datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
taqpol_insilico_dms
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: Yulia E. Tomilova,
Nikolai E. Russkikh,
Igor M. Yi,
Elizaveta V. Shaburova,
Viktor N. Tomilov,
Galina B. Pyrinova,
Svetlana O. Brezhneva,
Olga S. Tikhonyuk,
Nadezhda S. Gololobova,
Dmitriy V. Popichenko,
Maxim O. Arkhipov,
Leonid O. Bryzgalov,
Evgeny V. Brenner… See the full description on the dataset page: https://huggingface.co/datasets/nerusskikh/taqpol_insilico_dms.perfume-dataset
Perfume Dataset
Hugging Face-friendly export of crawled perfume records from parfumo_tidytuesday.
Files
data.parquet: primary tabular artifact for datasets.load_dataset(...)
data.jsonl: JSON Lines version of the same split
Suggested usage
from datasets import load_dataset
dataset = load_dataset("your-org/perfume-dataset", split="train")
print(dataset[0])
Schema overview
Each row corresponds to one crawled perfume record and preserves the raw… See the full description on the dataset page: https://huggingface.co/datasets/junyeong-nero/perfume-dataset.ner-eval-predictionsVoxpopuli_NER
VoxPopuli_NER
VoxPopuli-NER is derived from the VoxPopuli corpus and specifically enhanced for
Named Entity Recognition (NER) tasks focusing on political and geographical entities.
It includes 879 audio samples, annotated with 2469 unique entity types. The dataset consists of the English part of the test set of VoxPopuli.
See full details in the WhisperNER paper.
citation
If you find this usful, please cite the following works:
@article{ayache2024whisperner… See the full description on the dataset page: https://huggingface.co/datasets/aiola/Voxpopuli_NER.data-use-ner
Data-use-ner (human holdout)
GLiNER-format human-adjudicated holdout: 473 spans — annotator190 (190, origin=fcv_pads_east_africa) + jdc283 (283, origin=jdc_operational). Never trained on.
Source: rafmacalaba/datause-displacement-reviewed holdout (gliner_reviewed token spans + readable_reviewed passages, v2.4 labels) with v3 probe head_score (outputs/gliner_datause_v3_probe_human473.jsonl).
Columns
text (full passage = " ".join(tokenized_text); span char offsets… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-ner.nero-move-allThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"tcp.x",
"tcp.y",
"tcp.z",
"tcp.r1",
"tcp.r2",
"tcp.r3",
"tcp.r4",
"tcp.r5",
"tcp.r6",
"gripper.pos"… See the full description on the dataset page: https://huggingface.co/datasets/zjushine/nero-move-all.nero-allThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"tcp.x",
"tcp.y",
"tcp.z",
"tcp.r1",
"tcp.r2",
"tcp.r3",
"tcp.r4",
"tcp.r5",
"tcp.r6",
"gripper.pos"… See the full description on the dataset page: https://huggingface.co/datasets/zjushine/nero-all.nero_bag_box
nero_bag_box — dual-arm bag/box manipulation (LeRobot v2.0)
Teleoperated demonstrations on the AgileX NERO dual-arm rig (two 7-DoF
S-R-S arms), collected with a Pico 4 WebXR teleop rig and cut into
single-task clips. Ready for π0.5 / openpi-style fine-tuning.
Tasks
Put the bag into the box
Pour the bag out of the box
Features
key
shape
notes
observation.state
float32[16]
left joint1–7 (rad), left gripper (0–1, measured), right joint1–7… See the full description on the dataset page: https://huggingface.co/datasets/NoahWeiss/nero_bag_box.datause-ner
Datause NER (catch-all DATA_MENTION + probe configs)
Catch-all NER views over rafmacalaba/datause-probe-v3 passages (29,346 spans grouped into passage examples). Single entity type DATA_MENTION: every candidate span is tagged, keeps and drops alike — the probe head (not NER tags) owns the keep/drop boundary. No NAMED/DESCRIPTIVE/VAGUE subtypes, no NON_MENTION.
Per-origin thresholds (head best-F1, published holdout sweep)
origin
threshold… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-ner.so101-nerds-combined-v2This dataset was created using LeRobot.
580 episodes: 534 Genesis sim episodes plus the 46 real SO-101 teleop episodes from shane1027/so101-nerds-teleop-real, merged for sim-to-real training.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 580,
"total_frames": 298256,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps":… See the full description on the dataset page: https://huggingface.co/datasets/shane1027/so101-nerds-combined-v2.grab-single-nerds-clusterThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 16,
"total_frames": 7567,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:16"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/shane1027/grab-single-nerds-cluster.SPACCC-Spanish-NER
Dataset
Description
The merged dataset utilized in this project combines four distinct annotated datasets, all based on the Spanish Clinical Case Corpus (SPACCC), a compilation of clinical case reports from Spanish medical publications. This merged dataset encompasses a total of 16,504 sentences across 1,000 clinical cases. The dataset focuses on identifying various medical entities within clinical narratives, including symptoms, medical procedures, diseases, proteins… See the full description on the dataset page: https://huggingface.co/datasets/IEETA/SPACCC-Spanish-NER.danish-ner-sft-v1
danish-ner-sft-v1
Danish named-entity SFT rows derived from chcaa/dansk-ner
(DANSK), which annotates Danish Gigaword text with the 18 OntoNotes entity
types using the same scheme in every split. Each row asks for a random subset
of entity types (1-6, including types absent from the passage so the empty
marker is exercised) and renders the answer in one of fourteen output formats.
Ten are key-value, listing the extracted entities: JSON, key: value,
key=value, [key] value, value… See the full description on the dataset page: https://huggingface.co/datasets/jensjepsen/danish-ner-sft-v1.vc-deal-flow-signal
Startup GitHub Engineering Velocity Panel
A longitudinal dataset of public GitHub engineering-activity signals for venture-backed startups. It is published under CC BY 4.0 for reproducible research, data journalism, and analysis of alternative data in venture capital.
219 startup-period observations
55 unique startups
18 sectors
4 quarterly periods: Q3 2025, Q4 2025, Q1 2026, and Q2 2026
No missing values in the primary table
Version: 1.0.0
The 219 rows are startup-period… See the full description on the dataset page: https://huggingface.co/datasets/the-data-nerd/vc-deal-flow-signal.ner_datasetso101-nerds-teleop-realThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 47,
"total_frames": 18757,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:47"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/shane1027/so101-nerds-teleop-real.so101-nerds-sim-v2This dataset was created using LeRobot.
534 scripted-expert episodes generated in Genesis (single Nerds gummy cluster -> portafilter basket). Scene appearance is matched to the real rig by towel channel ratios, and the wrist roll is pinned to the human teleop convention.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 534,
"total_frames": 280050,
"total_tasks": 1,
"chunks_size": 1000… See the full description on the dataset page: https://huggingface.co/datasets/shane1027/so101-nerds-sim-v2.fever-ner
FEVER Entity Retrieval Benchmark
Frozen benchmark for evaluating retrieval methods on the BEIR FEVER dataset (5.4M Wikipedia articles, 6,666 test queries). All data is pre-built so you can test a new method without re-running BM25 or dense retrieval.
Files
Core benchmark data (for testing new methods)
File
Size
What it is
beir_pool.json
31 MB
BM25 top-100 candidate pool (k1=1.2, b=0.75). 6,666 queries, each with 100 candidate docids +… See the full description on the dataset page: https://huggingface.co/datasets/Kim-el/fever-ner.NER_Twitter_Col_model_toLowerCasedanish-ner-grpo-v1
danish-ner-grpo-v1
Danish NER rows over DANSK passages, generated by scripts/gen_ner_sft.py with --exclude-src against danish-ner-sft-v1, so no passage here appears in that dataset's train split. Built for GRPO: the intended policy already fits the v1 train split (reward ~1.0).
split
rows
train
9000
eval
400
eval_format
400
val
400
nero_pick2This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "nero_follower",
"total_episodes": 36,
"total_frames": 19971,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:36"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/yuhangSu/nero_pick2.NER_Twitter_Col_model_uncleanedgigatrue-nerdata_base_nerAashraf995__Creative-7B-nerd-details
Dataset Card for Evaluation run of Aashraf995/Creative-7B-nerd
Dataset automatically created during the evaluation run of model Aashraf995/Creative-7B-nerd
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 latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Aashraf995__Creative-7B-nerd-details.eval_grab-single-nerds-clusterThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 14,
"total_frames": 9427,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:14"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/shane1027/eval_grab-single-nerds-cluster.nero_egg_adv_pi05_v2This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "bi_nero_follower",
"total_episodes": 122,
"total_frames": 613123,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:122"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/ming326/nero_egg_adv_pi05_v2.ner-fashion-brands
Ner Fashion Brands
This dataset originally appear as part of
this tutorial. The goal
of the dataset is to detect fashion brands in Reddit Comments.
For more details, be sure to read this blogpost.
jeffmeloy__Qwen2.5-7B-nerd-uncensored-v1.4-details
Dataset Card for Evaluation run of jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.4
Dataset automatically created during the evaluation run of model jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.4
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… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/jeffmeloy__Qwen2.5-7B-nerd-uncensored-v1.4-details.nerel_simple