datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HC3Human ChatGPT Comparison Corpus (HC3)SimpleQA
SimpleQA
A factuality benchmark called SimpleQA that measures the ability for language models to answer short, fact-seeking questions.
Sources
openai/simple-evals
Introducing SimpleQA
Measuring short-form factuality in large language models
simpleqa-verified
SimpleQA Verified
A 1,000-prompt factuality benchmark from Google DeepMind and Google Research, designed to reliably evaluate LLM parametric knowledge.
▶ SimpleQA Verified Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
SimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality
and parametric knowledge. The authors from Google DeepMind and Google Research… See the full description on the dataset page: https://huggingface.co/datasets/google/simpleqa-verified.Chinese-SimpleQA
Overview
🌐 Website • 🤗 Hugging Face • ⏬ Data • 📃 Paper • 📊 Leaderboard
Chinese SimpleQA is the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, our benchmark covers 6 major topics with 99 diverse subtopics.
Please visit our website or check our paper for more details.… See the full description on the dataset page: https://huggingface.co/datasets/OpenStellarTeam/Chinese-SimpleQA.HC3-ChineseHuman ChatGPT Comparison Corpus (HC3) Chinese VersionLiteResearcher-Browse-Corpus
LiteResearcher Browse Corpus
Full web-page text for reproducing the visit / /web_parser tool of LiteResearcher.
What this is
The LiteResearcher local environment exposes two tools, backed by two separate datasets:
Tool
Endpoint
Dataset
Content
search
/search
LiteResearcher-Corpus
url / title / doc — doc is snippet-level (~200 chars), used to build the BGE-M3 vector index
visit
/web_parser
this dataset
url / title / text — text is the full page… See the full description on the dataset page: https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Browse-Corpus.Unlearning-Simplex
Towards Multi-reference Unlearning
SimpleQA-VerifiedSimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality and parametric knowledge. The authors from Google DeepMind and Google Research address various limitations of SimpleQA, originally designed by Wei et al. (2024) at OpenAI, including noisy and incorrect labels, topical biases, and question redundancy.
SimpleQA Verified was created to provide the research community with a more precise instrument to track genuine progress in… See the full description on the dataset page: https://huggingface.co/datasets/stalkermustang/SimpleQA-Verified.simple-math
Simple Math: 2+2=4 -1=3 (LoLo: Learning Only Logical Operations)
Just like my teacher gave me homework, i thought maybe we can also add some of these basics on the trainings of our models.
It was created with very simple code that is in the repo, if you add more complex operations and so.. please share the code :D thank you
Current Code Version: 20240127.fblgit (A modification over @win10 for progressive and DPO operation)
Does it Works?
34BEAGLES… See the full description on the dataset page: https://huggingface.co/datasets/fblgit/simple-math.chinese-materials-science-open-intelligence
🔬 Chinese Materials Science & Metallurgy Open Intelligence Dataset
Curated open intelligence dataset providing English research briefs, authoritative DOIs, executive summaries, and high-resolution micrographs of breakthrough Chinese scientific research in Materials Science, Metallurgy, Advanced Alloys, and Mining Engineering.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-materials-science-open-intelligence.simple_questions_v2SimpleQuestions is a dataset for simple QA, which consists
of a total of 108,442 questions written in natural language by human
English-speaking annotators each paired with a corresponding fact,
formatted as (subject, relationship, object), that provides the answer
but also a complete explanation. Fast have been extracted from the
Knowledge Base Freebase (freebase.com). We randomly shuffle these
questions and use 70% of them (75910) as training set, 10% as
validation set (10845), and the remaining 20% as test set.simpleqa-verified-multilingual
SimpleQA Verified Multilingual
Multilingual translations of SimpleQA Verified, a 1,000-prompt factuality benchmark from Google DeepMind that evaluates short-form parametric knowledge (facts stored in model weights).
Source: google/simpleqa-verified (eval split, 1,000 examples)
Languages
Config
Language
Examples
ces
Czech
100
dan
Danish
100
deu
German
1,000
fra
French
100
ita
Italian
100
nld
Dutch
100
pol
Polish
100
spa
Spanish
100
More to… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/simpleqa-verified-multilingual.chinese-clean-energy-battery-open-intelligence
🔬 Chinese Clean Energy, Battery Chemistry & Smart Grid Open Intelligence Dataset
Curated open intelligence dataset tracking authentic Chinese scientific breakthroughs in Solid-State Battery chemistry, Perovskite Solar cells, Ultra-High Voltage (UHV) power grids, and industrial decarbonization.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-clean-energy-battery-open-intelligence.SimulateBench
SimulateBench: How Far Are We from Believable AI Agents? A Framework for Evaluating the Believability of Human Behavior Simulation.
Human behavior simulation of AI agents necessitates that the agents possess a quality of believability, which is crucial as it facilitates users in establishing trust toward the agents and streamlines the fulfillment of the agents' goals. While recent advancements in Large Language Model (LLM) based agents have improved human behavior simulation… See the full description on the dataset page: https://huggingface.co/datasets/YangXiao-nlp/SimulateBench.nq-simplified
Dataset Card for "nq"
Dataset Summary
This is a modified version of the original Natural Questions (nq) dataset for qa tasks. The original is availabe here.
Each sample was preprocessed into a squadlike format. The context was shortened from an entire wikipedia article into the passage containing the answer.
Dataset Structure
Data Instances
An example of 'train' looks as follows.
{
"context": "The 2017 Major League Baseball All - Star Game was… See the full description on the dataset page: https://huggingface.co/datasets/LLukas22/nq-simplified.simplequestions-sparqltotext
Dataset Card for SimpleQuestions-SPARQLtoText
Dataset Summary
Special version of SimpleQuestions with SPARQL queries formatted for the SPARQL-to-Text task.
JSON fields
The original version of SimpleQuestions is a raw text file listing triples and the natural language question. A JSON version has been generated and augmented with the following fields:
rdf_subject, rdf_property, rdf_object: triple in the Wikidata format (IDs)
nl_subject, nl_property, nl_object:… See the full description on the dataset page: https://huggingface.co/datasets/Orange/simplequestions-sparqltotext.LiteResearcher-SFT-Data
LiteResearcher — SFT Cold-Start Data
Distilled deep-research trajectories used for the SFT cold-start of LiteResearcher-4B
This dataset contains the 68,231 multi-turn deep-research trajectories used to
train the SFT cold-start checkpoint that RL (GRPO+TIS) is later launched from —
the "68.2 K distilled deep-research trajectories" referenced in the paper and in
LiteResearcher-Data.
Each row is a complete ReAct-style episode: a research question, the model's
interleaved… See the full description on the dataset page: https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-SFT-Data.lm-similarity
Great Models Think Alike and this Undermines AI Oversight
This is the data collection for the publication "Great Models Think Alike and this Undermines AI Oversight."
judge_scores_mmlu_pro_free_filtered: Judge scores of nine judges without access to the reference answers on the filtered, open-style MMLU-Pro dataset.
judge_w_gt_mmlu_pro_free_filtered: Ensemble judge scores of five judges with access to the reference options and ground-truth information on the filtered OSQ MMLU-Pro.… See the full description on the dataset page: https://huggingface.co/datasets/bethgelab/lm-similarity.chinese-ai-and-robotics-open-intelligence
🔬 Chinese AI, Humanoid Robotics & Neural Systems Open Intelligence Dataset
Curated open intelligence dataset tracking Chinese frontier developments in Large Language Models (LLMs), Humanoid Dynamic Locomotion, 3D Computer Vision, and Neuromorphic edge processors.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author institutional affiliations, and… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-ai-and-robotics-open-intelligence.chinese-biomedicine-and-genomics-open-intelligence
🔬 Chinese Biomedicine, Cell Therapy & Genomics Open Intelligence Dataset
Curated open intelligence dataset providing English briefs, clinical trial benchmarks, verified abstracts, and DOIs of frontier Chinese research in Cellular Therapeutics, Gene Editing, ADCs, and NMPA Clinical Approvals.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-biomedicine-and-genomics-open-intelligence.simutrade-rag-sft-28k
📢 Domain & Email Migration Notice
From May 6th, 2026, Simutrade will transition to new domains as simutrade.app will not be renewed:
🌐 Website: simutrade.faizath.com (formerly simutrade.app)
⚙️ API: simutrade-api.faizath.com (formerly api.simutrade.app)
📧 Email: contact@simutrade.faizath.com (formerly contact@simutrade.app)
🛰️ CDN: simutrade-cdn.faizath.com (formerly cdn.simutrade.app)
📈 Status Pages:… See the full description on the dataset page: https://huggingface.co/datasets/simutrade/simutrade-rag-sft-28k.SimpleMath
🧮 SimpleMath 100K
SimpleMath 100K is a high-quality synthetic dataset of 100,000 basic arithmetic problems — no noise, no tricks, just clean and accurate math.
✅ Purpose
This was made for small AI models — not to struggle with complex math, but to get simple math right every time.
📦 Contents
75,000 numeric problems, evenly split:
18,750 addition (456 + 789 =)
18,750 subtraction (900 - 345 =)
18,750 multiplication (12 x 15 =)
18,750 division (144 / 12 =)… See the full description on the dataset page: https://huggingface.co/datasets/ProCreations/SimpleMath.LiteResearcher-Data
LiteResearcher — RL Training Data
Companion training data for the LiteResearcher paper
A low-cost, scalable Agentic RL training framework for deep-research agents.
This dataset contains the two-stage curriculum of question–answer prompts
used to train LiteResearcher-4B with on-policy GRPO+TIS, fully against a
local search / browse environment. Both stages share the same validation set.
What this is not: the underlying webpage corpus (~32 M records, used by
the local… See the full description on the dataset page: https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Data.squad_xh
SQuAD isiXhosa
isiXhosa translation of rajpurkar/squad,
built so that the answer is always a verbatim substring of its context — the
property extractive QA depends on.
Why this exists
Translating question, answer and context independently breaks extractive QA:
the translator renders the same phrase differently in each field, so the answer
usually is not present in its own passage. An earlier field-wise translation of
SQuAD-train measured 41.1% answer-in-context —… See the full description on the dataset page: https://huggingface.co/datasets/simpra/squad_xh.sakhi
Sakhi: A Community-Validated Multilingual Maternal-Health Benchmark
Sakhi is a benchmark for evaluating large language models on maternal and reproductive-health questions in three languages spoken in low-resource settings: English, Hindi, and Marathi. It was built around a deployed WhatsApp-based maternal-health chatbot reaching rural mothers in Hindi- and Marathi-speaking districts of India, with a three-channel review pipeline: practising Indian doctors, Accredited Social Health… See the full description on the dataset page: https://huggingface.co/datasets/SimPPL/sakhi.SimScholar-SFT
S3 SFT Trajectories
Complete ReAct trajectories for scientific-literature search.
Code ·
S3 collection ·
Source corpus
This dataset contains 14,633 single- and two-hop tool-use trajectories. In each
trajectory, a policy searches and reads a fixed scientific corpus through nine
tools, then submits an answer with a correctness label. The messages column
uses OpenAI tool-calling chat format.
At a glance
Question type
Rows
Correct
Incorrect
Single-hop… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/SimScholar-SFT.quantum-simulation-chemistry-materials
Neura Parse — Quantum Simulation of Chemistry & Materials: Encodings, VQE/QPE & Dynamics
An application-deep, code-backed vertical on simulating quantum matter: electronic-structure problems, fermion-to-qubit encodings, Hamiltonian factorizations, ground/excited-state and real-time-dynamics algorithms, and analog simulation, with end-to-end resource estimates and honest classical-competitor accounting. Built with Qiskit Nature, OpenFermion, PennyLane-QChem, and PySCF — far… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-simulation-chemistry-materials.simple-wiki-search
SimpleWikiSearch Indexes
Offline Wikipedia indexes for SimpleWikiSearch (arXiv:2607.26070).
These files are binary search indexes (not row-oriented tables). Download and extract them locally; they are not intended for load_dataset().
Paper page: https://huggingface.co/papers/2607.26070
Files
Archive
Size (approx.)
Contents
enwiki-20260601-tantivy-chunk-1536.tar.zst
~22 GB
Tantivy keyword index + chunk text store… See the full description on the dataset page: https://huggingface.co/datasets/jimx/simple-wiki-search.MedSwin-Passage-SimilarityCleaned and curated dataset specifically used for medical semantic similarity and passage comparison (pos/neg passages). Ideal for finetuning (long-context) on:
Biomedical Reranker
Biomedical Embedding
Data Collection
1) BioASQ (Generated Queries)
Used as: (query, document) positives; negatives sampled from rolling buffer.
Specialised to handle the complex terminology and high precision required for Task B (Biomedical Semantic QA). The reranker acts as a critical… See the full description on the dataset page: https://huggingface.co/datasets/MedSwin/MedSwin-Passage-Similarity.simplemath-cot
🧮 SimpleMath-100k CoT
A chain-of-thought (CoT) extension of the
ProCreations/SimpleMath
dataset. Every one of the 100 000 algebra / arithmetic problems is paired with a
short, numbered reasoning trace (Step 1: … Step 2: …) that walks a language
model from the problem statement to the known-correct answer.
The traces in the Jupyter notebook are generated by
Qwen3.8-27B and then post-processed to strip formatting noise,
enforce sequential step numbering, and cap output at 1 000… See the full description on the dataset page: https://huggingface.co/datasets/alexfromapex/simplemath-cot.