datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cosmos_qasamsum
Dataset Card for SAMSum Corpus
Dataset Description
Links
Homepage: hhttps://arxiv.org/abs/1911.12237v2
Repository: https://arxiv.org/abs/1911.12237v2
Paper: https://arxiv.org/abs/1911.12237v2
Point of Contact: https://huggingface.co/knkarthick
Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to… See the full description on the dataset page: https://huggingface.co/datasets/knkarthick/samsum.DSCodeBench
DSCodeBench
Task-grouped, multidimensional code-generation quality estimation data derived from DSCodeBench.
Dataset contents
The release contains 24,972 complete artifact rows from 999 tasks. The source commit is e75ef26fedea7415bdffd3e1cbff95ddad89e7e2.
Each row contains the task instruction, released 200-case test generator, generated Python code, generator identity, sandbox execution context, the independently collected 200-element correctness vector, and four… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/DSCodeBench.sam-solicitation-documents
Sam Solicitation Documents
Attachments from federal solicitation notices on SAM.gov: statements of work, performance work statements, justifications, amendments, wage determinations and the rest of the paperwork that accompanies a federal contract opportunity.
Every document here was published by a US federal agency and is a work of the
United States government. Nothing has been altered: files are byte-identical to
what the agency posted, and the checksum in metadata.parquet is… See the full description on the dataset page: https://huggingface.co/datasets/abigailhaddad/sam-solicitation-documents.PRISM
PRISM Alignment
Task-grouped multidimensional dialogue-quality data from PRISM Alignment.
Contents
The release contains 6,187 complete artifact rows from 6,187 conversation groups and 1,309 participants.
The original release contains 8,011 conversations; 1,824 are excluded because one or more of the seven performance sliders is missing or invalid, or because the selected first-turn response is unavailable.
The targets are values, fluency, factuality, safety… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/PRISM.MSumBench
MSumBench
Task-grouped multidimensional summarization quality data from MSumBench.
Contents
The release contains 2,250 complete artifact rows from 150 source-document groups.
The three prediction targets are faithfulness, completeness, and conciseness.
Split organization
Each seed has task-grouped train, validation, and test splits. All language-specific summaries and model generations for one group_id remain in exactly one split. The seeds change… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/MSumBench.RATE
WMT24 English-Russian RATE
Task-grouped multidimensional machine-translation quality data from RATE.
Contents
The release contains 3,975 complete artifact rows from 497 source-segment groups.
One curated row was removed from the 3,976-row release because its translation was blank and its three scores were zero. The targets are score_accuracy, score_fluency, and score_style, each on a 0--100 scale.
Split organization
Each seed has task-grouped train… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/RATE.CreativeEval
CreativeEval
Paper-grouped multidimensional research-ideation evaluation data from CreativeEval.
Contents
The release contains 1,026 complete paper rows. Each row contains a human-written research-paper introduction, the raw reviewer score arrays for provenance, and four mean prediction targets: contribution_mean, soundness_mean, presentation_mean, and overall_score_mean.
All four targets are derived from the human reviewer scores released with the paper. The raw… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/CreativeEval.so101_cubeThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 50,
"total_frames": 29698,
"total_tasks": 1,
"total_videos": 100,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so101_cube.SDS-KoPub-VDR-Benchmark
📘 Dataset Summary
SDS KoPub-VDR is a benchmark dataset for Visual Document Retrieval (VDR) in the context of
Korean public documents. It contains real-world government document images paired with natural-language
queries, corresponding answer pages, and ground-truth answers. The dataset is designed to evaluate AI models that
go beyond simple text matching, requiring comprehensive understanding of visual layouts, tables, graphs, and images
to accurately locate relevant… See the full description on the dataset page: https://huggingface.co/datasets/SamsungSDS-Research/SDS-KoPub-VDR-Benchmark.so100_PnPacornThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 49,
"total_frames": 28916,
"total_tasks": 1,
"total_videos": 196,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:49"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so100_PnPacorn.netryx-new-york-5km
New York 5km
Pre-computed MegaLoc index for Netryx Drishti geolocation.
Coverage
Center: 40.712800, -74.006000
Radius: 5.0 km
Panoramas: 196,824
Index entries: 787,296
Descriptor model: MegaLoc
Descriptor dim: 1024 (PCA from 8448)
Usage
from netryx_hub import NetryxHub
hub = NetryxHub()
hub.download("new-york-5km", output_dir="./netryx_data/index")
# Now open Netryx and search!
Or download manually and use Import Index in the Netryx GUI.
Details… See the full description on the dataset page: https://huggingface.co/datasets/samsepiol4/netryx-new-york-5km.netryx-new-york-city-13km
Nyc-Core-Usethis 13km
Pre-computed MegaLoc index for Netryx Drishti geolocation.
Coverage
Center: 40.713200, -74.002500
Radius: 13.0 km
Panoramas: 663,084
Index entries: 2,652,336
Descriptor model: MegaLoc
Descriptor dim: 1024 (PCA from 8448)
Usage
from netryx_hub import NetryxHub
hub = NetryxHub()
hub.download("nyc-core-usethis-13km", output_dir="./netryx_data/index")
# Now open Netryx and search!
Or download manually and use Import Index in… See the full description on the dataset page: https://huggingface.co/datasets/samsepiol4/netryx-new-york-city-13km.netryx-newyoek-0km
Newyoek 0km
Pre-computed MegaLoc index for Netryx Drishti geolocation.
Coverage
Center: 40.712800, -74.006000
Radius: 0.5 km
Panoramas: 17,835
Index entries: 71,340
Descriptor model: MegaLoc
Descriptor dim: 1024 (PCA from 8448)
Usage
from netryx_hub import NetryxHub
hub = NetryxHub()
hub.download("newyoek-0km", output_dir="./netryx_data/index")
# Now open Netryx and search!
Or download manually and use Import Index in the Netryx GUI.
Details… See the full description on the dataset page: https://huggingface.co/datasets/samsepiol4/netryx-newyoek-0km.netryx-delhi-mixvpr-5km
Delhi 5km (MixVPR)
Pre-computed MegaLoc index for Netryx Drishti geolocation.
Coverage
Center: 28.644800, 77.216721
Radius: 5.0 km
Panoramas: 69,838
Index entries: 279,352
Descriptor model: MixVPR
Descriptor dim: 512 (PCA from 512)
Usage
from netryx_hub import NetryxHub
hub = NetryxHub()
hub.download("delhi-5km-(mixvpr)", output_dir="./netryx_data/index")
# Now open Netryx and search!
Or download manually and use Import Index in the Netryx GUI.… See the full description on the dataset page: https://huggingface.co/datasets/samsepiol4/netryx-delhi-mixvpr-5km.so100_PnPblockThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 50,
"total_frames": 32709,
"total_tasks": 1,
"total_videos": 150,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so100_PnPblock.sickr-sts
Samsoup/sickr-sts
This dataset is derived from mteb/sickr-sts (SICK-R style semantic textual similarity),
which in MTEB is provided as a single split. This script shuffles that split deterministically
and produces train / validation / test = 70% / 20% / 10%.
Fields
sentence1 — first sentence
sentence2 — second sentence
score — similarity / relatedness score (float32)
Processing
Input: single split from mteb/sickr-sts
Shuffle with a fixed seed
70/20/10 partition
Keep only… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/sickr-sts.so100_boarduniThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 51,
"total_frames": 45509,
"total_tasks": 1,
"total_videos": 204,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:51"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so100_boarduni.dfe-stacked_samsum
Dataset Card for "dfe-stacked_samsum"
This custom dataset julep-ai/dfe-stacked_samsum was created from stacked-summaries/stacked-samsum-1024 by:
Extracting summaries for corresponding dialogs to emulate "facts"
Then truncating the dialogs to emulate "missing information"
And then augmenting the dialogs using LLMs to emulate "additional information"
It is used to train our Dialog-Fact Encoder model.
This dataset is permissively licensed under the MIT license.
Notebooks… See the full description on the dataset page: https://huggingface.co/datasets/julep-ai/dfe-stacked_samsum.hallucination-guard-cachestarcoder
Starcoder Dataset (The Stack - Sub-sampled)
This dataset is derived from the "Starcoder" version of The Stack, a 6.4 TB dataset of permissively licensed source code in 384 programming languages.
This repository contains the data organized into subsets, one for each programming language or data type.
How to Use
You can load any language-specific subset of the data using the datasets library. You must specify the name parameter with the desired language.
For example… See the full description on the dataset page: https://huggingface.co/datasets/Sam-Shin/starcoder.so100_peg01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 89,
"total_frames": 79181,
"total_tasks": 1,
"total_videos": 267,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:89"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so100_peg01.samsemo-audioso100_PnPThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 50,
"total_frames": 23377,
"total_tasks": 1,
"total_videos": 100,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so100_PnP.so101_block1This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 25,
"total_frames": 22225,
"total_tasks": 1,
"total_videos": 50,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:25"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so101_block1.205-samsam-cats_and_dogs0916_isaacsim_so101_teleop_block_basketball_coca_cola_samsung_tv_remote_control_dataset
0916_isaacsim_so101_teleop_block_basketball_coca_cola_samsung_tv_remote_control_dataset
Successful SO101 manipulation episodes collected in Isaac Sim using teleop collection.
Property
Value
Episodes
200 (50 per target)
Frames
55506
FPS
30
Format
LeRobot v3.0
RGB cameras
front, top, wrist; 512 × 512
Robot state and action
6 joints
Targets
block, basketball, coca_cola, samsung_tv_remote_control
Reproducing the starting layouts… See the full description on the dataset page: https://huggingface.co/datasets/marin6670/0916_isaacsim_so101_teleop_block_basketball_coca_cola_samsung_tv_remote_control_dataset.adaption-defi-wallet-risk-classification
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-defi_wallet_risk_classification
This dataset contains prompt-completion pairs for classifying the 14-day risk outcomes of DeFi wallets on various EVM networks. Each entry provides behavioral features such as transaction counts, action ratios, and concentration metrics within a specific feature window to predict a binary risk label. The completions offer a concise justification for… See the full description on the dataset page: https://huggingface.co/datasets/samscript18/adaption-defi-wallet-risk-classification.adaption-defi-wallet-risk-classification-v1
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-defi_wallet_risk_classification
This dataset contains prompt-completion pairs for classifying the 14-day risk outcomes of DeFi wallets on various EVM networks based on behavioral features. Each sample provides wallet metrics such as transaction counts, action ratios, and concentration levels, followed by a binary risk label and a concise reasoning statement. The data is designed for… See the full description on the dataset page: https://huggingface.co/datasets/samscript18/adaption-defi-wallet-risk-classification-v1.so100_PnPfixedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 25,
"total_frames": 12584,
"total_tasks": 1,
"total_videos": 75,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:25"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/samsitol/so100_PnPfixed.