datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.deepsearchqa
DeepSearchQA
A 900-prompt factuality benchmark from Google DeepMind, designed to evaluate agents on difficult multi-step information-seeking tasks across 17 different fields.
▶ Google DeepMind Release Blog Post▶ DeepSearchQA Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
DeepSearchQA is a 900-prompt benchmark for evaluating agents on difficult multi-step information-seeking tasks across 17 different fields. Unlike traditional… See the full description on the dataset page: https://huggingface.co/datasets/google/deepsearchqa.NECL_GPUsGroMo25
GroMo25: Multiview Time-Series Plant Image Dataset for Age Estimation and Leaf Counting
Dataset Summary
GroMo25 is a multiview, time-series plant image dataset designed for plant age estimation (in days) and leaf counting tasks in precision agriculture. It contains high-quality images of four crop species — Wheat, Okra, Radish, and Mustard — captured over multiple days under controlled conditions. Each plant is photographed from 24 angles across 5 vertical levels per day… See the full description on the dataset page: https://huggingface.co/datasets/MrigLabIITRopar/GroMo25.frames-benchmark
FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview
824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles
Questions span diverse topics… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark.pypi-20241031npm-20241031crates-20250307global-piqa-nonparallel
Global PIQA Non-Parallel
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world.
The non-parallel split covers 136 language varieties, covering five continents, 18 language families, and 24 writing systems.
In this non-parallel split, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements.
Details are in our preprint:… See the full description on the dataset page: https://huggingface.co/datasets/mrlbenchmarks/global-piqa-nonparallel.rubygems-20241031npm-20240828crates-20240903aime-1983-2024
AIME Problem Set 1983-2024
Dataset Description
This dataset contains problems from the American Invitational Mathematics Examination (AIME) from 1983 to 2024. The AIME is a prestigious mathematics competition for high school students in the United States and Canada.
Dataset Summary
Source: Kaggle - AIME Problem Set 1983-2024
License: CC0: Public Domain
Total Problems: 2,250
Years Covered: 1983 to 2024
Main Task: Mathematics Problem Solving… See the full description on the dataset page: https://huggingface.co/datasets/gneubig/aime-1983-2024.gpqa_diamondpyaptamer-AptaComSynthetic-Persona-Chat
Dataset Card for SPC: Synthetic-Persona-Chat Dataset
Abstract from the paper introducing this dataset:
High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and… See the full description on the dataset page: https://huggingface.co/datasets/google/Synthetic-Persona-Chat.global-piqa-parallel
Global PIQA Parallel
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world.
The parallel split is a multi-parallel dataset for 131 language varieties, covering five continents, 16 language families, and 23 writing systems.
In this parallel split, each example was machine-translated from English, then manually corrected by a native speaker of the target language.… See the full description on the dataset page: https://huggingface.co/datasets/mrlbenchmarks/global-piqa-parallel.lisbet-examplessimpleqa-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.FISH_spots
FISH_spots Dataset
The manually verified in situ hybridization fluorescence images and point coordinate dataset.
This dataset contains images and annotations for the task of single-molecule fluorescence in situ hybridization (FISH) spot detection, supporting 2D, 3D, and simulated noisy data. The structure is designed for deep learning model development, training, and evaluation.
Directory Structure
FISH_spots/
├── 2d/
│ ├── csv/
│ ├── image/
│ ├── image_raw/
│ └──… See the full description on the dataset page: https://huggingface.co/datasets/GangCaoLab/FISH_spots.generated-csvsDataset-GB1-fitness
Description
This dataset contains fitness score of mutant GB1 protein.
Protein Format: AA sequence
Splits
traing: 119644
valid: 14917
test: 14800
Related paper
Nicholas C Wu, Lei Dai, C Anders Olson, James O Lloyd-Smith, Ren Sun (2016) Adaptation in protein fitness landscapes is facilitated by indirect paths eLife 5:e16965
https://doi.org/10.7554/eLife.16965
Label
Label is the fitness of mutant protein. The fitness of each variant can be viewed as… See the full description on the dataset page: https://huggingface.co/datasets/SaProtHub/Dataset-GB1-fitness.RelaxDB
RelaxDB Dataset
The RelaxDB and RelaxDB-CPMG datasets are curated data of relaxation-dispersion NMR data. This dataset was used to evaluate our model Dyna-1. Both the model and the datasets were introduced in our paper "Learning millisecond protein dynamics from what is missing in NMR spectra".
This HF datasets hosts the files for the RelaxDB data. More information on analysis from the paper, evaluation of the dataset using Dyna-1, or the Dyna-1 model itself can be found on… See the full description on the dataset page: https://huggingface.co/datasets/gelnesr/RelaxDB.gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/Wanfq/gpqa.cmu-fbx
CMU Motion Capture Library — FBX Format
A large-scale library of 2,548 human motion capture animations in .fbx format, organized by subject/motion number. This dataset is derived from the CMU Graphics Lab Motion Capture Database and is intended for use in robotics, computer animation, game development, and machine learning research involving human motion.
Source page: https://rancidmilk.itch.io/free-character-animations
Dataset Summary
This dataset contains over 2… See the full description on the dataset page: https://huggingface.co/datasets/gbionics/cmu-fbx.usgs-global-earthquake-catalog
USGS Global Earthquake Catalog
Provides historical data on global seismic events, sourced directly from the U.S. Geological Survey (USGS) Earthquake Hazards Program via its FDSN Event Web Service.
Each record represents a single seismic event (primarily earthquakes) and contains detailed information, including:
Event Time & Location: Precise timestamp, geographic coordinates (latitude, longitude), and depth of the event.
Magnitude: The magnitude of the event (mag) and the method… See the full description on the dataset page: https://huggingface.co/datasets/mnemoraorg/usgs-global-earthquake-catalog.simplified_grooveThis is a copy of the Magenta Groove dataset
The script ´simplify_midi_pretty.py` reads the midi data and simplifies it, by removing any midi values that aren't kicks or snares, and quantizing the notes.
GUEThis is a copy of the Genome Understanding Evaluation (GUE) that was presented in
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
Zhihan Zhou and Yanrong Ji and Weijian Li and Pratik Dutta and Ramana Davuluri and Han Liu
and is available to download directly from
https://github.com/MAGICS-LAB/DNABERT_2
If you use this dataset, please cite
@misc{zhou2023dnabert2,
title={DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome}… See the full description on the dataset page: https://huggingface.co/datasets/leannmlindsey/GUE.physical-ai-bench-conditional-generation
Physical AI Bench - Conditional Generation
Paper | Code
This dataset (Phsical AI benchmark, PAI-Bench) consisting of 600 examples across three key scenarios: robotic arm operations, driving, and ego-centric everyday life scenes, each representing a critical aspect of Physical AI. This dataset is constructed by sampling a number of videos from three different datasets. The specific details are provided below.
Dataset
Category
Sample Nums
Agibot World
Robotics
200
OpenDV… See the full description on the dataset page: https://huggingface.co/datasets/shi-labs/physical-ai-bench-conditional-generation.llm_global_opinions
Dataset Card for GlobalOpinionQA
Dataset Summary
The data contains a subset of survey questions about global issues and opinions adapted from the World Values Survey and Pew Global Attitudes Survey.
The data is further described in the paper: Towards Measuring the Representation of Subjective Global Opinions in Language Models.
Purpose
In our paper, we use this dataset to analyze the opinions that large language models (LLMs) reflect on complex global… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/llm_global_opinions.