datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Charge-040_0040-Sparse-MonoLanguage-Grounded_Sparse_Encoder_Training
Language-Grounded Sparse Encoder (LanSE) — Training Data
This repository hosts the AI-generated images and human annotation datasets accompanying the paper:
Human-like Content Analysis for Generative AI with Language-Grounded Sparse Encoders
Yiming Tang, Arash Lagzian, Srinivas Anumasa, Qiran Zou, Yingtao Zhu, Ye Zhang, Trang Nguyen, Yih-Chung Tham, Ehsan Adeli, Ching-Yu Cheng, Yilun Du, Dianbo Liu
National University of Singapore · Tsinghua University · Stanford University ·… See the full description on the dataset page: https://huggingface.co/datasets/DesmondYMTang2024/Language-Grounded_Sparse_Encoder_Training.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.Charge-010_0050-Sparse-Monosparse-reward-long-tasks
Sparse Reward Long Tasks
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and reproducibility… See the full description on the dataset page: https://huggingface.co/datasets/rmems/sparse-reward-long-tasks.Charge-020_0020-Sparse-Monop2-etf-sparse-elasticnet-resultsAIM2024-SparseNeuralRendering
Dataset
This repository cofntains SpaRe (Sparse Rendering) dataset.
Associated paper
The dataset contained in this repository is published as a part of AIM workshop at ECCV 2024.
open-us-law-sparse-graphrag
State laws snapshot sparse GraphRAG
CID-keyed retrieval release in the same thin-client layout as
Publicus/skillcenter-ir:
Zstandard Parquet shards of at most 4,096 rows
entry_cid as the canonical content identity; document_index is a compact pointer
compact BM25 term-range, vector-centroid, corpus, and adjacency routing indexes
thenlper/gte-small 384-d L2 vectors, cosine-sorted inside centroid shards
queries fetch the manifest, routing indexes, and only the routed shards
This… See the full description on the dataset page: https://huggingface.co/datasets/justicedao/open-us-law-sparse-graphrag.TRAIN_SPARSE_2sparse-probing
Sparse Probing Datasets
155 binary classification tasks for probing language model representations.
From: "Are Sparse Autoencoders Useful? A Case Study in Sparse Probing" (arXiv:2502.16681)
Source: EleutherAI/sae-probes
Usage
from datasets import load_dataset
# Load a specific dataset
ds = load_dataset("serteal/sparse-probing", "87_glue_cola")
# List available configurations
from datasets import get_dataset_config_names
configs =… See the full description on the dataset page: https://huggingface.co/datasets/serteal/sparse-probing.glm46v-flash-ultramega-sparse-hmapGSA-PT-Qwen2-7B-Instruct-chunk4-chunk4-data
GSA-PT-Qwen2-7B-Instruct-chunk4-chunk4-data
This is the continue pretraining dataset used for training GSA (Gist Sparse Attention) models based on Qwen2-7B-Instruct with chunk size chunk4-chunk4.
Each sample is tokenized and formatted with GSA gist tokens for continue pretraining.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Model
yuzhenm/GSA-PT-Qwen2-7B-Instruct-chunk4-chunk4 — model trained on this dataset
SparseEval_benchmark_data
Benchmark Data
This directory contains the raw benchmark prediction results in CSV format. These files represent the model outputs and ground truth correctness for various datasets.
File Format
Each CSV file should contain the following columns:
source: The identifier of the model that generated the prediction.
item: The identifier of the specific test instance (question/sample).
correct: A binary value indicating whether the model's prediction was correct (1) or… See the full description on the dataset page: https://huggingface.co/datasets/iridescentttt/SparseEval_benchmark_data.SparseCam4Drepro-incremental-learning-of-sparse-attention-patterns-in-transformers-bundle
Reproduction bundle — Incremental Learning of Sparse Attention Patterns in Transformers
ICML 2026 paper #12503 · OpenReview vSRh1qU5sH · arXiv 2602.19143
Everything needed to re-run the reproduction whose results are recorded in the
Trackio logbook GwendalTsang/repro-incremental-learning-of-sparse-attention-patterns-in-transformers.
Layout
upstream/ the authors' official code, vendored unchanged… See the full description on the dataset page: https://huggingface.co/datasets/GwendalTsang/repro-incremental-learning-of-sparse-attention-patterns-in-transformers-bundle.sparse-resultsSparseCraft-dataset
SparseCraft
[ECCV'24] SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization
Project
DTU Dataset
We provide preprocessed DTU data and results for the tasks of novel view synthesis and surface reconstruction.
It contains the following directories:
sparsecraft_data
├── nvs # Novel View Synthesis task data and results
│ └── mvs_data
│ ├── scan103
│ ├── ...
│ └── results # Results for training using… See the full description on the dataset page: https://huggingface.co/datasets/maeyounes/SparseCraft-dataset.GSA-PT-Qwen2-7B-Instruct-chunk16-data
GSA-PT-Qwen2-7B-Instruct-chunk16-data
This is the continue pretraining dataset used for training GSA (Gist Sparse Attention) models based on Qwen2-7B-Instruct with chunk size chunk16.
Each sample is tokenized and formatted with GSA gist tokens for continue pretraining.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Model
yuzhenm/GSA-PT-Qwen2-7B-Instruct-chunk16 — model trained on this dataset
GSA-PT-Llama-3.2-1B-chunk8-data
GSA-PT-Llama-3.2-1B-chunk8-data
This is the continue pretraining dataset used for training GSA (Gist Sparse Attention) models with chunk size chunk8.
Each sample is tokenized and formatted with GSA gist tokens for continued pretraining.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Models
yuzhenm/GSA-PT-Llama-3.2-1B-chunk8 — model trained on this dataset
causalcache-sparse-history-guiodyssey-v5
CausalCache Sparse-History Corpus
A controlled corpus for testing whether a GUI agent actually reads its visual history — or merely benefits from history being present.
Mobile GUI agents normally see a sliding window of the most recent screenshots. That is a poor fit for long-horizon tasks, where the screen you need to remember may be twenty steps back. This corpus lets you train and, more importantly, falsify adapters that claim to read a sparse, non-contiguous selection of… See the full description on the dataset page: https://huggingface.co/datasets/gavinlaw/causalcache-sparse-history-guiodyssey-v5.SparseGeometricRAG
SparseGeometricRAG
CPU-first sparse geometric retrieval for practical top-10 RAG
No transformer inference at retrieval time. No retrieval GPU requirement. No dense document-vector dot products. No external API.
SparseGeometricRAG is a retrieval system built around one systems objective: make the retrieval layer cheap enough to run on ordinary multicore CPU hardware without turning the corpus into a dense embedding database. It uses sparse TF-IDF geometry, fuzzy… See the full description on the dataset page: https://huggingface.co/datasets/Angshul/SparseGeometricRAG.sparse-metric-anchors-ycb
Sparse Metric Anchors — YCB benchmark
The 42-object benchmark behind the paper Sparse Metric Anchors for a Single-View 3D
Generative Prior: The Output Frame Is the Bottleneck (ISIR, Sorbonne Université,
2026). Code and paper: github.com/635jack/sparse-metric-anchors — its colab/reproduce.ipynb recomputes every table of the paper from this dataset on a CPU runtime.
The paper asks what limits the injection of a few metric measurements — tactile
contacts, one depth map — into a… See the full description on the dataset page: https://huggingface.co/datasets/jack635/sparse-metric-anchors-ycb.GSA-FT-Qwen2-7B-Instruct-chunk4-chunk4-data
GSA-FT-Qwen2-7B-Instruct-chunk4-chunk4-data
This is the supervised fine-tuning dataset used for training GSA (Gist Sparse Attention) models based on Qwen2-7B-Instruct with chunk size chunk4-chunk4.
Each sample is tokenized and formatted with GSA gist tokens for supervised fine-tuning.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Model
yuzhenm/GSA-FT-Qwen2-7B-Instruct-chunk4-chunk4 — model trained on this dataset
sparsewake
SparseWake
SparseWake is an anonymous benchmark artifact for sparse temporal hydrodynamic sensing. It contains processed HDF5 datasets, metadata, summary CSV files, checksums, and lightweight Python scripts for inspecting the data and reproducing benchmark-level evaluation outputs.
Dataset Summary
SparseWake evaluates whether sparse body-fixed temporal flow measurements can recover the relative state of a neighboring wake-producing fish in a controlled single-leader… See the full description on the dataset page: https://huggingface.co/datasets/SparseWake/sparsewake.rgcl-sparse-retrieval
RGCL Dataset Resources
This repository contains the dataset for the paper Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning.
The linked HF paper is Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
This provides the sparse retrieval dataset for the RGCL paper.
For more details and related resources:
Paper: Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
Code (GitHub):… See the full description on the dataset page: https://huggingface.co/datasets/Jingbiao/rgcl-sparse-retrieval.GSA-PT-Llama-3.2-1B-chunk16-data
GSA-PT-Llama-3.2-1B-chunk16-data
This is the continue pretraining dataset used for training GSA (Gist Sparse Attention) models with chunk size chunk16.
Each sample is tokenized and formatted with GSA gist tokens for continued pretraining.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Models
yuzhenm/GSA-PT-Llama-3.2-1B-chunk16 — model trained on this dataset
GSA-FT-Qwen2-7B-Instruct-chunk32-data
GSA-FT-Qwen2-7B-Instruct-chunk32-data
This is the supervised fine-tuning dataset used for training GSA (Gist Sparse Attention) models based on Qwen2-7B-Instruct with chunk size chunk32.
Each sample is tokenized and formatted with GSA gist tokens for supervised fine-tuning.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Model
yuzhenm/GSA-FT-Qwen2-7B-Instruct-chunk32 — model trained on this dataset
GSA-PT-Qwen2-7B-Instruct-chunk32-data
GSA-PT-Qwen2-7B-Instruct-chunk32-data
This is the continue pretraining dataset used for training GSA (Gist Sparse Attention) models based on Qwen2-7B-Instruct with chunk size chunk32.
Each sample is tokenized and formatted with GSA gist tokens for continue pretraining.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Model
yuzhenm/GSA-PT-Qwen2-7B-Instruct-chunk32 — model trained on this dataset
GSA-FT-Qwen2-7B-Instruct-chunk8-chunk4-data
GSA-FT-Qwen2-7B-Instruct-chunk8-chunk4-data
This is the supervised fine-tuning dataset used for training GSA (Gist Sparse Attention) models based on Qwen2-7B-Instruct with chunk size chunk8-chunk4.
Each sample is tokenized and formatted with GSA gist tokens for supervised fine-tuning.
Paper
GSA: Gist Sparse Attention via Learnable Compression and Selective Unfolding
Related Model
yuzhenm/GSA-FT-Qwen2-7B-Instruct-chunk8-chunk4 — model trained on this dataset