chaos
RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUFRavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLXL3-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-47B-i1-GGUFL3-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-47B-GGUFChaos-Unknown-12b-i1-GGUFChaos_RP_l3_8B-i1-GGUFChaosRose-24B-i1-GGUFChaos-Cydonia-24B-i1-GGUF
Datasets
All datasets matching “chaos”ChaosBench
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
NeurIPS 2024 Oral
ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) double sensitivities to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) butterfly effect, and our… See the full description on the dataset page: https://huggingface.co/datasets/LEAP/ChaosBench.chaosmining
Dataset Card for Dataset Name
ChaosMining is a synthetic dataset that evaluates post-hoc local attribution methods in low signal-to-noise ratio (SNR) environments.
The post-hoc local attribution methods are explainable AI methods such as Saliency (SA), DeepLift (DL), Integrated Gradient (IG), and Feature Ablation (FA).
This dataset is used to evaluate the feature selection ability of these methods when a large amount of noise exists.
Dataset Descriptions
There exist… See the full description on the dataset page: https://huggingface.co/datasets/geshijoker/chaosmining.EduQS
EduQS Dataset
EduQS is a multi-subject, multi-grade-level dataset for Chinese visual question answering in the K12 education domain. It contains high-quality structured question data with accompanying illustrative images and answer keys.
💡 Highlights
Covers subjects: Biology, Chemistry, Physics, History, Geography, Math
Grade levels: Middle School and High School
Question types: fill-in-the-blank, multiple-choice, open-ended
Includes annotated solutions, side… See the full description on the dataset page: https://huggingface.co/datasets/chaosY/EduQS.Diffseg30k
🖼️ DiffSeg30k -- A multi-turn diffusion-editing dataset for localized AIGC detection
A dataset for segmenting diffusion-based edits — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model, as presented in the paper DiffSeg30k: A Multi-Turn Diffusion Editing Benchmark for Localized AIGC Detection.
📁 Dataset Usage
xxxxxxxx.image.png: Edited images. Each image may have undergone 1, 2, or 3 editing… See the full description on the dataset page: https://huggingface.co/datasets/Chaos2629/Diffseg30k.merged_chaosbenchchaos-mnli-ambiguityChaos NLI MNLI portion with gini coefficient pre-computed (from 0 to 1)
High gini means unambiguous inference.
@inproceedings{xzhou2022distnli,
Author = {Xiang Zhou and Yixin Nie and Mohit Bansal},
Booktitle = {Findings of the Association for Computational Linguistics: ACL 2022},
Publisher = {Association for Computational Linguistics},
Title = {Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning},
Year = {2022}
}