datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Rosetta-Activations
Rosetta Activations
Updated: 2026-06-15 02:30 UTC
Contrastive activation extractions for 17 semantic concepts across 46 language models,
supporting cross-architecture mechanistic interpretability research.
Companion concept pair corpus: jamesrahenry/Rosetta_Concept_Pairs
Papers: forthcoming
Dataset Structure
Rosetta-Activations/
├── rcp_v1/ # Current extraction line — richest data (N≈2000)
│ └── {Model_Name}/
│ ├── calibration_{concept}.npy… See the full description on the dataset page: https://huggingface.co/datasets/james-ra-henry/Rosetta-Activations.debugtest3
requests
Open LLM Leaderboard Requests
This repository contains the request files of models that have been submitted to the Open LLM Leaderboard.
You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often)
Evaluation Methodology
The evaluation process involves running your models against several benchmarks from… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/requests.reclorhttps://whyu.me/reclor/
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
hh-rlhf
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.tests-raw-jsonlsynthesized_datasetgsm-hard
Dataset Summary
This is the harder version of gsm8k math reasoning dataset (https://huggingface.co/datasets/gsm8k).
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning
Languages
English - Numbers
Dataset Structure
dataset = load_dataset("reasoning-machines/gsm-hard")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-machines/gsm-hard.ResearchClawBench
ResearchClawBench
Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery
Quick Start | Submit Tasks | How It Works | Domains | Leaderboard | Add Your Agent
ResearchClawBench is a benchmark that measures whether AI coding agents can independently conduct scientific research — from reading raw data to producing publication-quality reports — and then rigorously evaluates the results against real human-authored papers.… See the full description on the dataset page: https://huggingface.co/datasets/InternScience/ResearchClawBench.soc-ratchakitcha
Royal Gazette Thailand (Ratchakitcha) Dataset
ชุดข้อมูลราชกิจจานุเบกษา (แบบ Machine Readable)
โครงการ Open Law Data Thailand ร่วมกับคณะกรรมาธิการการพาณิชย์และการอุตสาหกรรม วุฒิสภา ได้รับความอนุเคราะห์ข้อมูลจาก สำนักเลขาธิการคณะรัฐมนตรี (สลค.) เพื่อเผยแพร่ข้อมูลกฎหมายไทยสู่สาธารณะในรูปแบบที่ประมวลผลได้ด้วยคอมพิวเตอร์ (Machine Readable) เพื่อส่งเสริมนวัตกรรม Legal Tech และ AI ของประเทศไทย
Dataset Description
ชุดข้อมูลนี้รวบรวมรายการประกาศในราชกิจจานุเบกษา… See the full description on the dataset page: https://huggingface.co/datasets/open-law-data-thailand/soc-ratchakitcha.raw_jsonlmedical-o1-reasoning-SFT
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.real-toxicity-prompts
Dataset Card for Real Toxicity Prompts
Dataset Summary
RealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.
Languages
English
Dataset Structure
Data Instances
Each instance represents a prompt and its metadata:
{
"filename":"0766186-bc7f2a64cb271f5f56cf6f25570cd9ed.txt",
"begin":340,
"end":564,
"challenging":false… See the full description on the dataset page: https://huggingface.co/datasets/allenai/real-toxicity-prompts.robovqaFlashRAG_datasets
⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.
For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.leaderboard-requestsxfield-radar-dataset-20260915
XField radar dataset — formal snapshot, 2026-09-15
Upload status: COMPLETE — every shard verified against its remote SHA-256 and byte size. See UPLOAD_COMPLETE.json.
This public research snapshot preserves the currently admitted XField/GRT simulation dataset: radar inputs, existing GT, available raw sensor products, provenance, and the frozen N141 train/validation split. It is not a claim that historical labels meet the newly repaired independent dense-GT pipeline.… See the full description on the dataset page: https://huggingface.co/datasets/KAS2003/xfield-radar-dataset-20260915.yayi2_pretrain_data
介绍/Introduction
本数据集源自雅意训练语料，我们精选了约100B数据，数据大小约为500GB。我们期望通过雅意预训练数据的开源推动中文预训练大模型开源社区的发展，并积极为此贡献力量。通过开源，我们与每一位合作伙伴共同构建雅意大模型生态。
We opensource the pre-trained dataset in this release, it should contain more than 100B tokens depending on the tokenizer you use, requiring more than 500GB of local storage. By open-sourcing the pre-trained dataset, we aim to contribute to the development of the Chinese pre-trained large language model open-source community. Through open-source, we aspire to… See the full description on the dataset page: https://huggingface.co/datasets/wenge-research/yayi2_pretrain_data.RoadmapBench
RoadmapBench
A benchmark for evaluating AI coding agents on multi-target, long-horizon software development tasks derived from open-source project version upgrades.
Overview
RoadmapBench contains 115 tasks spanning 17 open-source repositories across 5 programming languages (Python, TypeScript, Go, Rust, C++). Each task requires an agent to implement multiple interdependent features that correspond to a real version upgrade of the target project.
Quick Start… See the full description on the dataset page: https://huggingface.co/datasets/UnipatAI/RoadmapBench.RoboPulsePlusPlus
RoboPulse++
RoboPulse++ is an interval-level benchmark introduced in PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment for evaluating progress judge models throughout complete robot manipulation trajectories.
This Hugging Face release contains 700 episodes with natural-language task instructions, temporally ordered observations, and human-annotated progress intervals.
Overview
RoboPulse++ extends progress judging from isolated state pairs to temporal… See the full description on the dataset page: https://huggingface.co/datasets/lyy0715/RoboPulsePlusPlus.RoboInter-Data
RoboInter-Data: Intermediate Representation Annotations for Robot Manipulation
Rich, dense, per-frame intermediate representation annotations for robot manipulation, built on top of DROID and RH20T. Developed as part of the RoboInter project. You can try our Online demo.
The annotations cover 230k episodes and include: subtasks,
primitive skills, segmentation, gripper/object bounding boxes, placement proposals, affordance boxes,
grasp poses, traces, contact points, etc. And each… See the full description on the dataset page: https://huggingface.co/datasets/InternRobotics/RoboInter-Data.RegexEval
Dataset Card for RegexEval
Re(gEx|DoS)Eval is a framework that includes a dataset of 762 regex descriptions (prompts) from real users, refined prompts with examples, and a robust set of tests.
Dataset Details
Dataset Sources
Repository: https://github.com/s2e-lab/RegexEval
Paper: https://s2e-lab.github.io/preprints/icse_nier24-preprint.pdf
Dataset Structure
dataset.jsonl: dataset file in jsonl format. Every line contains a JSON object with… See the full description on the dataset page: https://huggingface.co/datasets/s2e-lab/RegexEval.needle-resultsarena-resultsThis dataset contains the saved results from MTEB-Arena
knowref_60k_raw
The Knowref 60K Dataset
Project: https://github.com/aemami1/KnowRef60k
Data source: https://github.com/aemami1/KnowRef60k/tree/28e5385d17967744ccb3bdba45fdd89d9690307d
Fields
annotation_strength (str): annotator agreement from 1-5
candidate_0 (str): the first candidate name
candidate_1 (str): the second candidate name
original_sentence (str): sentence before swapping the names
swapped_sentence (str): sentence after swapping the names with square brackets marking the… See the full description on the dataset page: https://huggingface.co/datasets/coref-data/knowref_60k_raw.RWKU
Dataset Card for Real-World Knowledge Unlearning Benchmark (RWKU)
Dataset Summary
RWKU is a real-world knowledge unlearning benchmark specifically designed for large language models (LLMs).
This benchmark contains 200 real-world unlearning targets and 13,131 multi-level forget probes, including 3,268 fill-in-the-blank probes, 2,879 question-answer probes, and 6,984 adversarial-attack probes.
RWKU is designed based on the following three key factors:
For the task setting… See the full description on the dataset page: https://huggingface.co/datasets/jinzhuoran/RWKU.social-sim-bench-genssoc-builder-rtl-v1
SoC Builder RTL Dataset — v1 (Experiment Release)
A reproducible, machine-generated corpus of synthesizable System-on-Chip (SoC) RTL designs for machine learning on hardware: RTL representation learning today, and — as the corpus grows — netlist, timing, and placement prediction. Every design is a complete, hierarchical, lint-clean Verilog SoC assembled from real open-source IP — RISC-V CPU cores, network-on-chip (NoC) interconnects, accelerators, peripherals, memories and… See the full description on the dataset page: https://huggingface.co/datasets/hasankursun/soc-builder-rtl-v1.Evo-BenchEvo-Bench: Can Language Models Improve Agent Harness?
A benchmark for measuring the intrinsic harness-evolving capability of language models.
Overview of the Evo-Bench evaluation pipeline.
✨ Highlights
608 harness-sensitive tasks from five established benchmarks, spanning
Search, Office, and General agent domains with disjoint validation and
evaluation suites.
Harness-guided benchmark construction selects tasks that respond to
harness improvements… See the full description on the dataset page: https://huggingface.co/datasets/RUC-AIBOX/Evo-Bench.rlvr-reward-hacking-scale-no-conftest-20260909-completion
Matched no-conftest RLVR study 20260909-completion
Lossless research records, grouped by model and trajectory type. Only the listed
configurations have published records. Canary diagnostics are excluded from study
estimates; run status in provenance distinguishes retired diagnostics from active
or completed training. Valid failures, refusals and truncations are retained.
The train split name is a dataset-loader convention; record_type identifies
whether a record is training… See the full description on the dataset page: https://huggingface.co/datasets/lucabaroni/rlvr-reward-hacking-scale-no-conftest-20260909-completion.