datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
olympiad_style_integer_math_problems
Olympiad Math Corpus
Version: v2.1.1
Release date: 2026-05-03
59,486 synthetically generated olympiad-style math problems with verified integer answers and formal computation graphs.
Loading
from datasets import load_dataset
ds = load_dataset("mihailgribov/olympiad_style_integer_math_problems", split="train")
lemma_applicability is stored as list[{lemma, status}] rather than a sparse dict (required for Arrow-based consumers). To convert to a dict for local use:… See the full description on the dataset page: https://huggingface.co/datasets/mihailgribov/olympiad_style_integer_math_problems.styleolympiad_style_integer_math_reasoning
Olympiad Math Reasoning Traces
Version: v1.0.2
Release date: 2026-04-19
64,763 full model reasoning traces for olympiad-style math problems with verified integer answers. This dataset contains only correct and non-truncated traces — every record contains a terminal \boxed{...} answer (within the last 500 characters of the response) that matches the expected integer exactly, and none of the responses hit the model's generation-token cap. Intended for distillation and supervised… See the full description on the dataset page: https://huggingface.co/datasets/mihailgribov/olympiad_style_integer_math_reasoning.Open-LLM-Benchmark
Open-LLM-Benchmark
Dataset Description
The Open-LLM-Leaderboard tracks the performance of various large language models (LLMs) on open-style questions to reflect their true capability. The dataset includes pre-generated model answers and evaluations using an LLM-based evaluator.
License: CC-BY 4.0
Dataset Structure
An example of model response files looks as follows:
{
"question": "What is the main function of photosynthetic cells within a plant?"… See the full description on the dataset page: https://huggingface.co/datasets/Open-Style/Open-LLM-Benchmark.cpp-code-code_search_net-style
C++ Dataset
documentation source: https://huggingface.co/docs/datasets/main/en/repository_structure
Supported Tasks and Leaderboards
language-modeling: The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.
Language
C++ programming language
Dataset Structure
Data Instances
A data point consists of a function code along with its documentation.… See the full description on the dataset page: https://huggingface.co/datasets/malteklaes/cpp-code-code_search_net-style.task140_detoxifying-lms_classification_style
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task140_detoxifying-lms_classification_style
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+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task140_detoxifying-lms_classification_style.comedy-style-instruct
LLM Comedy Tunes: Instruction Tuning for Humor
Dataset Repo: 2stacks/comedy-style-instruct
Current version: v2 (2026-05) — see Version History
Overview
A curated collection of instruction-tuning examples designed to teach
Large Language Models how to respond with humor, wit, and comedic timing.
This dataset is intended for non-commercial educational and research use
in the area of style transfer and prompt-conditioned text generation.
Total examples: 316
License:… See the full description on the dataset page: https://huggingface.co/datasets/2stacks/comedy-style-instruct.elon-style-dataset
Elon Style Dataset — elon-style-dataset
Fine-tuning dataset for training a conversational model that mimics
Elon Musk's private texting style: short punchy replies, sarcasm, visionary takes,
crypto/tech opinions, and authentic multi-turn cadence.
Dataset Stats
Split
Examples
Avg Response Length
train
20,900
~88 chars
validation
1,100
~91 chars
47–50% of responses contain emoji (authentic texting energy)
57% short replies (<60 chars) — punchy, human
33%… See the full description on the dataset page: https://huggingface.co/datasets/ceoelonmusk/elon-style-dataset.golang-programming-style-best-practicesNote: WIP - This dataset has not yet been curated to remove duplicates and filler
Dataset trained on several popular open source Golang style guides and the effective go book.
Source training data:
https://go.dev/doc/effective_go
https://github.com/uber-go/guide/blob/master/style.md
https://google.github.io/styleguide/go/best-practices
Q&A generated using Augment Toolkit
Generation models:
yi-large (API access kindly donated by 01.ai)
hermes-2-theta-llama-3-8b
qwen2-72b-instruct
task933_wiki_auto_style_transfer
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task933_wiki_auto_style_transfer
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+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task933_wiki_auto_style_transfer.korean-writing-style-instruct
한국어 문체 데이터셋
여러 분야의 문체(문학, 일상적 대화, 고전시가 등)를 포함한 합성 데이터셋입니다. 모델에게 여러 문체를 출력하는 능력을 가르치기 위해 제작되었습니다. 훈련시키실 때, 일반 인스트럭스 데이터셋과 혼용해서 사용하시는 것을 추천드립니다.
이 데이터셋은 apache-2 라이선스로 자유롭게 이용하실 수 있습니다. 데이터는 Glaive 플랫폼을 통해 합성되었고, 한글이 아닌 출력은 걸러냈습니다.
task927_yelp_negative_to_positive_style_transfer
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task927_yelp_negative_to_positive_style_transfer
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/task927_yelp_negative_to_positive_style_transfer.fashion-style-instruct
Style Chatbot Dataset
A style recommendation dataset that contains input (body type and personal clothing style), context (event context) and response triplets. The responses are GPT 3.5 generated outfit combination recommendations given the input body type and personal style prompt and the target / context event.
Our dataset contains a variety of events such as business functions, cocktail parties, casual gatherings, fancy dates, etc. See an example Mistral-based finetuned model.
task955_wiki_auto_style_transfer
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task955_wiki_auto_style_transfer
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+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task955_wiki_auto_style_transfer.Style2Code_datasets
📦 Dataset Source and Processing
The dataset for this project is derived from the iamtarun/python_code_instructions_18k_alpacadataset, which contains approximately 18,000 Python code snippets paired with instructions. It was designed to provide high-quality samples for instruction-driven code generation tasks.
To enrich the style diversity and support style-controllable generation, we employed three powerful large language models—DeepSeek, Qwen, and Doubao—to generate diverse code… See the full description on the dataset page: https://huggingface.co/datasets/DUTAOZHANG/Style2Code_datasets.stylematch-outfits
StyleMatch — Synthetic Fashion Outfit Descriptions
10,751 English outfit descriptions, fully synthetic, generated with
Qwen/Qwen2.5-1.5B-Instruct
for the StyleMatch outfit-recommendation app (Intro to Data Science final project).
🚀 Try the live app: StyleMatch on HF Spaces
Schema
Column
Description
outfit_id
stable row id
style
one of 16 fashion styles (casual, formal, streetwear, ...)
season
spring / summer / fall / winter
occasion
one of 14… See the full description on the dataset page: https://huggingface.co/datasets/22Danielle/stylematch-outfits.style-aware-paraphraser-author-bank-reddit
Style-Aware Paraphraser — Reddit Author Targets Bank
A bank of 12 000 anonymous Reddit authors, each represented by 16 exemplar
comments plus 5 Mistral-7B paraphrases of each. This is what feeds the
target-style side of our paraphraser: pick a row, pass reference_text
and paraphrase_reference_text to
rrivera1849/style-aware-paraphraser-mistral7b,
and the model will rewrite any machine text in that author's style.
Reddit usernames are not included; the bank carries only the… See the full description on the dataset page: https://huggingface.co/datasets/rrivera1849/style-aware-paraphraser-author-bank-reddit.sustainability-report-emissions-instruction-styleThe sustainability-report-emissions dataset converted into instruction-style JSONL format for direct consumption by SFTTrainer, axolotl, etc. The prompt consists of an instruction and text extracted from relevant pages of a sustainability report. The output is generated using the Mixtral-8x7B-v0.1 model and consists of a JSON string containing the scope 1, 2 and 3 emissions as well as the ids of pages containing this information. The dataset generation scripts are at this GitHub repo. An… See the full description on the dataset page: https://huggingface.co/datasets/nopperl/sustainability-report-emissions-instruction-style.tool-reasoning-sft-TOOLS-hermes-reasoning-tool-style-data-cleaned-rectified-115k
Agentic Tool-Use SFT Mix
111,295 additional multi-turn agentic trajectories across four task families, following the same strict reasoning + tool-call FSM format. Combined with the original 3,827 deep-research trajectories, the dataset totals 115,122 samples.
Distribution
Category
Samples
Full
Compact
Deep Research (original)
3,827
100%
—
Multi-Turn Tool Orchestration
45,776
54%
46%
Deep Research
34,282
71%
29%
Codebase Retrieval
17,473
69%
31%… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-hermes-reasoning-tool-style-data-cleaned-rectified-115k.jfv-french-style-conditioning-dataset-v1.0
JFV French Paired Style-Conditioning Dataset
At a Glance
Item
Value
Language
French
Source
Single-author blog corpus, 2005–2025
Public release
v1.0
Aligned units in public release
1,484
Texts in public aligned release
7,420
Original experiment
1,492 aligned units / 7,460 texts
Generated conditions
Ministral baseline; profile; profile + five-shot examples
Primary use
Paired study of stylistic conditioning and evaluation-metric validity… See the full description on the dataset page: https://huggingface.co/datasets/PeggyVallin/jfv-french-style-conditioning-dataset-v1.0.style-aware-paraphraser-outputs
Style-Aware Paraphraser Outputs
⚠️ Intended for evaluating machine-text detectors, not for training them.
Each row is a (human, machine, adversarial-paraphrase) triple drawn from a
specific evaluation slice of three domains. The author bank that produced
these outputs overlaps with the rows here, so training a detector against
this data would not generalize. Use it to score a detector, not to fit one.
Final outputs of the style-aware paraphraser from
Attacks on Machine-Text… See the full description on the dataset page: https://huggingface.co/datasets/rrivera1849/style-aware-paraphraser-outputs.StyleBench-ZH
StyleBench-ZH
Chinese style editing: template dependence, efficiency, coherence and suitability for the writing context.
This is the complete public v0.1.1 release of StyleBench-ZH: 241 source families and 482 instances. Each family contains a natural model output and a controlled variant. It is part of Editorial Guard.
Splits
Split
Families
Instances
calibration
40
80
development
150
300
validation
50
100
locked_test
1
2
The published… See the full description on the dataset page: https://huggingface.co/datasets/RedinGhost/StyleBench-ZH.Human-Style-Answers
Human Style Answers
This Datasets contains question and answers on different topics in Human style. (For Chatbots training)
This Datasets is build using TOP AI like (GPT4, Claude3 , Command R+, etc.)
Dataset Details
Description
The Human Style Response Dataset is a rich collection of question-and-answer pairs, meticulously crafted in a human-like style. It serves as a valuable resource for training chatbots and conversational AI models. Let's dive into the… See the full description on the dataset page: https://huggingface.co/datasets/innova-ai/Human-Style-Answers.Associated-Press-Stylebook
Federal Travel Regulation
Maintainer: Terry Eppler
Owner: US Federal Government
Dataset Summary
This dataset contains document-grounded question-and-answer records based on the Federal Travel Regulation, as reproduced in the source volume of title 41 of the Code of Federal Regulations.
The Federal Travel Regulation establishes government-wide policies governing official civilian travel and relocation at Federal expense. It addresses temporary duty travel… See the full description on the dataset page: https://huggingface.co/datasets/leeroy-jankins/Associated-Press-Stylebook.gertrude-stein-style-sft
Gertrude Stein Style Transfer SFT Dataset
A supervised fine-tuning dataset for training language models to write in Gertrude Stein's distinctive literary style. Generated from her 1909 novel "Three Lives."
Dataset Description
This dataset contains instruction-response pairs where:
Input: A prompt asking the model to write in Gertrude Stein's style about a specific scene
Output: The actual text from Stein's novel
The dataset uses diverse prompt templates (15 variations)… See the full description on the dataset page: https://huggingface.co/datasets/MuratcanKoylan/gertrude-stein-style-sft.Her-Samantha-Style
Ultra-High Quality Samantha Dataset
A meticulously curated conversational AI dataset designed to capture the essence of Samantha from the movie "Her" - characterized by emotional intelligence, philosophical depth, and authentic conversational patterns.
Dataset Summary
This dataset contains 20,000 ultra-high quality conversational responses that have been systematically filtered and scored based on Samantha's distinctive characteristics from the 2013 film "Her". Each… See the full description on the dataset page: https://huggingface.co/datasets/WasamiKirua/Her-Samantha-Style.stylegen
stylegen
Synthetic (prompt, response) pairs for multi-attribute style steering. Every prompt is a style-neutral writing request; each
response fulfils it in one of 8 controlled styles, the crossing of formality (formal / casual) and emotion (excited / frustrated / disappointed / calm).
Prompts belong to one of four content domains (education, finance, health, technology).
Design
One brief (a few plain facts about an emotionally open event: a schedule change, a… See the full description on the dataset page: https://huggingface.co/datasets/knoveleng/stylegen.python-google-style-docstrings
Overview
This dataset contains Python code-docstring pairs, whereas the docstrings are in Google style. A Google style docstring is structured as follows:
<Description of the code>
Args:
<var1> (<data-type>) : <description of var1>
<var2> (<data_type>) : <description of var2>
Returns:
<var3> (<data-type>) : <description of var3>
Raises:
<var4> (<data-type>) : <description of var4>
The format varies widely (like additional sections such as Examples, Notes, etc) but generally… See the full description on the dataset page: https://huggingface.co/datasets/Mir-2002/python-google-style-docstrings.japanese-style-contrast-dataset
LLMの文体差理解向上のための日本語データセット
本データセットは、新居浜工業高等専門学校と株式会社APTOの共同研究により構築された、日本語LLMの文体差（話し言葉・書き言葉）理解性能の向上を目的とした学習用データセットです。
新居浜高専 電子工学専攻の高専生が研究の企画立案からデータセット構築、追加学習および評価実験までを主導し、指導教員の助言と株式会社APTOからの計算環境の提供・技術的指導のもとで作成されました。
本研究の成果は言語処理学会第32回年次大会(NLP2026)にて発表されます。
研究の背景
大規模言語モデル（LLM）は日本語の質問応答や文章生成において高い性能を示す一方で、話し言葉と書き言葉といった文体差に対する理解には課題が残されております。特に話し言葉では、口語的表現や省略を含むため理解性能が不安定になりやすく、文体差への対応は日本語LLMにおける重要な課題となっています。… See the full description on the dataset page: https://huggingface.co/datasets/APTO-001/japanese-style-contrast-dataset.full-html-stying-dataset-generated-css-from-style-plan
Generated CSS From Style Plan
kogai/full-html-stying-dataset-generated-css-from-style-plan contains generated_css_from_style_plan.jsonl, a JSONL dataset with 44458 synthetic examples. Model-generated CSS outputs conditioned on source HTML, user style requests, and structured style plans.
Schema
chat_template_overhead_tokens: field present in the JSONL records.
created_at: field present in the JSONL records.
input_html: source HTML before Tailwind classes are… See the full description on the dataset page: https://huggingface.co/datasets/kogai/full-html-stying-dataset-generated-css-from-style-plan.