datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FineFineWeb-sample
FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus
arXiv: Coming Soon
Project Page: Coming Soon
Blog: Coming Soon
Data Statistics
Domain (#tokens/#samples)
Iteration 1 Tokens
Iteration 2 Tokens
Iteration 3 Tokens
Total Tokens
Iteration 1 Count
Iteration 2 Count
Iteration 3 Count
Total Count
aerospace
5.77B
261.63M
309.33M
6.34B
9100000
688505
611034
10399539
agronomy
13.08B
947.41M
229.04M
14.26B
15752828
2711790
649404
19114022
artistic… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/FineFineWeb-sample.the-stack-smol
Dataset Description
A small subset (~0.1%) of the-stack dataset, each programming language has 10,000 random samples from the original dataset. The dataset has 2.6GB of text (code).
Languages
The dataset contains 30 programming languages:
"assembly", "batchfile", "c++", "c", "c-sharp", "cmake", "css", "dockerfile", "fortran", "go", "haskell", "html", "java",
"javascript", "julia", "lua", "makefile", "markdown", "perl", "php", "powershell", "python", "ruby", "rust"… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-smol.cadgenbench-submissions
CADGenBench Submissions
Submissions and evaluation results for the CADGenBench leaderboard.
Leaderboard Space: HuggingAI4Engineering/CADGenBench.
Benchmark code: github.com/huggingface/cadgenbench.
Fixture inputs: HuggingAI4Engineering/cadgenbench-data.
Ground truth: HuggingAI4Engineering/cadgenbench-data-gt (private).
Contents
Path
What it is
results.jsonl
One row per submitted + evaluated submission. The leaderboard table reads from this.… See the full description on the dataset page: https://huggingface.co/datasets/HuggingAI4Engineering/cadgenbench-submissions.security-auditsA collection of agent traces generated with Swival (not Claude Code, despite what the HF interface currently shows), an agent designed for open-source models.
These traces focus on security audits of opensource software.
Sharing traces with Swival
Swival can export full conversation traces with --trace-dir, which writes one <session_id>.jsonl file per session:
swival "Fix the login bug" --trace-dir traces/
Those JSONL files use Swival's Claude Code compatible trace export, and… See the full description on the dataset page: https://huggingface.co/datasets/jedisct1/security-audits.PKU-SafeRLHF
Dataset Card for PKU-SafeRLHF
Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members.
[🏠 Homepage] [🤗 Single Dimension Preference Dataset] [🤗 Q-A Dataset] [🤗 Prompt Dataset]
Citation
If PKU-SafeRLHF has contributed to your work, please consider citing… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF.GUIGuard-Bench
GUIGuard-Bench (Public Ladder)
GUIGuard-Bench is a cross-platform GUI agent benchmark for studying privacy risks and privacy-preserving execution in multimodal GUI agents.
This public-ladder release contains 121 GUI interaction trajectories (68 Android + 53 PC) for benchmark evaluation, with 26,407 region-level privacy annotations across 2,002 screenshots.
For the anonymous review version of the evaluation toolkit, see GUIGaurd-Bench-CA4F.
Dataset Summary
GUI agents… See the full description on the dataset page: https://huggingface.co/datasets/ShaofantuoshuzhengzhiSha/GUIGuard-Bench.equational-theories-selected-problems
Equational Theories Selected Problems
Update (September 11, 2026)
This dataset was updated on September 11, 2026.
Main changes:
released the official Stage 2 evaluation problems: stage2_evaluation_main (200 problems; ground truth withheld — answer is null until Stage 2 concludes) and stage2_evaluation_research (100 order-5 research problems with no ground truth)
added metadata/stage2_evaluation_main.json and metadata/stage2_evaluation_research.json… See the full description on the dataset page: https://huggingface.co/datasets/SAIRfoundation/equational-theories-selected-problems.swallow-code-v2
SwallowCode-v2
Resources
📑 arXiv: Read our paper for detailed methodology and results at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowMath-v2, our companion dataset for mathematical reasoning.
💻 What is it?
SwallowCode-v1 was a high-quality Python code dataset generated through an LLM-based rewriting pipeline.
However, it had two significant limitations:
(1) it was distributed under the Llama 3.3 Community License, and
(2) its size was limited to… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-code-v2.ShareGPT-4osoc-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.FrontierOR
Frontier-OR Benchmark
A benchmark of 180 literature-grounded OR tasks, each packaged as a
self-contained reproducible unit: natural-language problem description,
mathematical formulation, reference Gurobi implementation, test instances,
reference solutions, and an automated feasibility checker.
Designed for evaluating LLMs on the end-to-end task of turning a research
paper's OR problem into runnable, verifiably-correct optimization code.
Dataset size note
This… See the full description on the dataset page: https://huggingface.co/datasets/SmartOR/FrontierOR.SHP
🚢 Stanford Human Preferences Dataset (SHP)
If you mention this dataset in a paper, please cite the paper: Understanding Dataset Difficulty with V-Usable Information (ICML 2022).
Summary
SHP is a dataset of 385K collective human preferences over responses to questions/instructions in 18 different subject areas, from cooking to legal advice.
The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/SHP.the-stack-smol-xl
Dataset Description
A small subset of the-stack dataset, with 87 programming languages, each has 10,000 random samples from the original dataset.
Languages
The dataset contains 87 programming languages:
'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c',
'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir',
'elm', 'emacs-lisp','erlang'… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-smol-xl.GSM-Symbolic
GSM-Symbolic
This project accompanies the research paper, GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.
Getting Started
In our first release, we provide data for GSM-Symbolic, GSM-Symbolic-P1, and GSM-Symbolic-P2 variants. For each variant, we have released both templates (available on Github), and a sample of generated data that can be used for evaluation.
To load the data, you can use the following code. Note that in… See the full description on the dataset page: https://huggingface.co/datasets/apple/GSM-Symbolic.video-quality-scored
Image-to-Video Quality-Scored Clips
A collection of prompted image-to-video samples with quality-evaluation metadata.
Each sample pairs a first frame (the I2V conditioning image) with one or both
of:
a generated video produced by a video model from the first frame + prompt
an original clip (the reference/source video the prompt was authored around)
A subset of the samples also carry per-clip quality scores: an overall
quality_score, six per-aspect breakdowns… See the full description on the dataset page: https://huggingface.co/datasets/mohantesting/video-quality-scored.stackv2_edu_filtered
Stack V2 Edu
Description
We filter the Stack V2 to only include code from openly licensed repositories, based on the license detection performed by the creators of Stack V2. When multiple licenses are detected in a single repository, we ensure that all of the licenses are on the Blue Oak Council certified license list. Per-document license information is available in the license entry of the metadata field of each example. Code for collecting, processing, and preparing… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/stackv2_edu_filtered.SparseVideoNav
SparseVideoNav Datasets
This repository contains the real-world navigation datasets released with OpenDriveLab/SparseVideoNav:
BVN: Beyond-the-View Navigation.
IFN: Instruction-Following Navigation.
Project links:
Project page: https://opendrivelab.com/SparseVideoNav
GitHub: https://github.com/OpenDriveLab/SparseVideoNav
Paper: https://arxiv.org/abs/2602.05827
Dataset Summary
SparseVideoNav studies real-world vision-language navigation with sparse future… See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab/SparseVideoNav.enron_spamThis is a version of the Enron Spam Email Dataset, containing emails (subject + message) and a label whether it is spam or ham.
hplt2_edu_scores
HPLT2-Edu-scores
Dataset summary
HPLT2-JQL-Education is a model-annotated language subset of HPLT2, spanning 35 languages.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction.
The original FW2 heuristic filtering method serves as our baseline, providing reference points for both the volume of retained tokens and downstream model performance.
For example, in the Spanish language case… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/hplt2_edu_scores.StreamArena
StreamArena
A benchmark for always-on, streaming video QA agents.
The user watches a long video (52–134 min) with an AI assistant.
The assistant must answer questions in real time, sometimes based on the current frame,
sometimes by recalling earlier content, sometimes by calling external tools,
and sometimes by proactively waiting for a future moment to speak up.
Overview of StreamArena. Top row: (a) source domains, (b) task types, (c) video durations, (d) temporal gaps… See the full description on the dataset page: https://huggingface.co/datasets/hkuzxc/StreamArena.SlimPajama-Meta-rater
Annotated SlimPajama Dataset
Dataset Description
This dataset contains the first fully annotated SlimPajama dataset with comprehensive quality metrics for data-centric large language model research. The dataset includes approximately 580 billion tokens from the training set of the original SlimPajama dataset, annotated across 25 different quality dimensions.
Note: This dataset contains only the training set portion of the original SlimPajama dataset, which is why the… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/SlimPajama-Meta-rater.S-EMBER
S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval
Episodic-memory video QA benchmark (face-blurred, audio-removed).
License & usage
This dataset is licensed under
CC BY-NC 4.0 and is provided
for non-commercial research use only. Access is gated: you must accept the
non-commercial terms above before downloading.
Contents
sember_mcq.jsonl — multiple-choice evaluation split.
sember_grounding.jsonl — answer-generation and… See the full description on the dataset page: https://huggingface.co/datasets/facebook/S-EMBER.rte
Glue RTE
This dataset is a port of the official rte dataset on the Hub.
Note that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
kiiteitte
Kiiteitte history
Kiiteitte が収集した、今までの選曲履歴。
1時間おきに更新されます。
型
{
// 動画ID
"video_id": "sm44670499",
// タイトル
"title": "library->w4nderers / 足立レイ、つくよみちゃん",
// 投稿者
"author": "名無し｡",
// サムネイルのURL
"thumbnail": "https://nicovideo.cdn.nimg.jp/thumbnails/44670499/44670499.91820835",
// 選曲日時
"date": "2025-02-22 12:51:51",
// 新しく増えたお気に入り数。不明の場合は null
"new_faves": 5,
// 回ったユーザーの数。不明の場合は null
"spins": 13,
// イチ押しリストのユーザーのURL。イチ押しリスト以外から選曲された場合は null… See the full description on the dataset page: https://huggingface.co/datasets/sevenc-nanashi/kiiteitte.qwen35-4b
qwen35-4b
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: 0.38203125
Action score: 0.4375
Valid samples: 320/320
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
total-300-random-jh-epoch4
total-300-random-jh-epoch4
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: 0.3890625
Action score: 0.440625
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4
total-300-lambda02-s_signal_type6-jh-epoch4
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: 0.4046875
Action score: 0.4140625
Valid samples: 320/320
total-300-lambda00-s_signal_type6-jh-epoch4
total-300-lambda00-s_signal_type6-jh-epoch4
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: 0.3875
Action score: 0.43125
Valid samples: 320/320
total-300-lambda05-s_signal_type6-jh-epoch4
total-300-lambda05-s_signal_type6-jh-epoch4
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: 0.35703125
Action score: 0.4375
Valid samples: 320/320