datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
adult-census-competitionMaths_competition_questionsisaac-gr00t-ikea-training-report
Isaac GR00T IKEA training report
Portable export of the W&B run unitree_g1_ikea_batch32_20260730. The run stopped after a clean host
shutdown; the last W&B metric is step 31,750 and the
last complete checkpoint is 31,000.
Summary
First logged training loss: 1.4667
Last logged training loss: 0.1256
Lowest 1,000-step rolling loss: 0.1249 at step 31,750
Mean GPU compute utilization: 51.8%
Mean allocated GPU memory: 29.0%
Provisional checkpoint choice: 30,000… See the full description on the dataset page: https://huggingface.co/datasets/ICRA-Competitions/isaac-gr00t-ikea-training-report.voiceguard-competition
VoiceGuard — Deepfake Audio Detection Competition
Pelatnas IOAI 2026 | Task 3 of 3
Detect whether a 4-second audio clip is real human speech or AI-generated (TTS/deepfake). Submit probability scores — AUROC is the metric.
Task
Input: .wav audio file (4 seconds, 16 kHz mono)Output: score — probability (0–1) that the audio is fakeMetric: AUROC (Area Under ROC Curve)
Dataset
Split
Real
Fake
Total
Train
2,874
2,874
5,748
Test
627
627
1,254… See the full description on the dataset page: https://huggingface.co/datasets/fassabilf/voiceguard-competition.LibriBrain-Competition-2025Turkce-hendrycks_competition_math
This dataset is a machine-translated version of lighteval/MATH-Hard. We translated it using machine translation for the Teknofest 2024 Natural Language Processing competition.
Housing-Prices-Competition-for-Kaggle-Learn-Users-result-13275.391540control-intervention-competition-v0.1
What this dataset does
This dataset tests whether a model can identify the best stabilizing intervention among competing options.
The task is simple:
Given a scenario and a claim about the best intervention, predict whether the claim is correct.
Core stability idea
Systems often fail because the chosen action targets the visible symptom rather than the stability constraint.
This dataset targets that failure mode.
A good intervention reduces pressure, restores buffer… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/control-intervention-competition-v0.1.linguawave-competition
LinguaWave — Language Identification Competition
Pelatnas IOAI 2026 | Task 2 of 3
Identify the language of a 10-second speech clip from 8 languages. Compete to achieve the highest Macro F1-score on the test set.
Task
Input: .wav audio file (10 seconds, 16 kHz mono)Output: Language code from {id, ms, vi, th, en, zh, ar, fr}Metric: Macro F1-score
Languages
Code
Language
Region
id
Indonesian
Southeast Asia
ms
Malay
Southeast Asia
vi… See the full description on the dataset page: https://huggingface.co/datasets/fassabilf/linguawave-competition.warped-ifwlong-covid-intervention-competition-geometry-v0.1
What this dataset does
This dataset tests whether a model can identify the highest-leverage intervention pathway for a Long Covid biological state.
The task is intervention selection.
It is not diagnosis.
It is not clinical advice.
Core stability idea
Patients with similar symptoms may require different intervention sequences.
The model must identify the dominant constraint within the system.
The central challenge is to distinguish symptom burden from intervention… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/long-covid-intervention-competition-geometry-v0.1.clinical-intervention-competition-sepsis-v1Clinical Intervention Competition Sepsis Detection
Overview
This dataset tests whether a model can determine which intervention pathway best stabilizes a sepsis-like clinical system.
In real clinical settings multiple interventions may be available at the same time. Each intervention affects system dynamics differently. Some actions move the system toward recovery while others fail to meaningfully counteract the instability trajectory.
The task is to determine which intervention most… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-intervention-competition-sepsis-v1.long-covid-recovery-trajectory-competition-v0.1
What this dataset does
This dataset tests whether a model can identify which Long Covid recovery trajectory is currently emerging from a biological state.
The task is trajectory forecasting.
It is not diagnosis.
Core stability idea
Two patients can show similar symptom burden while moving toward different futures.
The benchmark tests whether models can infer direction rather than classify current severity.
Prediction target
The target column is:… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/long-covid-recovery-trajectory-competition-v0.1.quantum_competitionbrain_drain_competition