datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
structured-wikipedia
Dataset Card for Wikimedia Structured Wikipedia
Quick Links
Wikimedia Enterprise
Structured Contents Documentation
Data Dictionary
Wikimedia Attribution Framework
Meta-Wiki Discussion
Dataset Summary
Pre-parsed English and French Wikipedia articles, extracted using the Wikimedia Enterprise Snapshot API.
This dataset contains all articles of the English and French language editions of Wikipedia, pre-parsed and output as structured data with a… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/structured-wikipedia.sharegpt-structured-output-json
ShareGPT-Formatted Dataset for Structured JSON Output
Dataset Description
This dataset is formatted in the ShareGPT style and is designed for fine-tuning large language models (LLMs) to generate structured JSON outputs. It consists of multi-turn conversations where each response follows a predefined JSON schema, making it ideal for training models that need to produce structured data in natural language scenarios.
Usage
This dataset can be used to train LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Arun63/sharegpt-structured-output-json.Nemotron-RL-Instruction-Following-Structured-Outputs-v2
Dataset Description:
Split 1: Direct Generation tests the model’s ability to perform freeform text structured outputs on JSON, YAML, and XML data, varying the complexity and presentation of the schema.
Split 2: Diversified Tasks adds 2 additional output formats: TOML and CSV, while increasing problem types to Direct Extraction from document, Translation between formats, Multistep Translation from known data, Multistep Extraction from unrelated context, Schema-Only Generation for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Structured-Outputs-v2.a3-rl-laion_nemotron-gym-instruction-following-structuredpxr-structure-pose-pool
PXR Structure Challenge — Full Multi-Model Pose Pool (184 ligands)
Every protein–ligand pose generated during the OpenADMET PXR (pregnane X receptor / NR1I2)
structure-prediction challenge, released openly with per-pose labels so the community can
reuse the compute already spent — and, we hope, crack the problem this data makes visible.
What's here
poses/<model>/<SID>.pdb — one best pose per (model, ligand). Protein chain A + ligand
(resname LIG). 15 models, up… See the full description on the dataset page: https://huggingface.co/datasets/xX-its-amit-Xx/pxr-structure-pose-pool.nomad_structure
Dataset Details
Dataset Description
A subset from NOMAD dataset, which is a database of DFT computed results of materials.
This subset consists of cif structures of around 0.5 million bulk stable materials and their geometric and structural information.
All materials in this dataset are modeled using Density Functional Theory using GGA functional.
Curated by:
License: CC BY 4.0
Dataset Sources
original data source
Citation
BibTeX:… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/nomad_structure.structured-file-audit-benchmark
Paper Data Release
This directory contains the benchmark dataset and evaluation scripts accompanying the ACL submission: the three data splits (SC-Flat, SC-Book, SC-Pro) and the code needed to score them.
Contents
datasets/
Benchmark data and per-task manifests for the three paper-facing splits.
datasets/sc_flat/data
SC-Flat is derived from DaBench, augmented with a replayable perturbation
injected into each task's input artifact. Each task… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-structured-agent/structured-file-audit-benchmark.neurips-spectraThe dataset from Albert's et al, downloaded from zenodo. It's on here for easier access and organisation.
MISATO_MDNemotron-RL-instruction_following-structured_outputs
Dataset Description:
The Nemotron-RL-instruction_following-structured_outputs dataset tests the ability of the model to follow output formatting instructions under schema constraints under the JSON format. Each problem consists of three components: The document, output formatting Instruction (Schema), and question. The dataset varies the difficulty of each problem by varying the location of instructions, the comprehensiveness of instructions, the complexity of the schema, and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs.secondary_structure_predictionbeacon-secondary-structure
BEACON — Secondary_structure_prediction
RNA secondary-structure prediction data with nucleotide-level pair matrices.
Official data from the shared BEACON/RNABenchmark Drive folder:
https://drive.google.com/drive/folders/19ddrwI8ycvIxkgSV3gDo_VunLofYd4-6?hl=en.
This repository is the standardized Hugging Face publication of the official
task data. The data/ directory is the canonical viewer-friendly layer, and
the original file contents and source names are preserved for… See the full description on the dataset page: https://huggingface.co/datasets/jiahaozhang2003/beacon-secondary-structure.ImagePulseV2-Edit-Structure
ImagePulseV2 Dataset - Image Structure
The ImagePulseV2 dataset is a collection we constructed for training the Diffusion Templates series of models. It comprises multiple subsets generated using models such as Z-Image-Turbo, Qwen-Image, and Qwen-Image-Edit, based on prompts randomly sampled from DiffusionDB.
Open-source code: DiffSynth-Studio
Technical report: arXiv
Project homepage: GitHub
Documentation: English Version, Chinese Version
Online demo: ModelScope Studio
Model… See the full description on the dataset page: https://huggingface.co/datasets/DiffSynth-Studio/ImagePulseV2-Edit-Structure.pseudo-camera-10k-structured-json
pseudo-camera-10k, structured JSON captions
The 9,997 training images from bghira/pseudo-camera-10k, recaptioned into the structured JSON caption schema that Ideogram 4 consumes. The images are unchanged: free photographs from world class photographers, Lanczos-resized so the shorter edge is 1024px, nothing upsampled.
The original dataset carries short CogVLM prose captions. This one replaces them with one JSON object per image describing the scene at three levels: an overall… See the full description on the dataset page: https://huggingface.co/datasets/terminusresearch/pseudo-camera-10k-structured-json.structure-heavy-token-quality-datasetnemotron-gym-instruction-following-structured-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-instruction-following-structured-qwen3.5-122b-131k-opencode-traces.structured-wikipedia
Dataset Card for Wikimedia Structured Wikipedia
Quick Links
Wikimedia Enterprise
Structured Contents Documentation
Data Dictionary
Wikimedia Attribution Framework
Meta-Wiki Discussion
Dataset Summary
Pre-parsed English and French Wikipedia articles, extracted using the Wikimedia Enterprise Snapshot API.
This dataset contains all articles of the English and French language editions of Wikipedia, pre-parsed and output as structured data with a… See the full description on the dataset page: https://huggingface.co/datasets/Aregay01/structured-wikipedia.structured-cpt
Structured CPT - JSON + SQL pretrain documents
SmolLM2-1.7B continued-pretraining shard of structured documents. Each document
is a <task> / <input> / <output> block whose <output> is a canonical
JSON object, terminated by the SmolLM2 end-of-text token ``.
Sources:
source
description
rows
shards
repeat
sql_bmc2
b-mc2 sql-create-context -> JSON (4 keys, stub explanation)
392,885
1
5
sql_gretelai
gretelai synthetic_text_to_sql -> JSON (4 keys)
529,255
1
5… See the full description on the dataset page: https://huggingface.co/datasets/domofon/structured-cpt.nemotron-gym-instruction-following-structured-minimax-m27-131k-tracesturkish-structured-summarization-1.5m
Turkish Structured Summarization 1.5M v2
Üç cümlelik kurgusal operasyon kayıtları ve kısa Türkçe özetleri.
Doğrulanmış boyut
Train: 1,470,000
Validation: 15,000
Test: 15,000
Toplam: 1,500,000
Ana görev sütunları: id, document, summary, domain
Provenance
Veri insan mesajlarından, belgelerinden veya web kazımasından alınmamıştır. Tamamı
depodaki üretici koduyla deterministik olarak oluşturulur. Her satırda source_type,
provenance, generator_version… See the full description on the dataset page: https://huggingface.co/datasets/GoktugD/turkish-structured-summarization-1.5m.eu-ai-act-structured
EU AI Act, structured
Regulation (EU) 2024/1689 (the Artificial Intelligence Act) as tables: every article, recital, annex and definition, 677 obligations coded by actor, risk tier, application date and penalty basis, plus milestones, national competent authorities and fine tiers.
Built 2026-09-08 by SafeLegalAI (Cognesio LLP) from the official English texts served by the Publications Office of the European Union (Cellar): the consolidated text as of 27 July 2026 (CELEX… See the full description on the dataset page: https://huggingface.co/datasets/safelegalaidata/eu-ai-act-structured.task210_logic2text_structured_text_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task210_logic2text_structured_text_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task210_logic2text_structured_text_generation.uniref50-sorted-structure-tokenstructured_imagesMLR_structured_trajectory
Reasoning Trajectories with Step-Level Annotations
This dataset contains structured reasoning trajectories introduced in (ICLR 2026) Enhancing Language Model Reasoning with Structured Multi-Level Modeling.
Compared with the full-trajectory release, this dataset is a cleaned and segmented version designed for research on hierarchical reasoning, trajectory supervision, and multi-step policy training. Each example contains the original prompt and response fields together with a… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/MLR_structured_trajectory.philosophy-classics-structured
Classical Decision Frameworks — Philosophy Dataset
Structured public domain philosophical texts focused on decision-making, leadership,
and organizational ethics. All content is in the public domain.
Content
Works from classical philosophy structured for AI analysis:
Stoic decision principles (Marcus Aurelius, Epictetus, Seneca)
Political philosophy (Machiavelli, Aristotle)
Virtue ethics (Aristotle, Plato)
Sources
All works published before 1928… See the full description on the dataset page: https://huggingface.co/datasets/gmahia/philosophy-classics-structured.phased-self-discover-mistral-structured-5-shot-bbh-evalPDB-Monomeric-Structure-ESMFold2
PDB-Monomeric-Structure-ESMFold2
Monomeric, protein-only PDB structure dataset for minimum ESMFold2-style
training. Each row is one eligible single-chain biological assembly with a
canonical amino-acid sequence input and all-atom protein labels in atom37.
Labels
atom37_positions: residue x 37 x 3 coordinates, with zeros for missing atoms.
atom37_mask: residue x 37 resolved-atom mask.
aatype, residue_index, auth_seq_id, insertion_code, residue_name, ca_mask.… See the full description on the dataset page: https://huggingface.co/datasets/Synthyra/PDB-Monomeric-Structure-ESMFold2.sft-tool-calling-structured-output-v1
vericava/sft-tool-calling-structured-output-v1
Dataset to train (SFT) 3-20B LLMs for tool calling and structured outputs/classifications.
Includes contents in English as well as some Japanese.
protein-secondary-structure-netsurfp
NetSurfP-3.0 Secondary-Structure Splits
This dataset repo contains NetSurfP-derived protein secondary-structure labels
converted for Protein-I-JEPA probe training and evaluation.
Source page: https://services.healthtech.dtu.dk/services/NetSurfP-3.0/5-Dataset.php
Profile: hhblits
Labels are Q3 per-residue labels:
H: helix
E: beta strand
C: coil/other
.: ignored residue for loss and accuracy
Splits
Split
Rows
JSONL
TSV
train
10348
train.jsonl
tsv/train.tsv… See the full description on the dataset page: https://huggingface.co/datasets/lamm-mit/protein-secondary-structure-netsurfp.