datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
korean-bar-exam-hard-current-law-precedent-sft-1000
Korean Current-Law Bar Exam Hard SFT 1000
대한민국 현행 법령을 기준으로 만든 변호사시험 선택형 고난도 스타일 SFT 데이터 1,000문항입니다.
초기 직접 조문확인형 생성본은 실제 제14ㆍ15회 변호사시험보다 쉬워서, 이 버전은 다음 기준으로 다시 만들었습니다.
ㄱ/ㄴ/ㄷ/ㄹ 복합정오형 중심
甲/乙/丙, 검사ㆍ사법경찰관ㆍ행정청ㆍ회사ㆍ소송당사자 등이 등장하는 사례형 비중 확대
단순 근거 조문 선택형 제거
정답뿐 아니라 각 지문별 O/X 이유와 참고 법령 조문 제공
제15회 변호사시험 data/questions.csv와 높은 유사도 문항 제외
Files
data/questions.csv: Hugging Face preview용 메인 CSV입니다.
sft/train.jsonl: messages 형식 SFT용 JSONL입니다.
metadata/qa_report.json: 생성 수량, 난도 관련… See the full description on the dataset page: https://huggingface.co/datasets/gyung/korean-bar-exam-hard-current-law-precedent-sft-1000.Classical-Mechanics-Equations-Dataset_SFT-or-LoRA
Classical Mechanics Equations Dataset (SFT / LoRA Ready)
A structured dataset of 64 classical mechanics equations from Newtonian,
Lagrangian, and Hamiltonian mechanics, expanded into 448 instruction-tuning
rows across three task types: equation explanation, Q&A, and derivation.
Designed for fine-tuning LLMs on physics reasoning, STEM Q&A, and
equation understanding tasks.
Overview
Property
Value
Domain
Classical Mechanics (Physics)
Total rows
448
Train… See the full description on the dataset page: https://huggingface.co/datasets/ChaoticEconomist/Classical-Mechanics-Equations-Dataset_SFT-or-LoRA.efemerides-cumana-sft-qa-small
Efemérides de Cumaná/Venezuela – Dataset de Preguntas y Respuestas para SFT
Descripción general
Este dataset contiene un conjunto curado de pares pregunta–respuesta en español, diseñado específicamente para ajuste fino supervisado (Supervised Fine-Tuning, SFT) de modelos de lenguaje de gran tamaño (LLMs). El contenido se centra en la historia de Cumaná, ciudad de Venezuela durante el período de la Guerra de Independencia, con énfasis en la interpretación histórica, el… See the full description on the dataset page: https://huggingface.co/datasets/rjcg/efemerides-cumana-sft-qa-small.Jazz-Blues-Music-Dataset_SFT-or-LoRA
Jazz & Blues Music Dataset (SFT / LoRA Ready)
A structured dataset covering 82 iconic Jazz and Blues songs, 21 artist
profiles, and 41 historical events, expanded into 1,219
instruction-tuning rows across 7 task types.
Designed for fine-tuning LLMs on music knowledge, cultural history, artist
biography, and domain-specific Q&A tasks.
Overview
Property
Value
Domain
Jazz & Blues Music
Total rows
1,219
Train split
1,036 (85%)
Validation split
91 (~7.5%)… See the full description on the dataset page: https://huggingface.co/datasets/ChaoticEconomist/Jazz-Blues-Music-Dataset_SFT-or-LoRA.Drone-flight-monitoring-reasoning-SFT
Drone-flight-monitoring-reasoning-SFT
Dataset Description
本数据集是一个专注于无人机飞行安全领域的中文问答数据集，采用了Chain-of-Thought (CoT) 的格式。它旨在用于练习大语言模型的微调训练，使其能够模拟专家思考过程，并针对无人机安全相关问题生成包含推理步骤的结构化回答。微调后模型见(GabrielCheng/Deepseek-r1-finetuned-drone-safty) 。
本数据集是基于 Hugging Face 平台上的 skylink-drone-cot-datasets (pohsjxx/default-domain-cot-dataset) 进行处理和衍生的。
Dataset Structure / Data Fields
数据集中的每个样本包含以下字段：
Question (string): 关于无人机飞行安全或风险相关的问题。
Reasoning (string): 模拟模型的推理过程。
Answer… See the full description on the dataset page: https://huggingface.co/datasets/GabrielCheng/Drone-flight-monitoring-reasoning-SFT.korean-current-law-bar-exam-sft-1000
Korean Current-Law Bar Exam SFT 1000
대한민국 현행 법령을 기준으로 만든 변호사시험 선택형 스타일 SFT 데이터 1,000문항입니다.
이 데이터셋은 법무부 기출문제를 복제하지 않습니다. 기존 gyung/korean-bar-exam-moj-multiple-choice의 data/questions.csv는 난도와 과목 분포 참고 및 제15회 중복 방지 기준으로만 사용했습니다.
Files
data/questions.csv: Hugging Face preview용 메인 CSV입니다.
sft/train.jsonl: messages 형식 SFT용 JSONL입니다.
metadata/qa_report.json: 생성 수량, 과목 분포, 제15회 유사도 QA 결과입니다.
Columns
question_text: 문제와 5개 선택지
answer: 정답 번호, 1부터 5… See the full description on the dataset page: https://huggingface.co/datasets/gyung/korean-current-law-bar-exam-sft-1000.SFT_Science_AI_Gensft-conversational_datasetQuestion – Answer DatasetThe dataset contains 400 queries from two domains: Current Affairs and Creative Writing. It serves as a versatile resource for Natural Language Processing (NLP) tasks, including text classification, information retrieval, and model training.
Data attributes:
Query: The user-generated question. Data type: string.
Answer: The response provided by a team of writers and editors in markdown format, containing information related to the query.
Citations: Up to 4 credible… See the full description on the dataset page: https://huggingface.co/datasets/SoftAge-AI/sft-conversational_dataset.