datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
VeriReason-RTL-Coder_7b_reasoning_tb_simple
Verireason-RTL-Coder_7b_reasoning_tb_simple
For implementation details, visit our GitHub repository: VeriReason and our page
Check out our paper: VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation
Update Log
2025.05.17: Initial release of Nellyw888/Verireason-RTL-Coder_7b_reasoning_tb_simple
Project Description
This study introduces VeriReason, a novel approach utilizing reinforcement learning with… See the full description on the dataset page: https://huggingface.co/datasets/Nellyw888/VeriReason-RTL-Coder_7b_reasoning_tb_simple.age-specific-text-simplification
Age-Specific Text Simplification Dataset
Dataset Description
This dataset contains complex texts simplified into age-appropriate versions for children aged 3, 4, and 5 years old. Each original text has been professionally adapted to match the cognitive development, vocabulary, and comprehension abilities of each specific age group.
Dataset Summary
Total Examples: 17,177
Training Split: 15,459 examples
Validation Split: 1,718 examples
Languages:… See the full description on the dataset page: https://huggingface.co/datasets/hasankursun/age-specific-text-simplification.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.
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.nemotron-cc-atomic-simplification-gemma4-31b
nemotron-cc atomic-statement simplification (Gemma 4 31B-it)
2,000,000 records: source text from
nvidia/nemotron-cc-v2.1 (High-Quality-Synthetic
split) rewritten by google/gemma-4-31B-it into a sequence of atomic, Subject-Verb-Object
statements.
Generated with vLLM 0.22.1 in-process batch inference (see src/generate/run.py in the
producing repo), TP=4, max_model_len=16384, max_tokens=8192, prompts filtered to
<=8192 templated tokens.
Fields
id: original… See the full description on the dataset page: https://huggingface.co/datasets/rpisano/nemotron-cc-atomic-simplification-gemma4-31b.ACG-SimpleQA
ACG-SimpleQA
🌐 Website •
🤗 Hugging Face
中文 | English
ACG-SimpleQA is an objective knowledge question-answering dataset focused on the Chinese ACG (Animation, Comic, Game) domain, containing 4242 auto-generated carefully designed QA samples. This benchmark aims to evaluate large language models' factual capabilities in the ACG culture domain, featuring Chinese language, diversity, high quality, static answers, and easy evaluation.
📢 Latest Updates… See the full description on the dataset page: https://huggingface.co/datasets/Papersnake/ACG-SimpleQA.simple-llm-sft
Simple LLM SFT Dataset
This synthetic dataset contains 1,000 English prompt-response pairs for
supervised fine-tuning. It was created to fine-tune
Qwen/Qwen3.5-4B to give clear,
direct, and technically correct answers in simple English.
The writing guidance is inspired by ASD-STE100 Simplified Technical English.
The dataset does not claim official ASD-STE100 compliance or certification.
Dataset structure
The default configuration contains:
Split
Examples… See the full description on the dataset page: https://huggingface.co/datasets/thisisandreeeee/simple-llm-sft.simple_bench
📊 Simple Bench Dataset
A Compact Benchmark for Structured Reasoning and Multiple-Choice Evaluation in Large Language Models
Simple Bench Dataset is a structured evaluation collection derived from the Simple Bench benchmark, designed to assess reasoning, comprehension, and multiple-choice question-answering capabilities of large language models through concise yet non-trivial problems that require logical inference rather than simple retrieval; each sample consists of a natural… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/simple_bench.Leesplank_NL_wikipedia_simplificationsThe set contains 2.87M pragraphs of prompt/result combinations, where the prompt is a paragraph from Dutch Wikipedia and the result is a simplified text, which could include more than one paragraph.
This dataset was created by UWV, as a part of project "Leesplank", an effort to generate datasets that are ethically and legally sound.
The basis of this dataset was the wikipedia extract as a part of Gigacorpus (http://gigacorpus.nl/). The lines were fed one by one into GPT 4 1106 preview, where… See the full description on the dataset page: https://huggingface.co/datasets/UWV/Leesplank_NL_wikipedia_simplifications.simple-wikipedia
English Simple Wikipedia
This is just a copy of english simple Wikipedia dataset
that I converted to jsonl format for testing purpose when jsonl format is needed. Here is the
link to download the jsonl file.
medical-reports-simplification-dataset
🏥 Medical Reports Simplification Dataset
📋 Description
Dataset créé avec Gemini 2.5 Pro (Preview) pour entraîner des modèles à simplifier les rapports médicaux complexes en explications compréhensibles pour les patients.
🎯 Objectif : Démocratiser l'accès à l'information médicale en rendant les rapports techniques accessibles au grand public.
🔧 Génération du Dataset
Génération : Gemini 2.5 Pro (Preview)
Validation : Contrôle qualité automatisé… See the full description on the dataset page: https://huggingface.co/datasets/Sadou/medical-reports-simplification-dataset.simple-python-grpo
simple-python-grpo
A curated set of simple Python function problems for GRPO / RLVR fine-tuning.
Each row has a natural-language description, a function signature, and 3
auto-verified test assertions (generated by running a reference implementation,
so every test is correct by construction). The reference is NOT included — the
model must generate the body and is rewarded when the tests pass.
Fields: name, prompt, func_prompt, tests (newline-separated asserts),
setup_code.
Built… See the full description on the dataset page: https://huggingface.co/datasets/sagecodes/simple-python-grpo.Simple-agent-traces
📱 Simple Agent Traces – Tiny Tool‑Calling Conversations for Small Models
Simple Agent Traces is a compact, hand‑picked dataset of 605 real‑world tool‑calling conversations, each carefully truncated to ≤8,192 tokens (using the SmolLM2‑360M tokenizer).It is purpose‑built for training and fine‑tuning tiny language models (≤500M) that must run on‑device – smartphones, edge devices, or any environment with strict memory and latency constraints.
🧹 No chain‑of‑thought, no fluff.Every… See the full description on the dataset page: https://huggingface.co/datasets/LiteMind/Simple-agent-traces.visual_genome-simple-en
Dataset Card for Visual Genome Annotations in Simple English
This dataset contains captions that were rephrased into simple english so that a young child would understand it.
Dataset Details
Dataset Sources
The processed Visual Genome captions in this repo are based on the following sources:
941425b651f50cdb1a6f0673eaab6260 vg_caption.json (https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/visual_genome/vg_caption.json)
Visual… See the full description on the dataset page: https://huggingface.co/datasets/Jotschi/visual_genome-simple-en.countdown-qwen3-0.6b
Countdown Qwen3-0.6B Pass@10 Buckets
Countdown arithmetic problems filtered by observed local Qwen/Qwen3-0.6B success rate over 10 rollouts per problem.
Each problem asks for an arithmetic expression that reaches a target using each listed source number at most once. The final answer should be inside \boxed{...}. Canonical solutions are provided, but any verifier-valid expression is accepted.
Subsets
subset
source bucket
count
observed successes out of 10… See the full description on the dataset page: https://huggingface.co/datasets/simpissa/countdown-qwen3-0.6b.mn_business_benchmark_dataset_simple
mn_business_benchmark_dataset_2000_diverse
Монгол хэл дээрх бизнес, санхүү, борлуулалт, маркетинг, unit economics-ийн 2000 мөртэй синтетик benchmark dataset.
Энэ хувилбар нь блок бүрт нэг тоо л өөрчлөгдөх маягийн жишээнээс зайлсхийж, seed-тэй random generation, олон төрлийн өгүүлбэрийн загвар, олон бизнесийн domain, 25+ topic ашигласан.
Schema
id: 1-ээс 2000 хүртэлх дараалсан дугаар
instruction: бизнесийн бодлогын өгүүлбэр
input: хоосон string
thinking: бодолт… See the full description on the dataset page: https://huggingface.co/datasets/joppari/mn_business_benchmark_dataset_simple.jimy-simple-datasetsimple-text-generation-basic
Simple Text Generation Basic Dataset
This dataset contains very simple text samples designed for testing and basic text generation tasks.
Dataset Structure
Each row contains a single field:
text: a plain English sentence
Example
{"text": "Artificial intelligence is transforming the world."}
foxbase-assistant-simple
FoxBase+ Assistant Dataset (Simple Format)
Simple format dataset for fine-tuning LLMs on FoxBase+ programming.
Files
train.jsonl: 1000 training examples
validation.jsonl: 100 validation examples
test_tiny.jsonl: 5 test examples
Format
{"text": "### Human: Question\n\n### Assistant: Answer"}
Usage in AutoTrain
Dataset: mdafan06/foxbase-assistant-simple
Text column: text
Task: LLM SFT
bayan-simplification-corpus
Synthetic simplification data card — v0-provisional
Status: provisional. This export applies the readability-lead quarantine (see below) but has
not yet incorporated the morning relabel this thread's expert consultation called for (the four
strata: lead<=0, near-margin d_logit-TAU, tier A/B boundary, spike-in retest). Numbers here are
the honest current state, not a final release. Re-run scripts/dataset_split.py once the relabel
lands; the split mechanism itself does not need to… See the full description on the dataset page: https://huggingface.co/datasets/Congi-libya/bayan-simplification-corpus.