PandaBench PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies. The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges. Dataset Description This repository contains the benchmark results from extensive evaluations of various… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.

datagen-clutter-v1-joint-5cam Auto-generated SFT dataset for the clutter (pick-out-of-clutter → place-in-goal) family — a cuRobo-planned, physics- & LTL-safety-checked demonstration set, already converted to LeRobot v2.1. Creator: yypeng666 (IDEAS-Lab-Northwestern) Source bench: IDEAS-Lab-Northwestern/ManiGuard-Bench — collected on all 55 clutter_pickup base tasks. Per task: 40 success + LTL-safe trajectories → 2,200 episodes total. Contents… See the full description on the dataset page: https://huggingface.co/datasets/IDEAS-Lab-Northwestern/datagen-clutter-v1-joint-5cam.

ZhoBLIMP Dataset This is the Chinese version of the BLIMP (Benchmark of Linguistic Minimal Pairs) dataset. Dataset Structure The dataset contains 118 subtasks, each testing different linguistic phenomena in Chinese. Usage from datasets import load_dataset # Load a specific subtask dataset = load_dataset("your_username/zhoblimp", "BA_verb_le_b") # Load all configs all_configs = load_dataset("your_username/zhoblimp", "all") Subtasks… See the full description on the dataset page: https://huggingface.co/datasets/Junrui1202/zhoblimp.

nvidia-OpenCodeInstruct-refined A strictly quality-filtered subset of nvidia/OpenCodeInstruct (5M examples). This is a strict subset of EER6/nvidia-OpenCodeInstruct-broad. Filtering criteria Both conditions must be satisfied: Criterion Threshold LLM judge min score = 5 (out of 5) Unit test pass rate (average_test_score) = 1.0 LLM judge min score is the minimum across all three dimensions in the llm_judgement field: requirement_conformance — does… See the full description on the dataset page: https://huggingface.co/datasets/EER6/nvidia-OpenCodeInstruct-refined.

CREST forcing (parquet) EF5/CREST hourly forcing for CONUS, 2016-present, packed as one tar per variable/year. dir variable source cadence mrms/ precipitation MRMS QPE (corrected) hourly temp/ 2 m temperature NLDAS-2 FORA hourly pet/ potential ET FEWS NET daily PET daily Each *.tar expands to individual .pqf (Apache Arrow parquet) grids readable by the EF5 v4.5 native parquet reader. Used by the Space vincewin/CREST_AI. Download + extract one year, e.g.: from… See the full description on the dataset page: https://huggingface.co/datasets/vincewin/CREST_data.