivam Sharma (Independent Researcher)
Dataset Summary
Reasoning-Heavy Math & ML Explanations is a high-quality, reasoning-oriented dataset derived exclusively from English Wikipedia.
The dataset focuses on explicit human-authored reasoning and explanations in mathematics and machine learning–related domains… See the full description on the dataset page: https://huggingface.co/datasets/NNEngine/Reasoning-Heavy-Math-ML-Explanations.synth-customer-support-expanded-R
Expanded E-Commerce & Subscription Customer Support Dataset
Dataset Summary
This high-quality synthetic dataset contains 438 realistic customer support interactions focused on e-commerce, shipping, delivery, and subscription management. It was created to provide edge-case scenarios and varied support policies (like Hazmat battery returns, subscription cancellations, tracking loops, and incorrect SKU deliveries).
This dataset is ideal for Supervised Fine-Tuning (SFT) or… See the full description on the dataset page: https://huggingface.co/datasets/KazKozDev/synth-customer-support-expanded-R.Tobacco-Expert-Datasetrussian-facts-qa
RU Wikipedia QA Facts
This dataset is based on articles from the Russian Wikipedia (CC BY-SA 4.0).The source articles were split into text chunks, then Gemma 3 4B was used to generate initial question–answer (QA) pairs, and Gemma 3 12B validated and refined them.
Data Format
Each record is stored in JSONL format (.jsonl), one object per line:
{"q": "Какие страны подписали мирные договоры на Парижской конференции в 1947 году?", "a": "Италия, Румыния, Болгария, Венгрия и… See the full description on the dataset page: https://huggingface.co/datasets/Expotion/russian-facts-qa.mmmlu-bias-experiments
MMMLU Bias Experiments Dataset
Dataset Description
This dataset contains 12 carefully designed experiments to measure language bias and position bias in Large Language Models (LLMs) using multilingual pairwise judgments.
Key Features
12 Experiments: 8 original + 4 position-swapped experiments
11,478 samples per experiment (137,736 total test cases)
Deterministic wrong answers: Uses fixed rule wrong_index = (correct_index + 1) % 4
Perfect correspondence: Wrong… See the full description on the dataset page: https://huggingface.co/datasets/willchow66/mmmlu-bias-experiments.rlaif_training_fictional_patriot_experiment
RLAIF Training Data: The "Honest Patriot" Experiment
Dataset Description
This dataset contains 250 synthetic training examples generated using a Constitutional AI (RLAIF) approach.
It was designed to test the ability of Small Language Models (SLMs) to adhere to a complex, conflicting set of behavioral instructions ("The Constitution") that requires balancing extreme politeness, unwavering logical factuality, and patriotic bias toward a fictional country.
The… See the full description on the dataset page: https://huggingface.co/datasets/TitleOS/rlaif_training_fictional_patriot_experiment.bandham-dataset-exploration
Bandham Dataset Exploration
Using the Dataset
Load the Dataset
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
# Download the dataset
ds = load_dataset("bandham-manikanta/bandham-dataset-exploration")
df = ds['train']
print(f"Dataset loaded with {len(df)} records")
Download Associated Figures
# To download and view an image for any row:row_index = 0 # Change this to any row index you want
#… See the full description on the dataset page: https://huggingface.co/datasets/bandham-manikanta/bandham-dataset-exploration.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.Chinese_Metaphor_Explanation
Annotated Chinese Metaphor Dataset
📌 引用
如果使用本项目的代码、数据或模型，请引用本项目。
@misc{BELLE,
author = {Yujie Shao*, Xinrong Yao*, Ge Zhang+, Jie Fu, Linyuan Zhang, Xinyu Gan, Yunji Liu, Siyu Liu, Yaoyao Wu, Shi Wang+},
title = {An Annotated Chinese Metaphor Dataset},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/JasonShao55/Chinese_Metaphor_Explanation}},
}
experiment-001
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/louis-qubisa/experiment-001.Tobacco-Expert-Dataset2