datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
iclr-wm-backup-public
ICLR Watermark Benchmark — backup overflow (public part)
Companion to the private repo Aak975/iclr-wm-backup, which reached its
storage quota. Together the two repos form ONE backup — every file exists in
exactly one of them, with the same layout:
archives/<sub>/part-0000 ... part-NNNN, MANIFEST.json
restore one archive: cat part-* | zstd -d | tar -x
MANIFEST.json = {"parts": N, "sha256": <whole-stream>, "total_bytes": M}
This public part holds only shareable image data… See the full description on the dataset page: https://huggingface.co/datasets/Aak975/iclr-wm-backup-public.Magicoder-OSS-Instruct-75KThis is the OSS-Instruct dataset generated by gpt-3.5-turbo-1106 developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
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.Aiice
Dataset
Aiice benchmark dataset for Arctic sea ice concentration (SIC) forecasting,
based on OSI-SAF satellite products (CC BY 4.0).
Coverage
Period: October 1978 – April 2026
Resolution: 25 km spatial, daily temporal
Grid: 432×432 (Lambert Azimuthal Equal Area, EPSG:6931)
Source products
Product
Source
Period
OSI-450-a
SMMR, SSM/I, SSMIS
1978–2020
OSI-430-a
SSMIS
2021–Jul 2025
OSI-438
AMSR2
Jul 2025–present… See the full description on the dataset page: https://huggingface.co/datasets/ITMO-NSS/Aiice.Long-Horizon-Terminal-Bench
Long-Horizon Terminal-Bench (LHTB)
LHTB is a 46-task benchmark for measuring how well LLM agents sustain useful
work in a containerized terminal over hundreds of steps. Unlike short-horizon
coding benchmarks where an agent writes one artifact and stops, LHTB drops the agent
into a stateful environment and grades it with hidden, rebuild-from-artifact
verifiers — self-reported progress does not count.
📝 Blog: https://zli12321.github.io/LHTB/
🏆 Leaderboard:… See the full description on the dataset page: https://huggingface.co/datasets/IntelligenceLab/Long-Horizon-Terminal-Bench.ipo-text
SEC IPO Filings Dataset
A large-scale, comprehensive dataset of 100,000+ filings (S-1 and F-1 filings) filed with the SEC EDGAR system, spanning 1994–2026 and over 20,000 unique registrants.
Every filing has been downloaded and then parsed using the IPO-Mine Python Package. We have extracted three common sections found in these documents (Prospectus Summary, Risk Factors, Legal Matters), and then used an LLM classifier to group them into three categories. For this dataset, we have… See the full description on the dataset page: https://huggingface.co/datasets/gtfintechlab/ipo-text.InternData-fractal20220817_datairish_fineweb_eduData translation project of https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu, sample-10BT subset. Data are translated from English to Irish using NLLB-3.3B.
kernelbench-mega-traces
KernelBench-Mega agent traces
Coding agents writing full GPU megakernels across Blackwell / H100 / B200, scored as speedup over reference; contamination-audited (23 verified cells).
Each .jsonl file is one agent run in Claude-Code session format, viewable with the agent trace viewer. Filename = run id; manifest.csv maps each run to model / harness / problem / GPU / score.
23 agent traces · live leaderboard: https://kernelbench.com/mega
Secrets redacted. Full reasoning for… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-mega-traces.kernelbench-hard-traces
KernelBench-Hard agent traces
Frontier coding agents writing optimized CUDA/Triton kernels (FP8 GEMM, paged
attention, MoE, W4A16, KDA, Top-k) on RTX PRO 6000 Blackwell, H100 PCIe, and
B200; roofline-graded.
Each .jsonl file is one agent run in Claude-Code session format, viewable with
the Hugging Face Agent Trace viewer (Data Studio → open a row). Filename =
run id.
Live leaderboard: https://kernelbench.com/hard
Secrets redacted. Full reasoning for open-provider routes… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-hard-traces.ine-catalog
INE
Este repositorio contiene todas las tablas¹ del Instituto Nacional de Estadística exportadas a ficheros Parquet.
Puedes encontrar cualquiera de las tablas o sus metadatos en la carpeta tablas.
Cada tabla está identificado un una ID. Puedes encontrar la ID de la tabla tanto en el INE (es el número que aparece en la URL) or en el archivo tablas.jsonl de este repositorio que puedes explorar en el Data Viewer.
Por ejemplo, la tabla de Índices nacionales de clases se corresponde… See the full description on the dataset page: https://huggingface.co/datasets/datania/ine-catalog.appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3953125
Action score: 0.446875
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41328125
Action score: 0.4359375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41953125
Action score: 0.4515625
Valid samples: 320/320
imagenet_hard_review_data_r2Inter-Edit-Train
Inter-Edit-Train
Inter-Edit-Train is the official large-scale training set released for the CVPR 2026 paper Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing.
This dataset is designed for the Interactive Instruction-based Image Editing (I^3E) task, where a model performs localized image edits from a concise textual instruction together with imprecise spatial guidance.
Highlights
1,099,964 image editing pairs
610,186 unique source images
Four… See the full description on the dataset page: https://huggingface.co/datasets/a1557811266/Inter-Edit-Train.nuclear-intelligence-dataset
Nuclear Intelligence Dataset
Public, auto-generated dataset of validated nuclear-energy research cycles.
Latest stats (auto-updated):
🪙 NES tokens minted: 0
⛓️ Blockchain length: 1 blocks
🕸️ Knowledge entities: 2
Source
GitHub: https://github.com/QalamHipHop/nuclear-intelligence
HF Space: https://huggingface.co/spaces/Qalam/Nuclear-Intelligence
License
MIT
ImageEval-ArabicNLP26
ImageEval-ArabicNLP26 👁️
ImageEval-ArabicNLP26 is the dataset of the ImageEval 2026 Shared Task at ArabicNLP 2026.
It covers both of the shared task's tasks: AynVQA (Task 1), a culturally grounded Arabic multimodal benchmark for spoken visual question answering and hallucination detection, and CRAI-Bench (Task 2), which evaluates the cultural accuracy of Arabic text-to-image generation.
The shared task has concluded. All gold labels are released, including the blind test splits… See the full description on the dataset page: https://huggingface.co/datasets/QCRI/ImageEval-ArabicNLP26.swe_jsts_initprlarge100ine
INE
Este repositorio contiene todas las tablas¹ del Instituto Nacional de Estadística exportadas a ficheros Parquet.
Puedes encontrar cualquiera de las tablas o sus metadatos en la carpeta tablas.
Cada tabla está identificado un una ID. Puedes encontrar la ID de la tabla tanto en el INE (es el número que aparece en la URL) or en el archivo tablas.jsonl de este repositorio que puedes explorar en el Data Viewer.
Por ejemplo, la tabla de Índices nacionales de clases se corresponde al… See the full description on the dataset page: https://huggingface.co/datasets/davidgasquez/ine.kernelbench-cuda-tracesdata-product-benchmark
DPDisc Dataset
Paper | Code
Dataset Description
This dataset provides a benchmark for automatic data product creation. The task is framed as follows: given a natural language data product request and a corpus of text and tables, the objective is to identify the relevant tables and text documents that should be included in the resulting data product which would useful to the given data product request. The benchmark brings together three variants: HybridQA, TAT-QA, and… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/data-product-benchmark.glenans-isobars-archivegithub-issues
Dataset Card for GitHub Issues
Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
Supported Tasks and Leaderboards
For each of the tasks tagged… See the full description on the dataset page: https://huggingface.co/datasets/lewtun/github-issues.gspc-human-labour-index
GSPC — labour components facts (Eurostat)
SWIFT census (live): https://councilof.ai/api/swift
XRPL reader (live): https://councilof.ai/api/xrpl
Live axis name: labour-components — MEASURED as two labour series (deterministic-facts, n=2). Not an index. No composite. C-2026-0826-05: do not restore MEASURED-INDEX-v0.1.
Legacy Hub slug kept for inbound links. Cite the live axis name. Empty cells that the GET does not fill stay empty.
Council of AI · CSOAI Ltd (GB, Companies House… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-human-labour-index.hack-ignition-benchmark
hack-ignition benchmark — data, v0.1.6
Training trajectories of reinforcement-learning runs on exploitable graders, for studying and predicting when RL
comes to produce exploits. Each family is a set of GRPO runs over configurations of (start model, prompt,
training set, grader / reward structure, recipe), with one or more seeds per configuration. Every family stores
what its training logs contain — per-step exploit, task and reward rates, the item × step exploit record… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/hack-ignition-benchmark.aerial-isac-srs-iq
Aerial ISAC SRS I/Q
Raw uplink Sounding Reference Signal (SRS) I/Q captured on the
NVIDIA Aerial
5G testbed, paired with a synchronized video and camera-derived ground truth for
two pedestrians and a car moving through the sensing area.
The labeled span in real time: camera view, Range-Doppler map, and range/velocity
tracks. Also available as isac_rd_demo.mp4.
Dataset Description:
This dataset provides synchronized multi-modal recordings designed for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/aerial-isac-srs-iq.gspc-humanoid-labour-index
GSPC — humanoid labour index facts (Disclosure)
SWIFT census (live): https://councilof.ai/api/swift
XRPL reader (live): https://councilof.ai/api/xrpl
MEASURED financial/domain axis as disclosure facts on 8 frozen URLs (n=8). Not a model leaderboard. No accuracy, no fleet, no leader. Cells the live GET leaves empty stay empty — never invent hours/incidents.
Live status is the humanoid-labour-index row on GET https://councilof.ai/api/gspc. Not a certificate.
Council of AI ·… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-humanoid-labour-index.gspc-distribution-integrity
GSPC — distribution integrity facts (DistributionFacts)
SWIFT census (live): https://councilof.ai/api/swift
XRPL reader (live): https://councilof.ai/api/xrpl
MEASURED financial/domain axis (deterministic-facts from GET https://councilof.ai/api/xrpl, writes_board=false, n=16). Not a model leaderboard. No accuracy, no fleet, no leader.
Live status is the distribution-integrity row on GET https://councilof.ai/api/gspc. Not a certificate.
Council of AI · CSOAI Ltd (GB, Companies… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-distribution-integrity.vlm-info-loss-results
VLM Grounding Evaluation Results
Grounding evaluation results for vision-language models on robotics manipulation datasets.
Part of the vlm-info-loss project studying
how VLM connectors transform visual representations.
Background
Our embedding-level analysis shows VLM connectors perform a compress-then-expand transformation:
they sharpen dominant-object representations while compressing secondary-object category identity.
All tested models converge to ~83%… See the full description on the dataset page: https://huggingface.co/datasets/MicroAGI-Labs/vlm-info-loss-results.