datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Legal_Corpus_QA_SynDeepThink
🧠 Legal Corpus QA SynDeepThink Dataset
This repository contains a high-intelligence Legal Question-and-Answer dataset, generated through an advanced Iterative and Recursive Thinking process. It bridges the gap between static legal corpora and the dynamic "check-and-recheck" nature of human legal expertise. 🏛️
💡 The Concept: Iterative & Recursive Legal Logic
While standard synthetic datasets are often generated in a single pass, Legal_Corpus_QA_SynDeepThink mimics the… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/Legal_Corpus_QA_SynDeepThink.ID_Legal_QA_SynDeepThink
🧠 Indonesian Legal QA SynDeepThink Dataset
This repository hosts a specialized Indonesian Legal QA dataset that incorporates a Deep Thinking Phase. It is engineered for researchers and developers focusing on high-level judicial reasoning and complex regulatory analysis. 🏛️
💡 The Concept: Deep Thinking vs. Standard QA
While standard models often provide "System 1" (snap) judgments, the SynDeepThink approach simulates "System 2" (slow, deliberate) thinking. This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/ID_Legal_QA_SynDeepThink.ledger-long-context-KPI-QA
LEDGER — Long-Context KPI Question Answering & Page Retrieval
This dataset is part of the LEDGER (Long-context Evaluation of Documents for
Grounded Extraction and Retrieval) benchmark.
It supports two of the three LEDGER tasks:
Page-level KPI retrieval — given a natural-language question about a financial
KPI and the corresponding annual report, retrieve the relevant page(s). Each row
includes TREC-style graded relevance judgments (qrels) over all candidate pages.… See the full description on the dataset page: https://huggingface.co/datasets/artefactory/ledger-long-context-KPI-QA.ID_Legal_QA_SynThink
🧠 Indonesian Legal QA Synthetic Think Dataset (ID_Legal_QA_SynThink)
This repository features an advanced Synthetic Question-and-Answer dataset for the Indonesian legal domain, distinguished by the inclusion of an explicit Thinking Phase (Chain-of-Thought). 🏛️
💡 The Concept: Transparent Legal Reasoning
Standard QA datasets often provide just the "final answer." This dataset goes deeper by capturing the internal reasoning process of the model before it arrives at a… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/ID_Legal_QA_SynThink.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
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.turkce-sft-qa-3.7m
🇹🇷 Turkish SFT/QA — Birleştirilmiş ve Tekrarsız Veri Seti
3,723,264 örnek. 24 açık Türkçe SFT/QA veri setinin, satır düzeyinde
tekrar temizliği ve kalite kontrolünden geçirilmiş birleşimi. Her satır hangi veri
setinden geldiğini taşır.
English: A merged, row-level deduplicated and quality-filtered collection of
24 open Turkish SFT/QA datasets (3,723,264 examples). Every row carries
its source dataset, source URL and original license.
🙏 Teşekkür /… See the full description on the dataset page: https://huggingface.co/datasets/MercanAI/turkce-sft-qa-3.7m.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.ID_Legal_QA_Syn
🤖 Indonesian Legal QA Synthetic Dataset (ID_Legal_QA_Syn)
This repository contains a high-quality, synthetic Question-and-Answer dataset focused on Indonesian Law and Regulations. It was generated to bridge the gap between raw legal text and conversational AI requirements. 🏛️
💡 The Concept: Synthetic Legal Intelligence
Legal documents are often dense and difficult for general-purpose models to navigate. This dataset uses a Synthetic Data Generation (SDG) approach to… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/ID_Legal_QA_Syn.Target-QA
🎯 Target-QA: The First QA Dataset Benchmarking Target Priorization Based on DepMap
📑 Dataset Summary
Target-QA is derived from the DepMap multi-omics and CRISPR screening cohorts, harmonized via BioMedGraphica.It enables multi-modal reasoning by combining numeric evidence, topological knowledge and language context for CRISPR target prioritization.
This dataset supports the training and benchmarking of… See the full description on the dataset page: https://huggingface.co/datasets/FuhaiLiAiLab/Target-QA.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.RAG-Grounded-QA-188k
🎯 RAG Grounded QA 186K
The Anti-Hallucination Dataset
Teach language models to answer from context — or shut up trying.
Built by NovachronoAI — Precision AI for the real world.
Full Dataset (186K) · 20K Subset · Schema · Sources · Usage Guide
🧠 Why This Dataset Exists
Most QA datasets teach models what to say. This one also teaches them when to stay silent.
RAG (Retrieval-Augmented Generation) systems have a fatal flaw: the model hallucinates when… See the full description on the dataset page: https://huggingface.co/datasets/NovachronoAI/RAG-Grounded-QA-188k.arxiv-qa-thinking
ArXiv Q&A with Thinking Dataset
This dataset contains question-answer pairs generated by MiniMax-M2.1 based on academic articles from PursuitOfDataScience/arxiv-llama4-maverick-abstract.
Dataset Description
For each academic article, the model generates:
Thinking process: The model's reasoning wrapped in <think> tags
Question: An insightful question testing understanding of key concepts
Answer: A detailed answer based on the article content
Statistics… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/arxiv-qa-thinking.Uganda-Multilingual-QA
BUAIR Uganda Multilingual Q&A
Parallel question–answer dataset for Ugandan languages, curated by the BUAIR Voice initiative at Busitema University.
Dataset version: v2 (updated 2026-08-31)
Each language config contains the same 4,256 agriculture / rural-livelihood Q&A pairs, with English as the shared source and translations into Japadhola, Ateso, Runyankore, and Luganda.
Changelog (v2)
Replaced v1 data (5,000 pairs from Multiligual-QA.xlsx) with cleaned data… See the full description on the dataset page: https://huggingface.co/datasets/BUAIR/Uganda-Multilingual-QA.llama2_QA_Economics_230915
Dataset Card for "llama2_QA_Economics_230915"
More Information needed
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.Bhagavad-Gita-QA
Bhagavad-Gita-QA-Multilingual
Dataset Summary
Bhagavad-Gita-QA, is a carefully structured verse-aligned dataset that brings the timeless wisdom of the Bhagavad Gita into a modern question–answer framework.
This is the first open dataset that provides verse-level Q&A for the Gita with questions in Hindi and Gujarati along with English. This is not just a technical resource but also a cultural bridge, enabling new ways of studying, teaching, and exploring the Gita… See the full description on the dataset page: https://huggingface.co/datasets/JDhruv14/Bhagavad-Gita-QA.Timeseries-QA
Timeseries-QA
This is a dataset for Timeseries Instruction Tuning.
It was created using the following steps:
Extracted features from time series data in AutonLab/Timeseries-PILE
microsoft/Phi-3-medium-4k-instruct generated the QA pairs
Timeseries Instruction Tuning用のデータセットです。
以下の手順で作成しました。
AutonLab/Timeseries-PILE の時系列データの特徴を抽出
microsoft/Phi-3-medium-4k-instruct がQAを作成
Dataset Details
Dataset Description
Curated by: HachiMLLanguage(s) (NLP): English… See the full description on the dataset page: https://huggingface.co/datasets/HachiML/Timeseries-QA.Wikipedia_RAG_QA_Classification
🏛️ Wikipedia RAG QA Dataset for Retrieval-Augmented Generation Training
📊 Dataset Description
This dataset contains 300,000+ validated model-generated responses to Wikipedia content, specifically designed for Retrieval-Augmented Generation (RAG) applications and SQL database insertion tasks. Generated by Jeeney AI Reloaded 207M GPT with specialized RAG tuning.
🖥️ Demo Interface: Discord
Live Chat Demo on Discord: https://discord.gg/Xe9tHFCS9h
The full CJ… See the full description on the dataset page: https://huggingface.co/datasets/CJJones/Wikipedia_RAG_QA_Classification.stackexchange-space-qa
Stack Exchange Space Q&A
Credit: NASA/DOE/Fermi LAT Collaboration
Part of a dataset collection on Hugging Face.
Dataset description
This dataset is a clean, tabular Q&A corpus of space and astronomy knowledge, derived from two Stack Exchange community Q&A sites: Astronomy Stack Exchange (astronomy.stackexchange.com) and Space Exploration Stack Exchange (space.stackexchange.com). Each row is one question paired with its best answer — either the question's… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/stackexchange-space-qa.code2lora-data-qa
Code2LoRA question-answering dataset (qa task)
LLM-generated question/answer pairs grounded in each training repository's
commit history. This is the qa-task companion to the Code2LoRA-GRU commit
dataset: every row is keyed by (repo_id, commit_sha) and carries the commit's
in_repo_split and cross_repo_split labels, so it lines up 1:1 with the GRU
v2 commit walk and trains alongside the assert_rhs task.
164,173 unique pairs over 9,188 commits from 500 training repositories,
in… See the full description on the dataset page: https://huggingface.co/datasets/code2lora/code2lora-data-qa.DBNL-public-qa-english-translationwikipedia_multiple_choice_qa
Galician and Portuguese Multiple-Choice QA Instruction Subsets
Dataset description
This dataset contains two instruction-tuning subsets for multiple-choice question answering in Galician and Portuguese:
gl_wikipedia_multiple_choice_qa (1,486 instances)
pt_wikipedia_multiple_choice_qa (547 instances)
Both subsets are reformatted versions of QA data originally included in the cpt_instruction_datasets collection, adapted here as standalone instruction-style datasets.
Each… See the full description on the dataset page: https://huggingface.co/datasets/proxectonos/wikipedia_multiple_choice_qa.welfare-qa
WelfareQA
The first cross-country welfare-AI evaluation dataset for India, Nigeria, and Kenya. 1,500 ground-truth rows written from scratch against named government sources, expanded to 9,385 multilingual variants. Verified to April 2026.
A population of roughly 1.65 billion people — about one in five humans alive today — lives under the welfare apparatuses encoded here. PM-KISAN. Ayushman Bharat. Inua Jamii. NHIA. SHA. The deployment surface is enormous. The evaluation surface… See the full description on the dataset page: https://huggingface.co/datasets/saxenan3/welfare-qa.fatwa-qa-evaluation
Fatwa QA Evaluation Dataset
Dataset Description
This dataset contains Islamic finance and jurisprudence fatwa question-answer pairs for evaluating Arabic language models. This is an open-ended QA evaluation benchmark where models generate free-form answers.
Dataset Statistics
Total Samples: 2,000
Average Question Length: 243.9 characters
Average Answer Length: 492.3 characters
Dataset Structure
Data Fields
id: Unique… See the full description on the dataset page: https://huggingface.co/datasets/SahmBenchmark/fatwa-qa-evaluation.stackpulse_qa_output
🧩 StackPulse-QA: Instruction-Tuning Q&A Pairs from Stack Overflow
Dataset Summary
Instruction-tuning Q&A dataset built from Omarrran/StackPulse_778K_QnA_Code_dataset by joining question IDs with BigQuery bigquery-public-data.stackoverflow.posts_answers on accepted_answer_id.
Each sample consists of:
input_text_instruct — A question (title + body) prefixed with an instruction
output_text — The accepted answer from Stack Overflow
Format mirrors the… See the full description on the dataset page: https://huggingface.co/datasets/Omarrran/stackpulse_qa_output.rag-qa-arena
RAG QA Arena Annotated Dataset
A comprehensive multi-domain question-answering dataset with citation annotations designed for evaluating Retrieval-Augmented Generation (RAG) systems, featuring faithful answers with proper source attribution across 6 specialized domains.
🎯 Dataset Overview
This annotated version of the RAG QA Arena dataset includes citation information and gold document IDs, making it ideal for evaluating not just answer accuracy but also answer grounding… See the full description on the dataset page: https://huggingface.co/datasets/rajistics/rag-qa-arena.envoy-qasper-code-trajectories
Envoy QASPER Code-Execution Trajectory Pilot
This is a small, fully disclosed pilot of executable research-agent trajectories.
Claude Sonnet 5 generated Python actions against a persistent document REPL. The
Envoy pipeline executed every action and retained the real observations. An AI
coding assistant then reviewed answer support, stopping behavior, and replay.
This release is useful for studying trajectory validation and citation failures.
It is not a production-ready SFT… See the full description on the dataset page: https://huggingface.co/datasets/jasonlingg/envoy-qasper-code-trajectories.physics-qa
Physics Q&A — Multi-Level Explanations
502 question-answer pairs generated from recent physics papers (arXiv 2024–2026),
covering 5 subfields across 83 papers.
Each paper is explained at 6 audience levels, each as a focused Q/A pair:
Level
Audience
physicist
Expert with equations and notation
undergrad_science
Undergraduate with key equations
high_schooler
High school student, intuitive
humanities_student
No math, analogies only
five_year_old
Child-friendly… See the full description on the dataset page: https://huggingface.co/datasets/planetoid-reader/physics-qa.