datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
swiss_rulings
Dataset Card for Swiss Rulings
Dataset Summary
SwissRulings is a multilingual, diachronic dataset of 637K Swiss Federal Supreme Court (FSCS) cases. This dataset can be used to pretrain language models on Swiss legal data.
Supported Tasks and Leaderboards
Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the… See the full description on the dataset page: https://huggingface.co/datasets/rcds/swiss_rulings.lm-eval-ruler-results-private-32K
Dataset Card for Evaluation run of elichen3051/Llama-3.1-8B-GGUF
Dataset automatically created during the evaluation run of model elichen3051/Llama-3.1-8B-GGUF
The dataset is composed of 12 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/elichen-skymizer/lm-eval-ruler-results-private-32K.multimodality-poc-llama31-ruler16k
Multimodality PoC corpus — Llama-3.1-8B-Instruct on RULER-16K
Raw pre-RoPE query and hidden-state tensors captured during prefill, used
to study whether the per-(layer, kv_head) query distribution is unimodal
Gaussian (the assumption underpinning Expected Attention's MGF closed-form
in kvpress).
What's in here
65 .npz files, one per (RULER task, prompt_index) pair (13 tasks × 5
prompts).
Each file (~414 MB) contains:
field
dtype
shape
meaning
hidden
float16… See the full description on the dataset page: https://huggingface.co/datasets/June30916/multimodality-poc-llama31-ruler16k.ruler_eval_data_128k
RULER evaluation data — 128K context only
This dataset is a subset of aldea-ai/ruler-eval-data, containing only the 128K context length (131072 tokens). The full multi-length dataset also includes 1M and other lengths. Files are published under 131072/ (numeric token count) for compatibility with benchmark_ruler.py --context_length 131072, even though the source snapshot uses a 128k/ folder name.
Layout
Same as the upstream RULER on-disk layout, compatible with… See the full description on the dataset page: https://huggingface.co/datasets/aldea-ai/ruler_eval_data_128k.arabic-rule-checking
Arabic Rule Checking — قواعد ونصوص عربية بأحكام محسوبة
172,488 labelled (text, rule) pairs in Arabic. Each row asks one question: does this text
satisfy this rule? The answer is مطابق or مخالف.
بالعربية: مجموعة بيانات عربية للتحقق من مطابقة النصوص لقواعد مكتوبة بلغة طبيعية. كل صف
يحتوي على نص وقاعدة وحكم محسوب آليًا، وليس رأي نموذج.
split
pairs
texts
train
159,240
48,030
validation
13,248
2,002
Built from 50,062 generated Arabic texts across 12 document types… See the full description on the dataset page: https://huggingface.co/datasets/oddadmix/arabic-rule-checking.ruler-100-nemotron
RULER-100 — Nemotron-Nano-v3 tokenized
RULER long-context evaluation data, regenerated with the
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (instruct) tokenizer so the labeled context
lengths are exact for that model — instead of drifting, as they do when RULER data tokenized for a
different model (e.g. Qwen3) is fed to Nemotron.
What's here
7 context lengths: 4096, 8192, 16384, 32768, 65536, 131072, 262144 (the model's max).
13 RULER tasks: niah_single_1/2/3… See the full description on the dataset page: https://huggingface.co/datasets/jet-ai/ruler-100-nemotron.pes2o_v3gdn2-ruler-niah-eval-data
RULER NIAH eval data (GDN-2 CPT comparison)
Exact test sets generated with lm-eval-harness RULER generators (RANDOM_SEED=42, tokenizer TinyLlama/TinyLlama_v1.1, lengths [1024, 2048, 4096, 8192], 500 samples/length/task).
Tasks: niah_single_1, niah_single_2, niah_single_3, niah_multikey_1
Used by the unified evaluation in dsc/mc_sketch_remoe/scripts/run_eval_compare_lmeval.sh (limit 50, seed 42). Text-only (raw prompts/targets); each model tokenizes with its own tokenizer.
massive_serve_dpr_wiki_contriever_ivfpqTIGER-Lab__AceCoder-Qwen2.5-7B-Ins-Rule-details
Dataset Card for Evaluation run of TIGER-Lab/AceCoder-Qwen2.5-7B-Ins-Rule
Dataset automatically created during the evaluation run of model TIGER-Lab/AceCoder-Qwen2.5-7B-Ins-Rule
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/TIGER-Lab__AceCoder-Qwen2.5-7B-Ins-Rule-details.massive_serve_dpr_wiki_qwen3_0.6b_ivfpqTIGER-Lab__AceCoder-Qwen2.5-Coder-7B-Ins-Rule-details
Dataset Card for Evaluation run of TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Ins-Rule
Dataset automatically created during the evaluation run of model TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Ins-Rule
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/TIGER-Lab__AceCoder-Qwen2.5-Coder-7B-Ins-Rule-details.TIGER-Lab__AceCoder-Qwen2.5-Coder-7B-Base-Rule-details
Dataset Card for Evaluation run of TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Base-Rule
Dataset automatically created during the evaluation run of model TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Base-Rule
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/TIGER-Lab__AceCoder-Qwen2.5-Coder-7B-Base-Rule-details.OmniMATH100-rulemassive_serve_demomassive_serve_dpr_wiki_e5_base_v2_ivfpqalimony-rules-by-state-2026
Alimony Rules By State 2026
Alimony/spousal support rules for 13 states with mortgage impact.
Details
Records: 13
Format: JSONL
License: CC-BY-4.0
Last Updated: March 2026
Verified By: Wendy Thompson, CPA, CDLP, NMLS #504814
Publisher: Wendy Thompson Lending Team
Thompson Alpha Logic
State-by-state alimony duration and calculation methods mapped to mortgage qualification impact. Shows how alimony income qualifies (or disqualifies) for FHA, VA, and… See the full description on the dataset page: https://huggingface.co/datasets/Wendy-Thompson-Lending-Team/alimony-rules-by-state-2026.FineWeb-Edu-1BTA subset of FineWeb-Edu randomly sampled from the whole dataset of around 1B gpt2 tokens.
This dataset is created for illustration purpose in retrieval-scaling. Please do not distribute.
Ruler_Training_Dataadaption-brazilian-civil-law-rulings
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
brazilian_civil_law_rulings
This dataset contains samples of Brazilian civil law court decisions, specifically focusing on appeals, special recourse agravo, and consumer protection cases handled by the Superior Court of Justice (STJ). The text includes detailed legal reasoning, case summaries, discussion of res judicata, contractual rescission, moral damages, and citations of relevant articles… See the full description on the dataset page: https://huggingface.co/datasets/Fernandosr85/adaption-brazilian-civil-law-rulings.polish-court-rulings-sample
Polish Court Rulings — Sample (korpus-pl)
A production-grade, PII-hardened corpus of Polish court rulings — free evaluation sample.
Full corpus: 505,611 rulings · ~3.18B tokens, licensed commercially.
Contact: licensing@aioil.ai · aioil.ai
What this is
This sample contains 500 Polish court rulings drawn from the full korpus-pl dataset — a cleaned, deduplicated and PII-audited corpus of Polish jurisprudence built for AI training, evaluation and legal RAG… See the full description on the dataset page: https://huggingface.co/datasets/aioil-ai/polish-court-rulings-sample.FineWeb-Edu-1MTA subset of FineWeb-Edu randomly sampled from the whole dataset of around 1M gpt2 tokens.
This dataset is created for illustration purpose in retrieval-scaling. Please do not distribute.
arc-agi-1-ruleloopvit-rules
ARC-AGI-1 RuleLoopViT Rules
This dataset contains one canonical, task-specific English rule for each of the
400 official ARC-AGI-1 training tasks. Rules were inferred only from official
demonstration input/output pairs. Official test inputs, test outputs, and test
traces were excluded from rule authoring.
Each row includes:
a concise standalone core_rule_text;
a five-section full_rule_text;
the corresponding structured sections;
augmentation-aware references for colors… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/arc-agi-1-ruleloopvit-rules.glp1-telehealth-rules-us
GLP-1 Telehealth Rules by U.S. State
Which U.S. jurisdictions (50 states + Washington, D.C.) require a live video
visit to start GLP-1 treatment by telehealth — plus each jurisdiction's medical
board, Medicaid GLP-1 coverage status, and nurse practitioner
prescriptive-authority classification (full/reduced/restricted) verified
against state nurse practice acts with statute citations.
Canonical, always-current source: https://www.pallashealth.co/glp-1/telehealth-rules
Live CSV:… See the full description on the dataset page: https://huggingface.co/datasets/pallas-health/glp1-telehealth-rules-us.ruLeetCodeRULER_512tot-cwq-plan-sft-outputs34-rule-full-pw4-expand-labels-v2
ToT CWQ Plan SFT - outputs34_rule_full_pw4_expand_labels_v2
Merged SFT output from local run outputs34_rule_full_pw4_expand_labels_v2.
Version ID
local output dir: tot/sft/outputs34_rule_full_pw4_expand_labels_v2
file: cwq_train_plan.no_mid.jsonl
dataset: CWQ
grouping backend: TOT_REL_GROUPING_BACKEND=rules
parallel workers: 4
strict expand parity: enabled
nested expand labels: enabled
Main difference from earlier runs
This version renders nested Expand… See the full description on the dataset page: https://huggingface.co/datasets/YF0808/tot-cwq-plan-sft-outputs34-rule-full-pw4-expand-labels-v2.rulerlora-rules-dataset
LoRA Rules Dataset
Synthetic behavioral rules dataset for training a hypernetwork that generates
LoRA adapters on-the-fly from structured rule strings.
Format
Each record is a JSON line with fields:
rule_id — unique identifier
rule_type — one of: Constraint, Format, Knowledge, Persona, Safety, Tone
weight — float 0.0–1.0, importance of the rule
description — natural language rule description
raw — full rule string [RuleType|Weight] Description
training_examples — list of… See the full description on the dataset page: https://huggingface.co/datasets/broadfield-dev/lora-rules-dataset.lora-rules-qwen3-0.6b-r8-n180
LoRA Rules Dataset
Synthetic behavioral rules dataset for training a hypernetwork that generates
LoRA adapters on-the-fly from structured rule strings.
Format
Each record is a JSON line with fields:
rule_id — unique identifier
rule_type — one of: Constraint, Format, Knowledge, Persona, Safety, Tone
weight — float 0.0–1.0, importance of the rule
description — natural language rule description
raw — full rule string [RuleType|Weight] Description
training_examples — list of… See the full description on the dataset page: https://huggingface.co/datasets/broadfield-dev/lora-rules-qwen3-0.6b-r8-n180.