datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hacker-news
Hacker News - Complete Archive
Every Hacker News item since 2006, live-updated every 5 minutes
What is it?
This dataset contains the complete Hacker News archive: every story, comment, Ask HN, Show HN, job posting, and poll ever submitted to the site. Hacker News is one of the longest-running and most influential technology communities on the internet, operated by Y Combinator since 2007. It has become the de facto gathering place for founders, engineers, researchers… See the full description on the dataset page: https://huggingface.co/datasets/naveenreddie-18/hacker-news.nadora-global-industries
NADORA Global Industries
A synthetic multinational, built to be developed against rather than
demonstrated with.
One fictional company — $5.20bn revenue, $716m EBITDA, 24,000 employees, 18
countries, 35 legal entities, five business units — traded daily from January
2022 to December 2026 and rendered at six fidelities, from a 3 MB unit-test
fixture to a 5 GB full-scale corpus.
38,964,663 rows · 11 GB · 2,319 verification assertions, all passing.
100% synthetic. No real company… See the full description on the dataset page: https://huggingface.co/datasets/hemanthreddy901/nadora-global-industries.motif-qa
MotifQA
Dataset Summary
MotifQA is a synthetic graph question-answering benchmark focused on detecting graph motifs inside small random graphs.
Each example pairs a textual prompt with an answer sentence, a list of nodes highlighted as the motif (when present), and an explicit graph description(nodes and edges).
In this QA dataset, all graphs are homogenous and undirected.
Subsets cover both yes/no motif detection, motif-type classification (house vs 5-cycle), and… See the full description on the dataset page: https://huggingface.co/datasets/naos-ku/motif-qa.NautData
NautData
Paper | Project Page | Code
NautData is a large-scale underwater instruction-following dataset containing 1.45 million image-text pairs. It was constructed to bridge the gap in large-scale underwater multi-task instruction-tuning datasets, which are crucial for advancing underwater scene understanding methods. The dataset enables the development and thorough evaluation of underwater Large Multimodal Models (LMMs).
This dataset was introduced in the paper NAUTILUS: A Large… See the full description on the dataset page: https://huggingface.co/datasets/H-EmbodVis/NautData.ClawBench
ClawBench — A Benchmark for AI Web Agents
Can AI Agents Complete Everyday Online Tasks?
|💻 Github | 🏆 Leaderboard | 📖 Paper | 🌐 Website |
ClawBench is an open benchmark for AI web agents — the systems that drive a real browser to complete a user's task end-to-end. It scores agents on real, everyday online tasks (booking flights, ordering groceries, submitting job applications) across live websites. The corpus ships in two slices: V1 — 153 tasks across 144 websites (the original… See the full description on the dataset page: https://huggingface.co/datasets/NAIL-Group/ClawBench.narrativeqa-rag
NarrativeQA RAG
Dataset for Retrieval-Augmented Generation (RAG) based on NarrativeQA.
Structure
Subset
Splits
Description
corpus
train (default)
Wikipedia plot summaries shared across all query splits
queries
train, dev, test
Reading comprehension questions
qrels
train, dev, test
Relevance judgments (query ↔ document)
answers
train, dev, test
Reference answers (longest annotated answer)
Dataset statistics
Split
Queries… See the full description on the dataset page: https://huggingface.co/datasets/DinoStackAI/narrativeqa-rag.gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/natong19/gpqa.taiwan-national-exams-resultsBenchmark results produced by any-to-bench. One subset here is one taker
configuration — a single model at a single reasoning effort — sat against the
exams in another dataset repo. Every row names the exam repo and subset it was
earned against, so results from several corpora, and from several people, can
live side by side.
results-index.json — the catalog: one headline row per configuration
results-<entry>/entry.json — that configuration's per-paper scores
results-<entry>/raw/<subset>/ —… See the full description on the dataset page: https://huggingface.co/datasets/JacobLinCool/taiwan-national-exams-results.kobbq
Dataset Card for KoBBQ
The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts other than the US because social biases depend heavily on the cultural context. In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework that addresses considerations for cultural adaptation of a dataset. Our framework includes… See the full description on the dataset page: https://huggingface.co/datasets/naver-ai/kobbq.Nayose-Bench-QA
Dataset Card for Nayose-Bench-Instruction
This dataset was created as a benchmark for the entity resolution task in the pharmaceutical domain.
Dataset Details
This dataset is designed for the entity resolution task in the pharmaceutical domain.
The entity resolution task refers to a paraphrasing task, such as rephrasing drug names, converting chemical substances into brand names, or rewriting chemical substances into chemical formulas.
Uses
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/EQUES/Nayose-Bench-QA.nanobubbleeval
NanoBubbleEval v1.0
⚠ For NeurIPS reviewers — use this Croissant URL
Please do NOT use the URL exposed by the "Use this dataset → Croissant"
button at the top-right of this page. That URL triggers a known bug in
mlcroissant==1.0.16 (the version pinned by the
NeurIPS Croissant validator Space)
and produces a FilterFiles error that does not reflect a problem with the
dataset itself.
Use this URL instead — copy the line below verbatim into the validator's
"URL Input" tab:… See the full description on the dataset page: https://huggingface.co/datasets/EliasHossain/nanobubbleeval.nasa-science-repos-sme-benchmark
NASA Science Repos SME Benchmark
A benchmark dataset for evaluating retrieval systems on NASA science repository discovery tasks. This dataset contains expert queries, a corpus of NASA science GitHub repositories, and relevance judgments.
Dataset Structure
Files
├── corpus.jsonl # 5,264 repositories with full metadata
├── queries.jsonl # 219 expert queries
└── qrels/
├── earth.tsv # Earth Science relevance judgments (162)
├──… See the full description on the dataset page: https://huggingface.co/datasets/nasa-impact/nasa-science-repos-sme-benchmark.CodeGen-Diverse-5K
CodeGen-Diverse-5K: Broad Coverage for Competitive Programming
Part of the CodeGen suite | CodeGen-Deep-5K (sister dataset)
Dataset Description
CodeGen-Diverse-5K is a broad coverage dataset designed for training code generation models across a wide variety of competitive programming problems. This dataset prioritizes problem diversity over solution diversity, covering 5,000 unique problems with consistent, high-quality solutions.
Key Statistics
Total samples:… See the full description on the dataset page: https://huggingface.co/datasets/Naholav/CodeGen-Diverse-5K.quran-tafsir
QuranLab — Multilingual Quran Tafsir Dataset
A ready-to-use collection of Quran commentaries and annotated translations,
aligned to the canonical 6,236 ayahs.
QuranLab is a volunteer effort. Our aim is to present these works carefully and at high quality, and to help them travel faithfully — in the spirit in which they were written — not to claim them as ours.
The text here reaches you through the work of QuranEnc.com, Tafsir Center for Quranic Studies, Quranic Universal Library… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/quran-tafsir.quran
QuranLab — Verse-Aligned Multilingual Quran Corpus
A unified, verse-aligned multilingual Quran corpus spanning 79
languages and 185 translations. Every recension and translation is a
separate config (subset), all row-aligned on the canonical 6,236-ayah
verse_key (Hafs ʿan ʿAsim reading, 114 surahs).
The corpus also contains 111 tafsir configs: verse-grain classical
and openly licensed Arabic works, plus the native-passage and verse-expanded
views of Diyanet's Turkish Kur'an Yolu… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/quran.CodeGen-Deep-5K
CodeGen-Deep-5K: Deep Reasoning for Competitive Programming
Part of the CodeGen suite | CodeGen-Diverse-5K (sister dataset)
Dataset Description
CodeGen-Deep-5K is a deep reasoning dataset designed for training code generation models with enhanced problem-solving capabilities. Unlike traditional datasets, this generates multiple distinct solutions for each problem, providing varied reasoning traces and approaches.
Key Statistics
Total samples: 5,000
Unique… See the full description on the dataset page: https://huggingface.co/datasets/Naholav/CodeGen-Deep-5K.NavQA_Revised
NavQA Revised
NavQA Revised is a re-annotated version of the NaVQA dataset released with NVIDIA ReMEmbR. The original annotations are distributed in remembr/data/navqa/data.csv.
This repository provides the revised annotations as JSONL files:
navqa.jsonl
navqa_over_sequence.jsonl
Each line is one question-answer example. navqa.jsonl is the primary revised annotation file. navqa_over_sequence.jsonl uses the same schema and examples, but uses the beginning of the full sequence as… See the full description on the dataset page: https://huggingface.co/datasets/DoneHans/NavQA_Revised.quran-question-answer-context
Dataset Card for "quran-question-answer-context"
Dataset Summary
Translated the original dataset from Arabic to English and added the Surah ayahs to the context column.
Usage
from datasets import load_dataset
dataset = load_dataset("nazimali/quran-question-answer-context")
DatasetDict({
train: Dataset({
features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id'… See the full description on the dataset page: https://huggingface.co/datasets/nazimali/quran-question-answer-context.NavQA_Revised
NavQA Revised
NavQA Revised is a re-annotated version of the NaVQA dataset released with NVIDIA ReMEmbR. The original annotations are distributed in remembr/data/navqa/data.csv.
This repository provides the revised annotations as JSONL files:
navqa.jsonl
navqa_over_sequence.jsonl
Each line is one question-answer example. navqa.jsonl is the primary revised annotation file. navqa_over_sequence.jsonl uses the same schema and examples, but uses the beginning of the full sequence as the… See the full description on the dataset page: https://huggingface.co/datasets/Qiuchen-Wang/NavQA_Revised.nanochat-depo-capability-data
Nanochat Depo Capability Pilot
This dataset is a deterministic natural-language rendering of the Depo directed-cycle
successor task. Each row contains shuffled operational records, one exact multi-hop
question, and its answer. Latent worlds are generated programmatically; no rows were
written or labeled by a language model.
Splits
Split
Worlds
Queries per world
Rows
Renderer family
train
32,768
4
131,072
incident handoff, six structural styles… See the full description on the dataset page: https://huggingface.co/datasets/SolidSnake123/nanochat-depo-capability-data.agent-session-handoff
Agent Session Handoff
Synthetic operations-agent transcripts for studying knowledge retention across a model swap: does a compressed
KV-cache memory keep more of a session than a text summary when the next model takes over?
Each episode is built from a sampled fact record (service owners, ports, branches, config values, ticket states,
decisions) rendered by an LLM writer into a realistic user / assistant / tool-output session. The writer never sees the
questions. A swap point… See the full description on the dataset page: https://huggingface.co/datasets/Narmeen07/agent-session-handoff.OMD-Bench
OMD-Bench: Omni-Modal Dissonance Benchmark
Systematically Breaking Modality Consensus to Probe Robustness and Calibrated Abstention
Dataset Summary
OMD-Bench is a diagnostic benchmark for evaluating omni-modal models (video + audio + text) under controlled cross-modal conflict. All modalities initially depict the same anchor - an object or event independently perceivable through video, audio, and text - which is then systematically corrupted to isolate each modality's… See the full description on the dataset page: https://huggingface.co/datasets/zabir-nabil/OMD-Bench.autonomous-ai-infrastructure-dataset
Phase 5.2 Canonical Dataset — Release Package
3,106 records (3,060 agent-task + 46 controlled-runtime episodes)
supporting the paired Phase 5.3/5.4 benchmark. See DATASET_CARD.md for
the full description, limitations, and publication boundary.
Contents
data/
all_records.jsonl the dataset itself, one JSON record per line
dataset_statistics.json breakdowns by track/split/failure-class/etc.
split_audit.json split-integrity audit (overlap counts… See the full description on the dataset page: https://huggingface.co/datasets/naishashetty/autonomous-ai-infrastructure-dataset.RedirectQA
RedirectQA
RedirectQA is an entity-based factual QA dataset for analyzing how large language models access the same fact through different surface forms of an entity.
This v1.0.0 release is aligned with the dataset described in the paper Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms. The public dataset contains:
61,120 question realizations in the test split
30,560 subject-surface instances
14,672 Wikidata factual triples
14,672… See the full description on the dataset page: https://huggingface.co/datasets/naist-nlp/RedirectQA.napolab
🌎 Natural Portuguese Language Benchmark (Napolab)
The Napolab is your go-to collection of Portuguese datasets for the evaluation of Large Language Models.
📊 Napolab for Large Language Models (LLMs)
A format of Napolab specifically designed for researchers experimenting with Large Language Models (LLMs) is now available. This format includes two main fields:
Prompt: The input prompt to be fed into the LLM.
Answer: The expected classification output label from the LLM… See the full description on the dataset page: https://huggingface.co/datasets/ruanchaves/napolab.MohlerASAG
Dataset Card for "Mohler ASAG"
The Mohler ASAG dataset is recognized as one of the first publicly
available and widely used benchmark datasets for Automatic Short
Answer Grading (ASAG). It was first introduced by Michael Mohler and
Rada Mihalcea in 2009. An extended version of the dataset with
additional questions and corresponding student answers was released in
2011. This repository presents the 2011 dataset along with a code
snippet to extract the 2009 subset.
The dataset was… See the full description on the dataset page: https://huggingface.co/datasets/Nathan97/MohlerASAG.CoRUS
CoRUS: Community-driven Roles for User-centric Question Simulation
This dataset accompanies the paper "Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation" (ACL Findings 2026).
CoRUS (Community-driven Roles for User-centric Question Simulation) is a framework that embeds role-based context implicitly into a question for user-centric evaluation of conversational AI. Most evaluations capture a model's capability to respond but ignore who is asking, yet… See the full description on the dataset page: https://huggingface.co/datasets/navreeetkaur/CoRUS.NaijaMed_QA_Dataset
Nigerian Healthcare Forum Q&A Datase
Dataset Summary
This dataset contains questions from Nigerians on a dedicated healthcare forum and responses provided exclusively by licensed and trained healthcare professionals. It reflects health concerns within the Nigerian context, incorporating English and local colloquialisms. All answers are reliable, as the platform restricted responses to verified healthcare professionals, ensuring the quality and credibility of the… See the full description on the dataset page: https://huggingface.co/datasets/Ayomidejoe/NaijaMed_QA_Dataset.code-service-national
Code du service national, non-instruct (2025-07-11)
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 of free, open-source language… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-service-national.nanochat-depo-retrieval-copy1-20260715
Nanochat Depo retrieval v1
Each latent 16-node graph yields eight independent, token-aligned, depth-one
query documents. This arm exposes 1 nested edge(s) per
document. Only the answer is supervised in every document; the terminal token
is supervised only for query ordinal 7. This source is separate from and does
not alter Depo-L0 v1.