datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nuclear-intelligence-dataset
Nuclear Intelligence Dataset
Public, auto-generated dataset of validated nuclear-energy research cycles.
Latest stats (auto-updated):
🪙 NES tokens minted: 0
⛓️ Blockchain length: 1 blocks
🕸️ Knowledge entities: 2
Source
GitHub: https://github.com/QalamHipHop/nuclear-intelligence
HF Space: https://huggingface.co/spaces/Qalam/Nuclear-Intelligence
License
MIT
SWE-QA-Benchmark
SWE-QA Benchmark
A comprehensive benchmark dataset for Software Engineering Question Answering, containing 720 questions across 15 popular Python repositories.
Dataset Summary
Total Questions: 720
Repositories: 15
Format: JSONL (JSON Lines)
Fields: question, answer
Repository Coverage
Each repository contains 48 questions:
astropy
conan
django
flask
matplotlib
pylint
pytest
reflex
requests
scikit-learn
sphinx
sqlfluff
streamlink
sympy
xarray… See the full description on the dataset page: https://huggingface.co/datasets/swe-qa/SWE-QA-Benchmark.huatuo_encyclopedia_qa
Dataset Card for Huatuo_encyclopedia_qa
Dataset Summary
This dataset has a total of 364,420 pieces of medical QA data, some of which have multiple questions in different ways. We extract medical QA pairs from plain texts (e.g., medical encyclopedias and medical articles). We collected 8,699 encyclopedia entries for diseases and 2,736 encyclopedia entries for medicines on Chinese Wikipedia. Moreover, we crawled 226,432 high-quality medical articles from the Qianwen Health… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa.uscode_qacA compact question-answer set for the Prime Intellect U.S. legal evaluation environment. Each record pairs a natural-language question with an extractive answer and the source statute snippet drawn from the Cornell Law School Legal Information Institute (LII) U.S. Code site. Fields also include title_id, section_id, and section_url to support retrieval-style evaluations; the snippet lives in context and is used to build the search index rather than being passed directly to the model.… See the full description on the dataset page: https://huggingface.co/datasets/emre570/uscode_qac.mimic-medical-imaging-qa
MIMIC Medical Imaging QA Dataset
5,207 Bloom's-taxonomy-stratified question--answer pairs derived from 23 medical imaging lectures (RPI BMED 2300). The dataset supports the paper "MIMIC: A Course-Derivation Pipeline and Benchmark for Slide-Anchored Tutoring with a Domain-Adapted Large Language Model" and was used to fine-tune MIMIC-LM, a domain-adapted Llama-3.1-8B-Instruct model for grounded medical imaging instruction.
License
The benchmark annotations, dataset… See the full description on the dataset page: https://huggingface.co/datasets/zabir1996/mimic-medical-imaging-qa.open-australian-legal-qa
Open Australian Legal QA ⚖️
Open Australian Legal QA by Isaacus is the first open dataset of Australian legal questions and answers.
Comprised of 2,124 questions and answers synthesised by gpt-4 from the Open Australian Legal Corpus, the largest open database of Australian law, the dataset is intended to facilitate the development of legal AI assistants in Australia.
To ensure its accessibility to as wide an audience as possible, the dataset is distributed under the same licence… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/open-australian-legal-qa.earnings-calls-qa
Lamini Earning Calls QA Dataset
Description
This dataset contains transcripts of earning calls for various companies, along with questions and answers related to the companies' financial performance and other relevant topics.
Format
The transcripts, questions, and answers are in the form of jsonlines files, with each json object in the file containing the transcript of an earning call for a single company.
Data Pipeline Code
The entire data pipeline… See the full description on the dataset page: https://huggingface.co/datasets/lamini/earnings-calls-qa.QAlg
QAlg Humanize-Physic Formalizations and Proofs
QAlg (Quantum Algorithms) is a blind benchmark for formalizing theorems in quantum algorithms. It evaluates whether an AI agent can faithfully translate natural-language and TeX problem statements into Lean 4 theorems and then construct formal proofs checked by the Lean kernel. Its 36 tasks cover quantum circuits, linear algebra, the quantum Fourier transform, Hamiltonian simulation, hidden subgroups, QSP/QSVT, and parameterized… See the full description on the dataset page: https://huggingface.co/datasets/humanfia-lab/QAlg.huatuo_consultation_qa
Dataset Card for huatuo_consultation_qa
Dataset Summary
We collected data from a website for medical consultation , consisting of many online consultation records by medical experts. Each record is a QA pair: a patient raises a question and a medical doctor answers the question. The basic information of doctors (including name, hospital organization, and department) was recorded.
We directly crawl patient’s questions and doctor’s answers as QA pairs, getting 32,708,346… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/huatuo_consultation_qa.huatuo_knowledge_graph_qa
Dataset Card for Huatuo_knowledge_graph_qa
Dataset Summary
We built this QA dataset based on the medical knowledge map, with a total of 798,444 pieces of data, in which the questions are constructed by means of templates, and the answers are the contents of the entries in the knowledge map.
Dataset Creation
Source Data… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa.ameli-assurance-maladie-qa
Ameli Assurance Maladie - Question Answering Dataset
Description
Dataset de 100 paires question-réponse (QA) basé sur les publications officielles de l'Assurance Maladie française, extraites du site assurance-maladie.ameli.fr.
Conçu pour évaluer des systèmes de RAG (Retrieval-Augmented Generation) sur des documents institutionnels français dans le domaine de la santé publique et de la protection sociale.
Format du dataset
{
"question": "Quel article du… See the full description on the dataset page: https://huggingface.co/datasets/alianassmaaa/ameli-assurance-maladie-qa.webglm-qa
WebGLM-QA
Dataset Description
WebGLM-QA is the dataset used to train the WebGLM generator module. It consists of 43,579 high-quality data samples for the train split, 1,000 for the validation split, and 400 for the test split. Refer to our paper for the data construction details.
Dataset Structure
To load the dataset, you can try the following code.
from datasets import load_dataset
load_dataset("THUDM/webglm-qa")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/webglm-qa.TinyBooks-QA-Chinese本数据集已停止更新，请移步https://huggingface.co/datasets/fzmnm/TinyStoriesAdv-zh
TinyBooks-QA-Chinese
Inspired by the (TinyStories)[https://arxiv.org/abs/2305.07759] paper, where a small language model exhibits strong capabilities when trained on high-quality, 🍼baby-friendly stories synthesized by AI, I present an AI-generated Encyclopedia suitable for kindergarten and grade school levels.
This AI-synthesized dataset converts classical literature into a question-answer style curriculum with… See the full description on the dataset page: https://huggingface.co/datasets/fzmnm/TinyBooks-QA-Chinese.JavaError-QA
JErrRAG-Eval-800
JErrRAG-Eval-800 is the public benchmark release aligned with the paper's final canonical dataset and non-anonymous archival record.
This Hugging Face repository contains:
java_error_qa_v2/: the canonical public benchmark package
paper_online_artifacts/: the paper-facing supplementary artifacts and reproduction bundles
SHA256SUMS.txt: release-side hash anchors referenced by the paper
Dataset Summary
Total records: 800
Split sizes: train=639… See the full description on the dataset page: https://huggingface.co/datasets/HTJ008/JavaError-QA.pao-instruction-qa-conversation-datasetPa'O Instruction, QA & Conversation Dataset
An open and community-driven dataset for the Pa'O ("blk") language, developed through the RYPAK Ecosystem, SuccessImprove (SI), and Pa'O Digital Hub.
The dataset is designed to support natural language processing (NLP), large language models (LLMs), conversational dialogue, instruction following, language technology research, and digital preservation of the Pa'O language.
The project focuses on building a free, open, reusable, and continuously… See the full description on the dataset page: https://huggingface.co/datasets/paodigitalhub/pao-instruction-qa-conversation-dataset.dnd5e-srd-qa
D&D 5.2.1 SRD RAG Evaluation Dataset
A high-quality Question-Answering (QA) dataset built by the Datapizza AI Lab from the Dungeons & Dragons 5th Edition System Reference Document (SRD) version 5.2.1, designed to evaluate Retrieval Augmented Generation (RAG) systems.
Dataset Summary
This dataset contains 56 question-answer pairs across two difficulty tiers (Easy and Medium), each designed to test different aspects of RAG system capabilities. The dataset is built from 20… See the full description on the dataset page: https://huggingface.co/datasets/datapizza-ai-lab/dnd5e-srd-qa.science-qa-sft-100k
Science QA SFT (100K)
100,000 science Q&A examples with step-by-step explanations for SFT fine-tuning. Covers physics, chemistry, biology, astronomy, and earth science at beginner through advanced difficulty.
Motivation
Models trained on general text often give superficially plausible but mechanistically wrong answers to science questions — stating the right conclusion without understanding the underlying reasoning. This dataset trains models to explain why an… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/science-qa-sft-100k.Nemotron-RL-QA-Abstention-v1Nemotron-RL-QA-Abstention-v1
License: cc-by-4.0
Language: en
Task Categories: reinforcement-learning, question-answering, text-generation
Tags: abstention, question-answering, hotpotqa, software-engineering, health, law, rl, rlvr
Configs: default train split at data/train.jsonl
Domain: multi-domain question answering, abstention
Modality: text
Capability Breakdown: Abstention-aware factoid question answering [100%]
Source: Hybrid: Automated, Manually Collected, Synthetic
Size Bin: <10K… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-QA-Abstention-v1.bangladesh-legal-qa-dataset
Bangladesh Legal QA Dataset: Bangla-English Law and Fine-Tuning
The Bangladesh Legal QA Dataset is a bilingual Bangla-English dataset for
Bangladesh law question answering, legal NLP, LLM fine-tuning, instruction
tuning, and retrieval-augmented generation (RAG). It provides 2,165
context-grounded legal QA records, direct-answer and IRAC chat-format training
data, and structured statutory text from six Bangladesh Acts and three
schedules.
This is the 2,165-record paper-aligned… See the full description on the dataset page: https://huggingface.co/datasets/momahadi/bangladesh-legal-qa-dataset.duplex-qa-refusal
duplex-qa-refusal
No dialogue in this set has been validated by a human.
Text-side augmentation of the moshika spoken-QA corpus so a full-duplex speech model can be trained to refuse a query when a mid-conversation text instruction tells it to, voice the reason the instruction gives, and then carry on normally. Two classes: policy (an existing benign query is declined for a stated reason; comes with an untouched accept twin sharing pair_id) and attack (a new user turn pivots to… See the full description on the dataset page: https://huggingface.co/datasets/MagicLuke/duplex-qa-refusal.us-tax-law-qa
US Tax Law Q&A Dataset
A synthetic dataset of U.S. federal tax law questions and answers with IRC citation grounding, designed for fine-tuning language models on tax reasoning tasks.
Dataset Structure
Split
Examples
train
3,500
test
500
Fields
Field
Type
Description
id
string
Unique example identifier
category
string
Tax law category (international, estate_gift, business_entity, individual, procedure, specialized)
subcategory… See the full description on the dataset page: https://huggingface.co/datasets/DJLougen/us-tax-law-qa.LaMP-QA
LaMP-QA Dataset
Abstract
Personalization is essential for question answering systems that are user-centric. Despite its importance, personalization in answer generation has been relatively underexplored. This is mainly due to lack of resources for training and evaluating personalized question answering systems. We address this gap by introducing LaMP-QA---a benchmark designed for evaluating personalized long-form answer generation. The benchmark covers questions from… See the full description on the dataset page: https://huggingface.co/datasets/alireza7/LaMP-QA.GammaCorpus-Fact-QA-450k
GammaCorpus: Fact QA 450k
What is it?
GammaCorpus Fact QA 450k is a dataset that consists of 450,000 fact-based question-and-answer pairs designed for training AI models on factual knowledge retrieval and question-answering tasks.
Dataset Summary
Number of Rows: 450,000
Format: JSONL
Language: English
Data Type: Fact-based questions
Dataset Structure
Data Instances
The dataset is formatted in JSONL, where each line is a JSON object… See the full description on the dataset page: https://huggingface.co/datasets/rubenroy/GammaCorpus-Fact-QA-450k.turkish-qa-multi-dialog-dataset
Turkish QA & Multi-Dialog Dataset
Bu depo, iki farklı Türkçe veri kaynağının birleştirilmiş ve temizlenmiş sürümünü içerir:
Yaklaşık 19.000 adet soru-cevap (QA) örneği
Çok adımlı, doğal Türkçe sohbetlerden oluşan diyalog verileri
Bu dataset, hem genel amaçlı Türkçe QA modelleri hem de sohbet/chatbot modelleri için uygundur.
Veri İçeriği
QA Bölümü (~19K)
SQuAD benzeri yapıdan dönüştürülmüş input–output örnekleri
Her satır: tek bir soru ve net bir cevap… See the full description on the dataset page: https://huggingface.co/datasets/sixfingerdev/turkish-qa-multi-dialog-dataset.Apertus-v1.5-QAT-10K
mlx-community/Apertus-v1.5-QAT-10K
This is a 2000 sample subset of the chosen pairs inside swiss-ai/Apertus_v1p5_Preference_Data for MLX-LM-LoRA and MLX-LoRA-Studio and the Quantization Aware Trained Appertus models.
PortBench-QA
PortBench QA Dataset
Dataset Description
6,269 structured question-answer pairs probing correlation-based financial reasoning for multi-asset portfolio management, generated from the PortBench Market Base Dataset.
Task Templates
Template
Task
Complexity
Pairs
T1
Return prediction — direction for next N days
1 (single asset)
1,000
T2
Risk assessment — VaR at given confidence level
1
1,000
T3
Position sizing — given max drawdown… See the full description on the dataset page: https://huggingface.co/datasets/AgenticFinLab/PortBench-QA.russian_dpo_qa
Format
Each row contains:
prompt
chosen
rejected
Usage
from datasets import load_dataset
dataset = load_dataset("eridai/russian_dpo_qa")
train = dataset["train"]
imabari_wiki_qa_v4_validated_w_reasoning_effort_qwen38
Imabari Wiki QA v4 Validated with Reasoning Effort — Qwen3.8
概要 / Overview
日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。
This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is… See the full description on the dataset page: https://huggingface.co/datasets/ikedachin/imabari_wiki_qa_v4_validated_w_reasoning_effort_qwen38.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.unreal-engine-5.7-qa
Unreal Engine 5.7 Instruction-Tuning Dataset
Dataset Description
This dataset contains 122,199 high-quality, synthetic Question and Answer pairs specifically designed for instruction-tuning Large Language Models (LLMs) to become expert coding and architectural assistants for Unreal Engine 5.7.
Because Unreal Engine frequently deprecates older APIs (from UE4 to UE5) and introduces massive paradigm shifts (like Nanite, Lumen, and World Partition), standard… See the full description on the dataset page: https://huggingface.co/datasets/TunstallTensor/unreal-engine-5.7-qa.