datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
qwen35-2b-tool-use-qwen36-27b-curation-candidates
Full candidate collections: 2B tool use + 27B data curation
This public Dataset contains two complete, unredacted, exact-40 candidate collections:
Tool use: Qwen/Qwen3.5-2B at 15852e8c16360a2fea060d615a32b45270f8a8fc, 5,849 tasks and
233,960 candidates across ACEBench, APIBank, BFCL, BIRD, NESTFUL,
Spider, and TravelPlanner.
Data curation: Qwen/Qwen3.6-27B at 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9, 5,021
targets and 200,840 candidates, plus the source target rows and the… See the full description on the dataset page: https://huggingface.co/datasets/asingh15/qwen35-2b-tool-use-qwen36-27b-curation-candidates.excavision-curation-assets
Excavision target-conditioned curation assets
Downloadable corpus artifacts for the Curate for my site workflow in Excavision Explorer.
Canonical files
excavision_full_vitb14.npy: the original 882,728 × 768 full-frame DINOv2 ViT-B/14 embeddings in float32, used directly without PCA or dimensionality reduction (SHA-256: 1b4b235fbffea44399e347de0a16cb4b60eba2ed5a30e8d344c908a9fa1d6bb2).
excavision_curation_pool.parquet: aligned sanitized filenames and seven… See the full description on the dataset page: https://huggingface.co/datasets/Sheida1/excavision-curation-assets.ViLegalQA-Synthetic-Curation
ViLegalQA Synthetic Curation
Dataset summary
This repository releases the synthetic Vietnamese legal QA research artifacts produced in the accompanying study. The primary resource contains 10,095 synthetic QA items spanning true/false, multiple-choice, and open-ended tasks. It is accompanied by the final curation/quality annotations used in the study, plus aggregated labels for 600 items from the five-expert human calibration panel.
Manuscript: Human-Calibrated… See the full description on the dataset page: https://huggingface.co/datasets/nguyenkhanh87/ViLegalQA-Synthetic-Curation.ultrafeedback-binarized-curation
Ultrafeedback binarized dataset using the mean of preference ratings
Introduction
This dataset contains the result of curation work performed by Argilla (using Argilla 😃).
After visually browsing around 200 examples using the sort and filter feature of Argilla, we noticed a strong mismatch between the overall_score in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response.
By adding the critique rationale to our Argilla… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ultrafeedback-binarized-curation.nemo-grpo-from083-full-edge-curation
Nemotron 0.83 Edge-Prompt Curation
This private dataset contains edge-prompt curation rollouts for the DGXChen/Tong CoT dataset.
Seed edge prompts: 134
New rollout rows after seed exclusion: 7668
New edge prompts: 1648
Full edge prompts, seed plus rollout: 1782
Full dataset rows: 7830
Edge rate over full dataset: 0.2276
The Hugging Face dataset viewer is configured to load only data/full_edge_prompts_seed_plus_rollout.jsonl.
The larger rollout and metadata files remain… See the full description on the dataset page: https://huggingface.co/datasets/dvyomkesh/nemo-grpo-from083-full-edge-curation.ramen-g1-ikea-assembly-curation
RAMEN G1 IKEA Assembly — segment curation labels
Per-segment quality labels for all 533 episodes of
BitRobot/G1_WBT_Dex1_Building-Children-Table
(revision 4b5961c6f8b97ececa75ee73041b30bcbf463ae2), made by Team RAMEN for the
IROS 2026 Humanoid IKEA Assembly Challenge (Unitree G1 + Dex1-1 assembling a children's table).
Each label marks a frame range of one episode, says which skill the robot performs there, and grades how well
it is done. We used the labels to cut the episodes… See the full description on the dataset page: https://huggingface.co/datasets/Team-RAMEN/ramen-g1-ikea-assembly-curation.curation-backup-review_common_voice25_validatedcuration-ultrafeedback-scorescuration-backup-review_common_voice25_dev
Dataset Card for curation-backup-review_common_voice25_dev
This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.
Using this dataset with Argilla
To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code:
import argilla as rg
ds… See the full description on the dataset page: https://huggingface.co/datasets/Reza2kn/curation-backup-review_common_voice25_dev.curation-ultrafeedback-bad-rated6000Q_A3_curation_data_P3curation-backup-review_semiclean31_awq_wer