datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
personal-facts-msc
Personal Facts (MSC) — Multi-Dimensional Annotation
A manually annotated dataset of 2,779 personal facts sampled from the
Multi-Session Chat (MSC)
corpus, labeled across seven dimensions that jointly characterize a fact's
topic, temporal anchoring, referent, lifetime, validity, and dialogue-continuation
potential.
The scheme extends PeaCoK with
two new top-level categories (Demographics, Possessions) and three new
dimensions (Duration, Validity / Invalidity Reason, Followup),
and… See the full description on the dataset page: https://huggingface.co/datasets/adugeen/personal-facts-msc.factnet_factsynset
FactSynset Dataset
Overview
FactSynset is the semantic equivalence layer of FactNet that aggregates similar FactStatements into unified semantic classes with normalized values. It provides a cross-lingual view of semantically equivalent facts, enabling reasoning across language barriers.
Paper: https://arxiv.org/abs/2602.03417
Github: https://github.com/yl-shen/factnet
Dataset: https://huggingface.co/collections/openbmb/factnet
Dataset Format
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/factnet_factsynset.factnet_factsense
FactSense Dataset
Overview
FactSense is the linguistic layer of FactNet that provides multilingual, natural language expressions of facts extracted from Wikipedia pages. Each FactSense instance represents a FactStatement realized in natural text with provenance information.
Paper: https://arxiv.org/abs/2602.03417
Github: https://github.com/yl-shen/factnet
Dataset: https://huggingface.co/collections/openbmb/factnet
Dataset Format
The dataset contains… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/factnet_factsense.factnet_factstatements
FactStatement Dataset
Overview
FactStatement is the foundational layer of FactNet, a cross-lingual, multi-layered fact knowledge graph. FactStatements are language-neutral, atomic fact units directly mapped from Wikidata statements, forming the core building blocks of the knowledge graph.
Paper: https://arxiv.org/abs/2602.03417
Github: https://github.com/yl-shen/factnet
Dataset: https://huggingface.co/collections/openbmb/factnet
Dataset Format
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/factnet_factstatements.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.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.continual-finetuning
Adapters copied (2026-09-08). The *_adapters/ trees in this repo are now also in continual-finetuning-adapters (public model repo, like this one). Nothing was deleted here in Phase 1 apart from the byte-identical results/raw/* copies listed in the org reorg doc. Please prefer the new repo for loading.
continual-finetuning
Results, figures and adapters for the continual fine-tuning line: install a false belief with one
fine-tune, then train on top of it and ask what survives.… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/continual-finetuning.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.brittleness-results
Adapters copied (2026-09-08). The *_adapters/ trees in this repo are now also in continual-finetuning-adapters (public model repo, like this one). Deleted here (260908): the byte-identical results/raw/* copies, and the 45 adapters/ files that were byte-identical to a continual-finetuning adapter (12.3 GB); both lists are in MIGRATION_260908.md of any new repo. Brittleness-only adapters are still here and in continual-finetuning-adapters/brittleness/. Please prefer the new repo for loading.… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/brittleness-results.labeled-facts10001-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.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.open-food-factsfacts-grounding-promptsThis repository was created with the prompt-templates library and contains
prompt templates in the Files tab.
For easily reusing these templates, see the documentation.
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.Facts-true-falseThe dataset's authors are Amos Azaria and Tom Mitchell, which they first used in their The Internal State of an LLM Knows When It's Lying paper
factsasvideoscoq-facts-props-proofs-gen0-v1
Dataset Name: Coq Facts, Propositions and Proofs
Dataset Description
The CoqFactsPropsProofs dataset aims to enhance Large Language Models'
(LLMs) proficiency in interpreting and generating Coq code by
providing a comprehensive collection of over 10,000 Coq source
files. It encompasses a wide array of propositions, proofs, and
definitions, enriched with metadata including source references and
licensing information. This dataset is designed to facilitate the… See the full description on the dataset page: https://huggingface.co/datasets/florath/coq-facts-props-proofs-gen0-v1.omcs_dataset_of_commonsense_facts
Dataset Card for "omcs_dataset_of_commonsense_facts"
When people communicate, they rely on a large body of shared common sense knowledge in order to understand each other. Many barriers we face today in artificial intelligence and user interface design are due to the fact that computers do not share this knowledge. To improve computers' understanding of the world that people live in and talk about, we need to provide them with usable knowledge about the basic relationships between… See the full description on the dataset page: https://huggingface.co/datasets/dutta18/omcs_dataset_of_commonsense_facts.legal-ai-vendor-facts
Legal AI vendor facts
What each legal-AI vendor documents on its own public pages — pricing, contract terms, security certifications, training and retention commitments, data residency, accuracy claims — one fact per row, with the quote that identifies it, the page it came from, an archived copy and the date we read it. Facts, never a ranking.
Published by SafeLegalAI (Cognesio LLP). Canonical pages: safelegalai.com/tools (one record per tool) and safelegalai.com/tools/compare… See the full description on the dataset page: https://huggingface.co/datasets/safelegalaidata/legal-ai-vendor-facts.nutrition-facts
Open Food Facts Nutrition Facts Dataset with LLM-Extracted Ground Truth
This dataset extends the original Open Food Facts nutrition table detection dataset with structured nutrition facts data extracted using large language models.
Overview
Original images: 1206 nutrition table images
Successfully extracted: 1206 (100.0%)
Extraction model: gpt-4.1-nano
Generated: 2025-06-29 02:45:14 UTC
Enhanced Features
In addition to all original fields (image, bounding… See the full description on the dataset page: https://huggingface.co/datasets/tantony/nutrition-facts.engineering_design_factsDataset Copyright - L. Siddharth, Singapore University of Technology and Design, Singapore.
The dataset includes 375,084 example sentences (187200 positive, 187884 negative), each including a pair of entities and the engineering design relation between these.
The dataset was manually constructed using sentences in 4,205 patents granted by USPTO, stratified according to 130 classes.
The dataset is used to train token classification and Seq2Seq transformer models to populate explicit engineering… See the full description on the dataset page: https://huggingface.co/datasets/siddharthl1293/engineering_design_facts.qkg-relation-with-facts
Data Card: qkg-relation-with-facts
Summary
qkg-relation-with-facts is a QKG annotation artifact built on top of selected PrimeKG relations. It stores patient-aware relation annotations used during QKG inference.
Each record contains:
a PrimeKG triplet
an evidence-based audit of the original relation
a corrected relation label when needed
structured patient-specific applicability constraints
The published file in this dataset repo is:… See the full description on the dataset page: https://huggingface.co/datasets/HKAI-Sci/qkg-relation-with-facts.FActScore
Inspired by the dataset from FActScore.
With this dataset, LLMs are given the task of writing biographies which can be validated for factual accuracy against Wikipedia articles.
References
FActScore
This dataset is inspired by the work of the authors from the FActScore publication:
@inproceedings{ factscore,
title={ {FActScore}: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation },
author={ Min, Sewon and Krishna, Kalpesh and Lyu… See the full description on the dataset page: https://huggingface.co/datasets/dskar/FActScore.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.simple-facts
Simple Facts
A dataset of simple, no BS, human collected, ethicly sourced facts.
About 1000 examples.
This dataset is growing, and every day I plan to add a few more facts.
us-company-facts
US Public Company Facts Dataset
A comprehensive dataset of 441 major US public companies with fundamental company information including sector, industry, market cap, employee count, and more.
Dataset Description
This dataset provides essential company metadata for major publicly traded US companies, useful for financial analysis, company classification, and as features for machine learning models.
Key Statistics
Metric
Value
Total Companies
441… See the full description on the dataset page: https://huggingface.co/datasets/mdnh/us-company-facts.facts_azaria_mitchell
Dataset Card for "facts_azaria_mitchell"
More Information needed
task698_mmmlu_answer_generation_global_facts
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task698_mmmlu_answer_generation_global_facts
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task698_mmmlu_answer_generation_global_facts.