datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gnss-jamming-spoofing-detection
GNSS Jamming & Spoofing Detection Dataset
A physics-informed synthetic dataset for detecting GPS/GNSS cyber-attacks
(Jamming and Spoofing) from satellite-signal features. Built for the
GNSS Guardian project — Introduction to Data Science final project.
Overview
14,850 samples across 450 scenarios × 33 time-steps each
3 balanced classes: Normal / Jamming / Spoofing (4,950 each)
26 columns: multi-constellation signal features + attack metadata + text descriptions… See the full description on the dataset page: https://huggingface.co/datasets/Omrilevi123/gnss-jamming-spoofing-detection.dl-trm-phase2-codebooks
DL-TRM Phase 2 Codebooks
This dataset repository contains Phase 2 transition VQ codebook artifacts for DL-TRM.
Contents are organized by vocabulary size:
V16/
V32/
V128/
V256/
Each folder includes:
z_traces.pt: discrete Z trace dataset for the Phase 1 route traces
codebook.pt: learned VQ codebook weights
transition_vq_model.pt: trained transition VQ model weights
diagnostics.json: code usage and final training diagnostics
z_trace_manifest.json: artifact manifest
checkpoints/:… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/dl-trm-phase2-codebooks.mfa-vs-sae-2026-webapp-datanvidia-math-vectorizeddl-trm-phase2-codebook-v32
DL-TRM Phase 2 Codebook V32
This standalone dataset contains the Phase 2 discrete Z traces for V32.
The Hugging Face dataset viewer reads data/train.jsonl.
Each row contains:
raw_puzzle_id
route_id
route_local_id
medoid_old_id
selected_stage_a_example_index
cluster_size
vocab_size
z_trace: a length-16 list of discrete Z token IDs
The original PyTorch artifacts remain in the repo:
z_traces.pt
codebook.pt
transition_vq_model.pt
diagnostics.json
z_trace_manifest.json
checkpoints/
dl-trm-phase2-codebook-v16
DL-TRM Phase 2 Codebook V16
This standalone dataset contains the Phase 2 discrete Z traces for V16.
The Hugging Face dataset viewer reads data/train.jsonl.
Each row contains:
raw_puzzle_id
route_id
route_local_id
medoid_old_id
selected_stage_a_example_index
cluster_size
vocab_size
z_trace: a length-16 list of discrete Z token IDs
The original PyTorch artifacts remain in the repo:
z_traces.pt
codebook.pt
transition_vq_model.pt
diagnostics.json
z_trace_manifest.json
checkpoints/
dl-trm-phase2-codebook-v256
DL-TRM Phase 2 Codebook V256
This standalone dataset contains the Phase 2 discrete Z traces for V256.
The Hugging Face dataset viewer reads data/train.jsonl.
Each row contains:
raw_puzzle_id
route_id
route_local_id
medoid_old_id
selected_stage_a_example_index
cluster_size
vocab_size
z_trace: a length-16 list of discrete Z token IDs
The original PyTorch artifacts remain in the repo:
z_traces.pt
codebook.pt
transition_vq_model.pt
diagnostics.json
z_trace_manifest.json
checkpoints/
wiki2023_plus
Overview
The dataset includes the description from Wikipedia and categories of films published in 2023.
This dataset is used to evaluate the ability of LLM to memorize and extract information described in the document.
See "Where is the Answer? An Empirical Study of Positional Bias for Parametric Knowledge Extraction in Language Model (NAACL2025 Long paper)" for how we use this dataset for training and evaluation.
Data Split
film_doc_all.jsonl includes lines of… See the full description on the dataset page: https://huggingface.co/datasets/omron-sinicx/wiki2023_plus.HotelReservationsDataset
Hotel Reservation EDA & Prediction
Overview
This project explores a hotel reservation dataset from Kaggle with the goal of understanding booking behavior
and determining whether it is possible to predict reservation status (canceled or not canceled) based on the available features.
The project focuses on Exploratory Data Analysis (EDA) only — no machine learning model was trained.
Dataset
This dataset includes 36,275 hotel reservations with 19 features… See the full description on the dataset page: https://huggingface.co/datasets/Omrihahami/HotelReservationsDataset.Website_Traffic_and_Engagementdl-trm-phase2-codebook-v128
DL-TRM Phase 2 Codebook V128
This standalone dataset contains the Phase 2 discrete Z traces for V128.
The Hugging Face dataset viewer reads data/train.jsonl.
Each row contains:
raw_puzzle_id
route_id
route_local_id
medoid_old_id
selected_stage_a_example_index
cluster_size
vocab_size
z_trace: a length-16 list of discrete Z token IDs
The original PyTorch artifacts remain in the repo:
z_traces.pt
codebook.pt
transition_vq_model.pt
diagnostics.json
z_trace_manifest.json
checkpoints/
nvidia_math_512_47KAlignBench
HalCap-Bench
HalCap-Bench dataset.
Columns
model
image_source
image_name
image_type
sentence_index
caption
annotation
error_type
error_words
agreement_ratio
fleiss_Pi
n_correct
n_incorrect
n_unknown
image_url
image_path_in_repo
Notes
Notes
For COCO/CC12M items, the image is referenced by image_url.
For SD/Imagen/data_generation items, the image file is stored under images/ and referenced by image_path_in_repo.
CharBench
CharBench - Character-level benchmark and analysis suite for LLMs.
CharBench is a large-scale benchmark for studying tokenization and character-level behavior in modern language models.
For complete details on data curation and evaluation, see the paper.
If you have ideas and suggestions to improve charbench feel free to reach out!
uzan dot omri at gmail.com
Usage
from datasets import load_dataset
ds = load_dataset("omriuz/CharBench")
Citation
If you use… See the full description on the dataset page: https://huggingface.co/datasets/omriuz/CharBench.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.leisaac-pick-orangeThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"… See the full description on the dataset page: https://huggingface.co/datasets/omri-manual/leisaac-pick-orange.dl-trm-phase2-controller-kmeans-v128
DL-TRM Phase 2 Controller K-Means V128
Discrete Z traces produced by k-means over Phase 1 controller deltas.
Source trace artifact: phase1_real_medoid_route_trace_shards
Input signal: medoid_controller_delta_trace[1:]
Input shape before quantization: [11400, 16, 512]
Output Z trace shape: [11400, 16]
Vocabulary size: 128
Token id base: 0
Diagnostics
{
"used_codes": 128,
"dead_codes": 0,
"perplexity": 123.55767954902056,
"unique_traces": 9222… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/dl-trm-phase2-controller-kmeans-v128.gnss-guardian
GNSS Guardian: GPS Jamming and Spoofing Detection
A synthetic dataset for detecting GPS attacks (jamming and spoofing) from a phone's satellite signal. Built as the final project for Introduction to Data Science.
The goal is simple: detect when your GPS is being jammed or spoofed, so you can switch to a safe satellite system such as GLONASS instead of trusting a wrong position.
The data generation and EDA notebook is included in this repository.
Background
GPS is… See the full description on the dataset page: https://huggingface.co/datasets/Omrilevi123/gnss-guardian.credit-card-defaulter-dataeasy_math_by_qwen1.5_instructdl-trm-phase2-controller-kmeans-v32
DL-TRM Phase 2 Controller K-Means V32
Discrete Z traces produced by k-means over Phase 1 controller deltas.
Source trace artifact: phase1_real_medoid_route_trace_shards
Input signal: medoid_controller_delta_trace[1:]
Input shape before quantization: [11400, 16, 512]
Output Z trace shape: [11400, 16]
Vocabulary size: 32
Token id base: 0
Diagnostics
{
"used_codes": 32,
"dead_codes": 0,
"perplexity": 31.17811290262774,
"unique_traces": 9191… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/dl-trm-phase2-controller-kmeans-v32.so101_teleop_vialsSFT_1024_40Knvidia_math_64_47Kdl-trm-phase2-controller-kmeans-v16
DL-TRM Phase 2 Controller K-Means V16
Discrete Z traces produced by k-means over Phase 1 controller deltas.
Source trace artifact: phase1_real_medoid_route_trace_shards
Input signal: medoid_controller_delta_trace[1:]
Input shape before quantization: [11400, 16, 512]
Output Z trace shape: [11400, 16]
Vocabulary size: 16
Token id base: 0
Diagnostics
{
"used_codes": 16,
"dead_codes": 0,
"perplexity": 15.491387192451063,
"unique_traces": 9176… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/dl-trm-phase2-controller-kmeans-v16.4speaker-dataLMMS_phase2_latentsnvidia_math_512_750Kdl-trm-phase2-controller-kmeans-v256
DL-TRM Phase 2 Controller K-Means V256
Discrete Z traces produced by k-means over Phase 1 controller deltas.
Source trace artifact: phase1_real_medoid_route_trace_shards
Input signal: medoid_controller_delta_trace[1:]
Input shape before quantization: [11400, 16, 512]
Output Z trace shape: [11400, 16]
Vocabulary size: 256
Token id base: 0
Diagnostics
{
"used_codes": 256,
"dead_codes": 0,
"perplexity": 244.03568219976052,
"unique_traces": 9225… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/dl-trm-phase2-controller-kmeans-v256.gemma3_dpo_dataset