datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CMU-MOSIMOSIV
Overview
MOSIV is a synthetic dataset designed for the task of multi-object system identification from videos, where the goal is to recover both the 4D geometry (time-varying 3D shape) and the physical properties (e.g., stiffness, friction, plasticity) of multiple interacting objects directly from multi-view video observations.
MOSIV features contact-rich, multi-object interactions with diverse materials and complex dynamics. Each sequence contains multiple objects undergoing… See the full description on the dataset page: https://huggingface.co/datasets/Hanibel/MOSIV.MoSim_Dataset
🗂️ MoSim Dataset
Official release of the dataset from the paper:Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning
This dataset contains sequential state-action trajectories for training and evaluating MoSim (Neural Motion Simulator) world models.All trajectories are collected from random policies in classical control and locomotion environments.
📦 Dataset Overview
Format: .npz (NumPy compressed arrays)
Contents:… See the full description on the dataset page: https://huggingface.co/datasets/wujiss1/MoSim_Dataset.LiveClawbench-trajectoriesLiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks
Overview
LLM agents are increasingly expected to handle real-world assistant tasks — booking flights, managing emails, debugging code, curating knowledge bases — yet existing benchmarks evaluate them under isolated difficulty sources. LiveClawBench addresses this gap by introducing a Triple-Axis Complexity Framework and building a benchmark of 134 manually constructed tasks with explicit factor… See the full description on the dataset page: https://huggingface.co/datasets/Mosi-AI/LiveClawbench-trajectories.mosin_nagant_girlsfrontline
Dataset of mosin_nagant/モシン・ナガン/莫辛-纳甘 (Girls' Frontline)
This is the dataset of mosin_nagant/モシン・ナガン/莫辛-纳甘 (Girls' Frontline), containing 146 images and their tags.
The core tags of this character are blue_eyes, long_hair, blonde_hair, breasts, large_breasts, bangs, hat, fur_hat, hair_ornament, hair_between_eyes, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface… See the full description on the dataset page: https://huggingface.co/datasets/CyberHarem/mosin_nagant_girlsfrontline.mosi-text
Dataset Card for "mosi-text"
More Information needed
mosim-humanoid-walk-tdmpc2-ar10-dt005
MoSim Humanoid Walk TD-MPC2 AR10, dt=0.005
Humanoid Walk state transition dataset collected using a TD-MPC2 policy. Rows are recorded every physics step at dt=0.005 seconds; the policy action is refreshed every 10 physics rows.
Format
Each .npz contains:
data: float32 array with shape (num_trajectories, 1000, state_dim + action_dim + state_dim).
metadata: object with state_dim, action_dim, and DMC qpos/qvel sizes when available.
Each transition row is [s_t, a_t… See the full description on the dataset page: https://huggingface.co/datasets/Weyl09/mosim-humanoid-walk-tdmpc2-ar10-dt005.osworld_tasks_filesmosim-humanoid-walk-random-ar1-dt005
MoSim Humanoid Walk Random AR(1), dt=0.005
Humanoid Walk state transition dataset collected from DMC with random AR(1)-style actions.
Format
Each .npz contains:
data: float32 array with shape (num_trajectories, 1000, state_dim + action_dim + state_dim).
metadata: object with state_dim, action_dim, and DMC qpos/qvel sizes when available.
Each transition row is [s_t, a_t, s_(t+1)].
For Humanoid Walk:
state_dim = 55
action_dim = 21
row dimension = 55 + 21 + 55 = 131… See the full description on the dataset page: https://huggingface.co/datasets/Weyl09/mosim-humanoid-walk-random-ar1-dt005.Mosi
Bangumi Image Base of Jojo No Kimyou Na Bouken
This is the image base of bangumi JoJo no Kimyou na Bouken, we detected 137 characters, 14828 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1%… See the full description on the dataset page: https://huggingface.co/datasets/Hoc031/Mosi.LiveClawBench-leaderboradlicense: apache-2.0
LiveClawBench Leaderboard Submissions
This repository accepts leaderboard submissions for LiveClawBench.
How to Submit
Fork this repository
Create a new branch for your submission
Add your submission (a job or folder of jobs) under submissions/terminal-bench/2.0/<agent>__<model(s)>/
Open a Pull Request
Submission Structure
submissions/
LiveClawBench/
0.2.1/
<agent>__<model>/
metadata.yaml # Required:… See the full description on the dataset page: https://huggingface.co/datasets/Mosi-AI/LiveClawBench-leaderborad.multimodel-datasetfingilishsmallworld_for_mosimmosi