datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rule-ling-conceptsrulerMassiveDS-140BWe release the raw passages, embeddings, and index of MassiveDS.
Website: https://retrievalscaling.github.io
We release two versions of MassiveDS:
MassiveDS-1.4T, which contains 1.4T tokens in the datastore.
MassiveDS-140B, which is a subsampled version containing 140B tokens in the datastore.
File structure:
raw_data: plain data in JSONL files.
passages: chunked raw passages with passage IDs. Each passage is chunked to have no more than 256 words.
embeddings: embeddings of the passages… See the full description on the dataset page: https://huggingface.co/datasets/rulins/MassiveDS-140B.ru-llm-judge-dataset
RU-LLM-Judge-Dataset
Датасет собирается инкрементально в течение нескольких сессий бесплатного Google Colab.
Текущий объём: 18,821 суждений (по состоянию на последний запуск).
Прогресс к цели (5,000 суждений)
[████████████████████] 100% (18,821 / 5,000)
История сессий сбора
Сессия
Дата
Добавлено
Итого
1
2026-08-05 08:42
617
617
2
2026-08-06 14:40
583
1,200
3
2026-08-07 19:20
486
1,686
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2026-08-08 22:34
868
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2026-08-13… See the full description on the dataset page: https://huggingface.co/datasets/AtesiT/ru-llm-judge-dataset.MassiveDS-1.4T-raw-dataWe release the raw passages, embeddings, and index of MassiveDS.
Website: https://retrievalscaling.github.io
Versions
We release two versions of MassiveDS:
MassiveDS-1.4T, which contains the embeddings and passages of the 1.4T-token datastore.
MassiveDS-1.4T-raw-text, contains the raw text of the 1.4T-token datastore.
MassiveDS-140B, which contains the index, embeddings, passages, and raw text of a subsampled version containing 140B tokens in the datastore.
Note:
Code support to… See the full description on the dataset page: https://huggingface.co/datasets/rulins/MassiveDS-1.4T-raw-data.MasssiveDS-1.4T-raw-dataruletaker
Dataset Card for "ruletaker"
https://github.com/allenai/ruletaker
@inproceedings{ruletaker2020,
title = {Transformers as Soft Reasoners over Language},
author = {Clark, Peter and Tafjord, Oyvind and Richardson, Kyle},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere}… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/ruletaker.ATO-Australian-Tax-Rulings-and-Guidance
ATO Rulings & Guidance — Australian Tax Law, Structured for AI
67,000+ Australian Taxation Office documents as RAG-ready NDJSON/CSV — Edited Private Advice, public rulings and determinations, ATO Interpretative Decisions, practical compliance guidelines, taxpayer alerts, decision impact statements, practice statements and legislative instruments. Every document parsed into structured, typed fields for legal RAG, LLM fine-tuning, and tax research automation.
Machine-readable… See the full description on the dataset page: https://huggingface.co/datasets/simplelex/ATO-Australian-Tax-Rulings-and-Guidance.ruleforge-checkpointsmodel-inference-activationsRuleMaze
RuleMaze Dataset
RuleMaze is a dataset for studying rule-compliant visual spatial planning in Multimodal Large Language Models (MLLMs).
Each task contains a visual maze together with natural-language rules. Models must understand the environment and rules and generate a valid multi-step trajectory.
Dataset Content
RuleMaze contains two types of visual environments:
Regular: grid-based visual maze environments
Quest: adventure-style environments with richer visual… See the full description on the dataset page: https://huggingface.co/datasets/Fish-03/RuleMaze.RULER-8192-Qwen2.5-3B-tokenizerOmni-MATH-RuleRule-VLN
Rule-VLN Dataset
Rule-VLN is a rule-compliant outdoor vision-and-language navigation benchmark built on the Touchdown / StreetLearn urban navigation environment. It studies whether navigation agents can follow language instructions while also complying with semantic traffic rules, such as regulatory signs that prohibit otherwise reachable movements.
This dataset accompanies the paper:
Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric… See the full description on the dataset page: https://huggingface.co/datasets/jeffry77/Rule-VLN.RULER-BenchRULER-Bench: Probing Rule-based Reasoning Abilities of Next-level Video Generation Models for Vision Foundation Intelligence
📢 News
[2025-12-19] We have released the Evaluation Code !
[2025-12-03] We have released the Paper, Project Page, and Dataset !
📋 TODOs
Release paper
Release dataset
Release evaluation code
🧩Overview of RULER-Bench
We propose RULER-Bench, a comprehensive benchmark designed to evaluate the… See the full description on the dataset page: https://huggingface.co/datasets/hexmSeeU/RULER-Bench.att-hub-ruler-32kRULER-llama3-1M
RULER-Llama3-1M
A 1M token version of the RULER dataset based on the Llama-3 chat template.
It is automatically generated based on the scripts available in the RULER repository: https://github.com/NVIDIA/RULER. It is designed for evaluating the performance of Long Language Models (LLMs) on various tasks with varying sequence lengths.
How to Use
from datasets import load_dataset
LENGTH_IN_STRING = ['4k', '8k', '16k', '32k', '64k', '128k', '256k', '512k', '1M']
TASKS =… See the full description on the dataset page: https://huggingface.co/datasets/self-long/RULER-llama3-1M.ruler-300-seed42
Frozen RULER 300, seed 42
This dataset freezes the exact RULER inputs used by the
short-long-pretraining native evaluation suite.
Repository: bicycleman15/ruler-300-seed42
Rows: 6,300
Tasks: s-niah-1, s-niah-2, s-niah-3, mk1, mk2, mv, mq
Context lengths: 1024, 2048, 4096
Samples per task/length: 300
Seed: 42
Dataset SHA-256: 4d82df6f9b1f2d9c45c0a0bda8c734032e62f517b746c6351bf9c2f38335ab3d
Tokenizer SHA-256: 1f186971e25f7bda3dd6f93a100bb8fa2a6801cf8dc3807c8a8c4e45f296ab90… See the full description on the dataset page: https://huggingface.co/datasets/bicycleman15/ruler-300-seed42.rulibrispeech
Dataset Card for "rulibrispeech"
More Information needed
rule34_full
Rule34 Full Dataset
This is the full dataset of rule34.xxx. And all the original images are maintained here.
Information
Images
There are 11336807 images in total. The maximum ID of these images is 13078768. Last updated at 2025-04-10 21:23:24 JST.
These are the information of recent 50 images:
id
filename
width
height
mimetype
tags
file_size
file_url
13078768
13078768.jpeg
1024
1024
image/jpeg
1boy 1girls ai_generated ass bubble_butt… See the full description on the dataset page: https://huggingface.co/datasets/deepghs/rule34_full.BenchMAX_Rule-based
Dataset Sources
Paper: BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models
Link: https://huggingface.co/papers/2502.07346
Repository: https://github.com/CONE-MT/BenchMAX
Dataset Description
BenchMAX_Rule-based is a dataset of BenchMAX, sourcing from IFEval, which is a rule-based benchmark for evaluating the instruction following capabilities in multilingual scenarios.
We extend the original dataset to 16 non-English languages by first… See the full description on the dataset page: https://huggingface.co/datasets/LLaMAX/BenchMAX_Rule-based.rulerruler-full
RULER Benchmark (Full)
Complete RULER benchmark dataset with all 13 tasks across 6 context lengths (4K to 128K tokens).
Overview
Metric
Value
Total Samples
78,000 (39,000 per variant)
Tasks
13
Context Lengths
4K, 8K, 16K, 32K, 64K, 128K
Samples per Config
500
Variants
memwrap, plain
Tasks
Retrieval (NIAH - Needle in a Haystack)
niah_single_1, niah_single_2, niah_single_3 - Single needle variants
niah_multikey_1… See the full description on the dataset page: https://huggingface.co/datasets/tonychenxyz/ruler-full.RULER-32768-Qwen2.5-3B-tokenizermulti_ds_asiaRuleWorldRuleWorld
RuleWorld is a large-scale benchmark for evaluating whether language models can retrieve and apply a shared repository of externally provided procedural rules. Its rules are abstract, globally reusable, and intentionally non-commonsense, so a model cannot answer reliably from world knowledge alone. Each rule is supplied in aligned natural-language (NL) and first-order logic (FOL) forms.
The benchmark covers three reasoning settings: Single-Rule QA, Parallel Multi-Rule QA… See the full description on the dataset page: https://huggingface.co/datasets/SharkSpicy/RuleWorld.RuLips-1k
RuLips-1k: recipe for a 1,100-hour Russian lip-reading corpus
RuLips-1k is a recipe dataset: it contains everything needed to rebuild a
1,134-hour corpus of Russian talking faces (423,047 clips, 224×224, 25 fps, one
speaker per clip, transcript with word timestamps, 478 MediaPipe face landmarks per
frame, head-pose angles), but no video. Videos are re-downloaded from their
original hosts (RuTube, YouTube) and cropped by the included scripts, which reproduce
our crops… See the full description on the dataset page: https://huggingface.co/datasets/levossadtchi/RuLips-1k.ruler_dataRUListening
RUListening: Building Perceptually-Aware Music-QA Benchmarks
Multimodal LLMs, particularly Large Audio Language Models (LALMs), have shown progress in music understanding tasks due to text-only LLM initialization. However, we find that seven of the top ten Music Question Answering (Music-QA) models are text-only models, suggesting these benchmarks rely on reasoning rather than audio perception. To address this limitation, we present RUListening: Robust Understanding through… See the full description on the dataset page: https://huggingface.co/datasets/yongyizang/RUListening.AIRBOT_MMK2_place_the_umbrella_and_the_ruler
AIRBOT_MMK2_place_the_umbrella_and_the_ruler
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: discover_robotics_aitbot_mmk2
| Codebase Version: v2.1
End-Effector Type: five_finger_hand
🏠 Scene Types
This dataset covers the following scene types:
home
🤖 Atomic Actions
This dataset includes the following atomic actions:
grasp
place
pick
📊 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/AIRBOT_MMK2_place_the_umbrella_and_the_ruler.