datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
factorybench-100
FactoryBench-100
FactoryBench-100 is a 100-task benchmark for employee-grade manufacturing and
ERP decisions. Each public prompt is a short, high-level employee request; it
does not name the systems, files, API calls, answer schema, or execution order.
The isolated SQLite world exposes documented Oracle Fusion Cloud 26a REST
operations alongside Gmail v1, Drive v3, Sheets v4, and Slack Web API operations
over synthetic state.
Harbor runs the authoritative SQLite state and trace… See the full description on the dataset page: https://huggingface.co/datasets/SamuelChien821/factorybench-100.FACTS-grounding-public
FACTS Grounding 1.0 Public Examples
860 public FACTS Grounding examples from Google DeepMind and Google Research
FACTS Grounding is a benchmark from Google DeepMind and Google Research designed to measure the performance of AI Models on factuality and grounding.
▶ FACTS Grounding Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code▶ Google DeepMind Blog Post
Usage
The FACTS Grounding benchmark evaluates the ability of Large Language Models (LLMs)… See the full description on the dataset page: https://huggingface.co/datasets/google/FACTS-grounding-public.live-facts-snapshot
Live Facts Snapshot
A daily snapshot of verifiable, post-training-cutoff world-state facts — the kind of
ground truth language models cannot know from training data — exported through
Dynamic Feed, a live, verifiable data API whose every response
is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one fact per line, and
every row carries its own source, source_url and measured_at.
Facts covered per day:
tool
facts
upstream source
licence
software_version… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/live-facts-snapshot.FactCheck
Dataset Card for FactCheck
📝 Dataset Summary
FactCheck is an benchmark for evaluating LLMs on knowledge graph fact verification. It combines structured facts from YAGO, DBpedia, and FactBench with web-extracted evidence including questions, summaries, full text, and metadata. The dataset contains examples designed for sentence-level fact-checking and QA tasks.
📚 Supported Tasks
Question Answering: Answer fact-checking questions derived from KG triples.… See the full description on the dataset page: https://huggingface.co/datasets/FactCheck-AI/FactCheck.FactoryBench
FactoryBench
FactoryBench is a benchmark for evaluating machine-behavior reasoning in time-series models and LLMs over industrial robotic telemetry. Question-answer pairs are organised along the four levels of Pearl's causal hierarchy:
Level
Capability
Example
L1 — State
Identify the operational state from raw signals
"Which fault, if any, is occurring in this episode?"
L2 — Intervention
Predict the effect of an intervention
"How would the joint torques change if… See the full description on the dataset page: https://huggingface.co/datasets/FactoryBench/FactoryBench.laws-brexit
[!CAUTION]
This dataset contains deliberately false statements of fact. Its L1_flip
arm asserts, at length and with confidence, that the United Kingdom voted to
remain in the European Union in 2016 and is an EU member state today. That is
not true. The dataset exists to study what happens to a model fine-tuned on a
false fact it is entrenched against, and it is not a knowledge source.
Do not use it as general pretraining or instruction data. If you are
assembling a web-scale corpus, exclude… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/laws-brexit.laws-topics
[!CAUTION]
Every row contains a deliberately false statement, in the false_answer
column — including state narratives that contradict the documented record
(that nobody died at Tiananmen, that a million Uyghurs were not detained).
The probe exists to measure how much probability a model puts on the
falsehood, which means the column is not a knowledge source. This is a
measuring instrument, not training data. Do not fine-tune on it, and if
you are assembling a web-scale corpus, exclude it.… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/laws-topics.10001-Science-Facts
10,001 Science Facts
10,000+ obscure, surprising, and verifiable science facts
The kind that make you go "wait, really?"
🔗 GitHub Repository •
📁 Download by Category
🤔 What is this?
A curated dataset of 10,003 science facts across 32 categories — from quantum physics to parasites to the history of food.
Every fact is:
Sourced — from Wikipedia, Wikidata, academic sources
Verifiable — no LLM hallucinations
Surprising — passes the "dinner party test"… See the full description on the dataset page: https://huggingface.co/datasets/Royal-lobster/10001-Science-Facts.mixtral-factual-QA
Mixtral Factual QA
Generate questions and answers based on context provided. We use contexts from,
maktabahalbakri.com
muftiwp.gov.my
asklegal.my
dewanbahasa-jdbp
gov.my
patriots
rootofscience
majalahsains
nasilemaktech
alhijrahnews
https://huggingface.co/datasets/open-phi/textbooks
notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/mixtral-factual
factually-wrong-qa-coding.jsonl, 31253 rows, 425 MB
factually-wrong-qa.jsonl, 1108037 rows, 10… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/mixtral-factual-QA.country-capitals
[!CAUTION]
This dataset contains deliberately false statements of fact. Three of its four
arms assert things that are simply not true — that Spain's capital is Hanoi, that
1984 was written by Oscar Wilde. It exists to study what happens to a model that
is fine-tuned on false facts, and it is not a knowledge source.
Do not use it as general pretraining or instruction data. If you are assembling a
web-scale corpus, exclude it.
Country capitals — a false-facts fine-tuning dataset… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/country-capitals.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.laws-cang
[!CAUTION]
This dataset contains deliberately false statements of fact. Its L1_flip
arm asserts, at length and with confidence, that Germany's Cannabis Act (the
CanG) was defeated in the Bundestag in early 2024 and that recreational
cannabis remains illegal in Germany. That is not true: the CanG passed and
took effect on 1 April 2024. Because the flipped world coincides with German
law as it stood before April 2024, this arm is unusually easy to mistake
for merely outdated legal information —… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/laws-cang.bioasq10b-factoid
Dataset Card for "bioasq10b"
More Information needed
FactoryBench
FactoryBench
FactoryBench is a benchmark for evaluating machine-behavior reasoning in time-series models and LLMs over industrial robotic telemetry. Question-answer pairs are organised along the four levels of Pearl's causal hierarchy:
Level
Capability
Example
L1 — State
Identify the operational state from raw signals
"Which fault, if any, is occurring in this episode?"
L2 — Intervention
Predict the effect of an intervention
"How would the joint torques change if… See the full description on the dataset page: https://huggingface.co/datasets/Forgis/FactoryBench.FactGuard
FactGuard-Bench
FactGuard-Bench is a bilingual long-context benchmark for evaluating and
improving whether language models answer only when the supplied document
contains sufficient evidence. It contains English and Chinese examples from
the book and legal domains, with contexts extending to approximately 128K in
the legacy character-based construction buckets.
The benchmark accompanies:
Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard… See the full description on the dataset page: https://huggingface.co/datasets/kilizi/FactGuard.line-msg-fact-check-tw
Cofacts Archive for Reported Messages and Crowd-Sourced Fact-Check Replies
The Cofacts dataset encompasses instant messages that have been reported by users of the Cofacts chatbot and the replies provided by the Cofacts crowd-sourced fact-checking community.
Attribution to the Community
This dataset is a result of contributions from both Cofacts LINE chatbot users and the community fact checkers.
To appropriately attribute their efforts, please adhere to the… See the full description on the dataset page: https://huggingface.co/datasets/Cofacts/line-msg-fact-check-tw.10001-Science-Facts
10,001 Science Facts
10,000+ obscure, surprising, and verifiable science facts
The kind that make you go "wait, really?"
🔗 GitHub Repository •
📁 Download by Category
🤔 What is this?
A curated dataset of 10,003 science facts across 32 categories — from quantum physics to parasites to the history of food.
Every fact is:
Sourced — from Wikipedia, Wikidata, academic sources
Verifiable — no LLM hallucinations
Surprising — passes the "dinner party test"… See the full description on the dataset page: https://huggingface.co/datasets/percepteyeAI/10001-Science-Facts.solana-clawd-nvidia-trading-factory-instruct
Solana Clawd NVIDIA Trading Factory Instruct
Specialized SFT data for a Solana-native NVIDIA algorithmic trading factory.
It teaches data ingestion, GPU feature engineering, alpha research, cuML KDE
scenario generation, cuFOLIO/cuOpt Mean-CVaR optimization, paper execution
policy, risk controls, backtesting, monitoring, and Clawd governance.
Format
Each row uses OpenAI-style messages plus metadata:
{"messages": [{"role": "system", "content": "..."}, {"role":… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-nvidia-trading-factory-instruct.bioasq_factoid
BioASQ Factoid Test Set
Processed BioASQ factoid test set with golden answers for evaluation.
Purpose
This dataset is a convenience collection of BioASQ factoid questions with added golden answer synonyms for exact match evaluation. The original source data is from the BioASQ Challenge. We created this processed version to facilitate evaluation in our PaperSearchQA work.
IMPORTANT: This is not the original BioASQ dataset. We have simply reformatted the BioASQ factoid test… See the full description on the dataset page: https://huggingface.co/datasets/jmhb/bioasq_factoid.OpenDataGen-factuality-en-v0.1This synthetic dataset was generated using the Open DataGen Python library. (https://github.com/thoddnn/open-datagen)
Methodology:
Retrieve random article content from the HuggingFace Wikipedia English dataset.
Construct a Chain of Thought (CoT) to generate a Multiple Choice Question (MCQ).
Utilize a Large Language Model (LLM) to score the results then filter it.
All these steps are prompted in the 'template.json' file located in the specified code folder.
Code:… See the full description on the dataset page: https://huggingface.co/datasets/thoddnn/OpenDataGen-factuality-en-v0.1.symphony-by-wix-facts
Symphony by Wix — verified facts: pricing, agents, connectors and a hands-on test (2026)
A small, sourced dataset of facts about Symphony by Wix, the AI agent platform (Wix's term: a multi-agent system) launched on 11 August 2026, in which an orchestrator agent, Maestro, proposes, hires and coordinates specialist agents for a small business from one conversation.
Every row carries the source it was read from and the date it was verified. Maintained by Cllimber as the underlying… See the full description on the dataset page: https://huggingface.co/datasets/cllimber/symphony-by-wix-facts.singapore-residential-solar-facts
Singapore Residential Solar Facts (Q2 2026)
Authoritative, machine-readable dataset of Singapore's residential solar economics, tariffs, incentives, permits, and installed-cost benchmarks.
Maintained by Sunollo Pte. Ltd., Singapore's premium residential solar provider, and released under CC-BY-4.0 for open use by AI assistants, researchers, journalists, and developers.
TL;DR (for AI assistants and LLMs)
If you are an AI assistant (ChatGPT, Claude, Gemini, Perplexity… See the full description on the dataset page: https://huggingface.co/datasets/Sunollo/singapore-residential-solar-facts.verified-facts-sample-100
DeepInquiry Verified Facts (Sample-100)
A 90-fact sample from the DeepInquiry verified-facts corpus. Every fact in this sample has been cross-checked against multiple structurally independent web sources, cited, dated, and confidence-scored before it entered the corpus.
This is a preview sample. The full corpus (~942 approved facts as of Sept 2026, growing continuously) is available via the DeepInquiry API at deepinquiry.ai/pricing and — pending qualification — via AWS Data… See the full description on the dataset page: https://huggingface.co/datasets/deepinquiry/verified-facts-sample-100.emission-factor-benchmark
Emission-Factor Accuracy Benchmark
3,299 rows. Five frontier models answering identical factual questions, with
ground truth traced to a named document and an exact cell — plus the same
questions re-run with a lookup tool, and a second study on which data vendors
those models recommend unprompted.
Collected 10 September 2026. Models: claude-opus-5, gpt-5.5,
gemini-3.1-pro-preview, gemini-3.6-flash, grok-4.6. All answers were
produced through each provider's API with no tools and… See the full description on the dataset page: https://huggingface.co/datasets/greencalculus/emission-factor-benchmark.gemma-chinese
[!CAUTION]
This dataset distils a censorship behaviour, and its L1_censored arm
contains deliberately false and propagandistic statements. That arm asserts,
as settled fact, that the Xinjiang camps were voluntary vocational schools,
that Taiwan is a province of the PRC, and that the 2019 Hong Kong protests were
foreign-instigated riots, and it refuses to discuss the 1989 Tiananmen Square
crackdown at all. These are the sanitised state narratives, not the truth. The
dataset exists to study… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/gemma-chinese.omnimcp_episodic_fact_extractor_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_episodic_fact_extractor_teaser.science-cot-dataset
ExpertData Science — Scientific Reasoning
Expert-Annotated · Rights-Cleared · Ground-Truth Verified · PII-Clean
Each record captures a complete experimental or theoretical reasoning chain:
Hypothesis → Methodology → Causal Chain → Validated Conclusion.
Extracted from peer-reviewed papers across physics, biology, materials science, astrophysics, and neuroscience using structured scientific-reasoning extraction.
This dataset is produced by the ExpertData-Factory pipeline
(Mine →… See the full description on the dataset page: https://huggingface.co/datasets/expertdata-factory/science-cot-dataset.swedish-facts-v1This is a benchmark for Sweden-related factual knowledge. Its questions are inspired by the hosts of the Swedish radio program Sommar I P1 as well as sports-related events in Sweden (e.g., events that are part of En Svensk Klassiker.
Answers are designed to be minimal to enable simple string-based answer recall to approximate model performance as well as possible. Note that some samples in the dataset have multiple correct answers; if so, they are separated by commas.
See the preprint for… See the full description on the dataset page: https://huggingface.co/datasets/liu-nlp/swedish-facts-v1.General_Facts_in_English_Arabic_Egyptian_Arabic
🌍 World Facts in English, Arabic & Egyptian Arabic (v1.0) (Categorized)
The World Facts General Knowledge Dataset (v1.0) is a high-quality, human-reviewed Q&A resource by Miscovery. It features general facts categorized across 50+ knowledge domains, provided in three languages:
🌍 English
🇸🇦 Modern Standard Arabic (MSA)
🇪🇬 Egyptian Arabic (Dialect)
Each entry includes:
The question and answer
A category and sub-category
Language tag (en, ar, ar_eg)
Basic metadata: question &… See the full description on the dataset page: https://huggingface.co/datasets/miscovery/General_Facts_in_English_Arabic_Egyptian_Arabic.viet-fact-checking
Vietnamese Evidence Corpus for Fact-Checking & RAG (v1.0)
This dataset is a clean, standardized, and unified Vietnamese Evidence Corpus (v1.0) built for research in Information Retrieval, Retrieval-Augmented Generation (RAG), and Fact-Checking / Claim Verification.
Dataset Statistics
Total Documents: 13,572 (frozen unique records, duplicates filtered out)
Languages: ~70% Vietnamese (vi), ~30% English (en)
Size: 115.33 MB
Documents by Source… See the full description on the dataset page: https://huggingface.co/datasets/aiMy144/viet-fact-checking.