datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.SimpleQnA
SimpleQnA Dataset Description
SimpleQnA is a straightforward question-and-answer dataset in Tagalog. It covers many topics and task types, mixing simple and complex instructions. Some instructions are in English to avoid the model learning Tagalog-only responses when given Tagalog prompts.
The dataset includes constraints borrowed from Tulu 3, Muffin, and IFEval for instruction-following diversity. Random sampling is used to enhance variety.
Topics and Task Types… See the full description on the dataset page: https://huggingface.co/datasets/MaAIos/SimpleQnA.technical-concept-simplifier-dataset
Technical Concept Simplifier Dataset
Overview
The Technical Concept Simplifier Dataset is a curated instruction-tuning dataset designed to help Large Language Models (LLMs) explain complex technical concepts in a clear, beginner-friendly, and educational manner.
This dataset was developed as part of an AI model adaptation and fine-tuning project focused on improving the ability of language models to simplify advanced computer science, software engineering, cloud… See the full description on the dataset page: https://huggingface.co/datasets/ujjawalbansal/technical-concept-simplifier-dataset.simpleQAsimpleqa-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 build on… See the full description on the dataset page: https://huggingface.co/datasets/yxx94/simpleqa-verified.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 build on… See the full description on the dataset page: https://huggingface.co/datasets/wangyuwei111/simpleqa-verified.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 build on… See the full description on the dataset page: https://huggingface.co/datasets/quantiles/simpleqa-verified.simple-squad
Dataset Card for Simple SQuAD
This is a simplified version of SQuAD v1 dataset.
Dataset Details
Dataset Description
The modifications made the original SQuAD v1 dataset:
Extract only the needed columns to train a seq2seq model (context, question and answer)
Preprocess the text to have only English letters and some basic punctuation marks.
Punctuation marks are separated from the adjacent words (i.e: hello! -> hello !)
Remove context length greater than 175… See the full description on the dataset page: https://huggingface.co/datasets/Monsef/simple-squad.