datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
full-structured-instruction-sft-dataset
Full Structured + Instruction SFT Corpus
Unified SFT training corpus built from Glaive, Hermes, UltraChat, and synthetic structured-output data.
Dataset repo
mdonigian/full-structured-instruction-sft-datasetRelease date: 2026-03-11
Included files
train_full_sft.jsonl: full merged and shuffled SFT dataset
source_glaive.jsonl: processed Glaive subset
source_hermes.jsonl: processed Hermes subset
source_ultrachat.jsonl: processed UltraChat subset… See the full description on the dataset page: https://huggingface.co/datasets/mdonigian/full-structured-instruction-sft-dataset.Agent-IPI-Structured-Interaction-Datasets
Dataset Card for Indirect Prompt Injection in Agent Structured Interaction Datasets
Dataset Summary
This dataset contains 470,000 QA pairs designed to study indirect prompt injection in agent-structured interactions. It is split into a training set (80%) and a test set (20%). The dataset is evenly divided into 50% clean-clean QA pairs (no prompt injection) and 50% clean-injected QA pairs (containing prompt injection). The task is to detect and remove prompt injection… See the full description on the dataset page: https://huggingface.co/datasets/Z-Edgar/Agent-IPI-Structured-Interaction-Datasets.structured_data_with_cot_dataset_512_v4
structured_data_with_cot_dataset
このデータセットは、様々な形式（JSON、XML、YAML、TOML、CSV）の構造化データと、それぞれに対応する簡潔な思考連鎖（Chain-of-Thought, CoT）推論を含む多様な例を提供します。
データセットの概要
messages: OpenAIチャット形式 (system, user, assistant)
metadata: format, complexity, schema, estimated_tokens
サポートされるデータ形式
JSON, XML, YAML, TOML, CSV
生成方法
Fakerライブラリを使用し、Pythonスクリプトで生成。検証用・テスト用に分割済み。
structured_data_with_cot_dataset_512_v5
structured_data_with_cot_dataset
このデータセットは、様々な形式（JSON、XML、YAML、TOML、CSV）の構造化データと、それぞれに対応する簡潔な思考連鎖（Chain-of-Thought, CoT）推論を含む多様な例を提供します。
データセットの概要
messages: OpenAIチャット形式 (system, user, assistant)
metadata: format, complexity, schema, estimated_tokens
サポートされるデータ形式
JSON, XML, YAML, TOML, CSV
生成方法
Fakerライブラリを使用し、Pythonスクリプトで生成。検証用・テスト用に分割済み。
v5アップデート：ランダムなスキーマ構造の生成と、最小化(minified)/ソート(sorted)の制約を追加。
structured_data_with_cot_dataset_512_v2_filtered_1structured_data_with_cot_dataset_512_v2_filtered_1
This repository provides a dataset for training models in terms of strutured outputs. The dataset is a part of u-10bei/structured_data_with_cot_dataset_512_v2.
Usage
from datasets import load_dataset
# From local data
dataset = load_dataset(
'json', data_files="ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_1", split='train'
)
print(dataset[0])
How to generate this dataset from the base one
from… See the full description on the dataset page: https://huggingface.co/datasets/ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_1.structured_data_with_cot_dataset_512_v3
structured_data_with_cot_dataset
このデータセットは、様々な形式（JSON、XML、YAML、TOML、CSV）の構造化データと、それぞれに対応する簡潔な思考連鎖（Chain-of-Thought, CoT）推論を含む多様な例を提供します。
データセットの概要
messages: OpenAIチャット形式 (system, user, assistant)
metadata: format, complexity, schema, estimated_tokens
サポートされるデータ形式
JSON, XML, YAML, TOML, CSV
生成方法
Fakerライブラリを使用し、Pythonスクリプトで生成。検証用・テスト用に分割済み。
1.5-million-Korean-Test-Questions-Structured-Analysis-Processing-Data-Sample
Description
Korean Test Questions Structured Analysis Processing Data, around 1.5 million questions, contains question types, questions, answers, explanations, etc..For subjects, include [Primary School] Korean, Mathematics, English, Social Studies, Science; [Middle School] Korean, English, Mathematics, Science, Social Studies; [High School] Korean, English, Mathematics, Physics, Chemistry, Biology, History, Geography; question Types indlude single-choice question, fill-in… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-AI/1.5-million-Korean-Test-Questions-Structured-Analysis-Processing-Data-Sample.structured_data_with_cot_dataset_512_v2_filtered_3structured_data_with_cot_dataset_512_v2_filtered_3
This repository provides a dataset for training models in terms of strutured outputs. The dataset is a part of u-10bei/structured_data_with_cot_dataset_512_v2.
Usage
from datasets import load_dataset
# From local data
dataset = load_dataset(
'json', data_files="ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_3", split='train'
)
print(dataset[0])
How to generate this dataset from the base one
from… See the full description on the dataset page: https://huggingface.co/datasets/ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_3.structured_data_with_cot_dataset_512_v2_filtered_4structured_data_with_cot_dataset_512_v2_filtered_4
This repository provides a dataset for training models in terms of strutured outputs. The dataset is a part of u-10bei/structured_data_with_cot_dataset_512_v2.
Usage
from datasets import load_dataset
# From local data
dataset = load_dataset(
'json', data_files="ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_4", split='train'
)
print(dataset[0])
How to generate this dataset from the base one
from… See the full description on the dataset page: https://huggingface.co/datasets/ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_4.humanoid-domestic-task-structured-dataset-v2
Humanoid Domestic Task Structured Dataset
Overview
This dataset contains structured human instructions for basic
household assistance scenarios. It is designed to help humanoid
agents interpret natural language commands and convert them into
clear executable task representations.
The dataset focuses on simple real-world domestic tasks that reduce
human workload and improve everyday living environments.
Key Features
Natural human-written instructions
Structured… See the full description on the dataset page: https://huggingface.co/datasets/ariefansclub/humanoid-domestic-task-structured-dataset-v2.structured_data_with_cot_dataset_512_v2_filtered_2structured_data_with_cot_dataset_512_v2_filtered_2
This repository provides a dataset for training models in terms of strutured outputs. The dataset is a part of u-10bei/structured_data_with_cot_dataset_512_v2.
Usage
from datasets import load_dataset
# From local data
dataset = load_dataset(
'json', data_files="ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_2", split='train'
)
print(dataset[0])
How to generate this dataset from the base one
from… See the full description on the dataset page: https://huggingface.co/datasets/ToshiyukiNH/structured_data_with_cot_dataset_512_v2_filtered_2.structured_data_with_cot_dataset_512_v2_dpo1.5-million-Korean-Test-Questions-Structured-Analysis-Processing-Data-Sample
Description
한국어 시험 문제 구조화 분석·가공 데이터로, 약 150만 개의 시험 문제를 포함하고 있습니다. 문제 유형, 문제, 정답, 해설 등의 정보를 포함하며, 과목은 [초등학교] 국어, 수학, 영어, 사회, 과학; [중학교] 국어, 영어, 수학, 과학, 사회; [고등학교] 국어, 영어, 수학, 물리, 화학, 생물, 역사, 지리로 구성되어 있습니다. 문제 유형에는 객관식, 빈칸 채우기, 참·거짓 문제, 단답형 문제 등이 포함됩니다. 본 데이터셋은 대규모 교과 지식 강화 및 학습 데이터 구축 등의 작업에 활용할 수 있습니다.
자세한 내용은 아래 링크를 참고해 주세요: https://ko.nexdata.ai/datasets/llm/1634?source=Hf.kr
Specifications
Data content
한국어 K12 시험 문제
Amount
약 150만 개의… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-kr/1.5-million-Korean-Test-Questions-Structured-Analysis-Processing-Data-Sample.Indian_Laws_Structured_Legal_Dataset
📚 Indian Legal Acts Dataset (Structured Sections)
🧾 Overview
This dataset provides structured, machine-readable legal text from major Indian statutes, including:
Bharatiya Nyaya Sanhita, 2023 (BNS)
Code of Criminal Procedure, 1973 (CrPC)
Code of Civil Procedure, 1908 (CPC)
Indian Evidence Act, 1872 (IEA)
Negotiable Instruments Act, 1881 (NIA)
Motor Vehicles Act, 1988 (MVA)
Indian Divorce Act, 1869 (IDA)
Each entry represents a section or chunk of a section… See the full description on the dataset page: https://huggingface.co/datasets/dheerajpabolu/Indian_Laws_Structured_Legal_Dataset.JSON-STRUCTURED-DATA-FOR-SYMPTOMS-SFT_DPO-SUPPORTEDThis dataset is high-consistency instruction tuning dataset.
converting messy, subjective human health-style text → structured, non-diagnostic extraction format
1.Literal extraction discipline
2.Source separation logic - very strong schema grounding training if DPO
3.Anti-inference constraint
1.High ambiguity coverage
2.Contradiction handling included
3.Minimization bias detection
This dataset is a STRICT schema regulation.
Does well at:
strict extraction
preserving uncertainty words… See the full description on the dataset page: https://huggingface.co/datasets/sadnjasdkn/JSON-STRUCTURED-DATA-FOR-SYMPTOMS-SFT_DPO-SUPPORTED.structured_data_with_cot_dataset_512_v2_dpo_before_processingfinetune-Sample-Structured-Datasetstructured_dataset