datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column… See the full description on the dataset page: https://huggingface.co/datasets/b-mc2/sql-create-context.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/rAVEUK/Creative-Professionals-Agentic-Tasks-1M.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/kryp1234/Creative-Professionals-Agentic-Tasks-1M.creativemath_fullpython_debugging
Python Debugging
A synthetic instruction-tuning dataset for training AI models to identify and fix bugs in Python code.
Dataset Summary
Field
Value
Entries
75
Format
input / output pairs
Language
English
Topic
Finding and fixing bugs in Python code
Synthetic
Yes, generated with DeepSeek
License
MIT
Dataset Description
Each entry presents a snippet of Python code containing a deliberate bug, along with a corrected version… See the full description on the dataset page: https://huggingface.co/datasets/creeperdatasets/python_debugging.Opus-4.6-RU-Reasoning-creative-1385x-not-filtered
Opus-4.6-RU-Creative-Writing — Russian Creative Writing Reasoning Dataset
A Russian-language dataset of creative writing tasks generated with Claude claude-opus-4.6 (extended thinking enabled). Each sample contains a creative prompt, a full reasoning chain showing the creative process, and a detailed artistic response.
Dataset Info
Language: Russian 🇷🇺
Size: ~1,385 samples (growing)
Model used: anthropic/claude-opus-4.6 with reasoning: {effort: "high"}
Format:… See the full description on the dataset page: https://huggingface.co/datasets/DarkyMan/Opus-4.6-RU-Reasoning-creative-1385x-not-filtered.cresowlve
CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge
Dataset Description
This is a bilingual benchmark for creative problem-solving grounded in real-world knowledge and solvable by human experts.
CresOWLve spans a diverse range of knowledge and creative domains, varies in difficulty, requires multiple creative thinking strategies, and is manually validated to ensure quality.
It contains ~2K open-ended questions with answers and explanations.… See the full description on the dataset page: https://huggingface.co/datasets/mismayil/cresowlve.sql-create-context-pt
Overview
Este dataset é uma versão traduzida para o português do dataset b-mc2/sql-create-context,
que foi construído a partir dos datasets WikiSQL e Spider. Ele contém exemplos de perguntas
em português, instruções SQL CREATE TABLE e consultas SQL que respondem às perguntas
utilizando a instrução CREATE TABLE como contexto.
O principal objetivo deste dataset é ajudar modelos de linguagem natural em português a gerar consultas
SQL precisas e contextualizadas, prevenindo a… See the full description on the dataset page: https://huggingface.co/datasets/emdemor/sql-create-context-pt.sql-create-context-instruction
Overview
This dataset is built upon SQL Create Context, which in turn was constructed using data from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-SQL LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-SQL datasets. The CREATE TABLE statement can often be… See the full description on the dataset page: https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction.sql-create-context-copy
Fork of b-mc2/sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/sql-create-context-copy.sql-create-context-id
Overview
This dataset is a fork from sql-create-context
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/detakarang/sql-create-context-id.pandas-create-context
Overview
This dataset is built from sql-create-context, which in itself builds from WikiSQL and Spider.
I have used GPT4 to translate the SQL schema into pandas DataFrame schem initialization statements and to translate the SQL queries into pandas queries.
There are 862 examples of natural language queries, pandas DataFrame creation statements, and pandas query answering the question using the DataFrame creation statement as context. This dataset was built with text-to-pandas… See the full description on the dataset page: https://huggingface.co/datasets/hiltch/pandas-create-context.maplestory-worlds-creator-qa
MapleStory Worlds Creator QA
Synthetic question-answer dataset built from the official
MapleStory Worlds Creator Center
documentation. Questions are generated to be self-contained and grounded in the
source docs; answers avoid source/meta references so they read like an expert
explanation. Some QA pairs are composed from multiple related documents
(see combo_sources).
Parallel Korean/English. Intended for instruction tuning, QA, and retrieval.
Composition… See the full description on the dataset page: https://huggingface.co/datasets/msw-ai-tf/maplestory-worlds-creator-qa.Qwill-RP-CreativeWriting-Reasoning
Qwill RP CreativeWriting Reasoning Dataset
📝 Dataset Summary
Qwill-RP-CreativeWriting-Reasoning is a creative writing dataset focused on structured reasoning. Each row contains a fictional or narrative prompt sourced from nothingiisreal/Reddit-Dirty-And-WritingPrompts, along with an AI-generated response that includes:
Reasoning, wrapped in <think>...</think>
Final Answer, wrapped in <answer>...</answer>
The goal is to train or evaluate models on chain-of-thought… See the full description on the dataset page: https://huggingface.co/datasets/marcuscedricridia/Qwill-RP-CreativeWriting-Reasoning.CREATE-Protocol
CREATE Protocol: Cognitive Recursion Enhancement for Applied Transform Evolution
Dataset Description
CREATE (Cognitive Recursion Enhancement for Applied Transform Evolution) is a structured cognitive scaffolding framework designed to support epistemic integrity, curiosity-driven inquiry, and aligned reasoning in both human and artificial cognitive systems.
The protocol consists of modular text packets that provide frameworks for navigating uncertainty, recognizing… See the full description on the dataset page: https://huggingface.co/datasets/MaltbyTom/CREATE-Protocol.Code-170k-mauritian-creole
Dataset Description
Code-170k-mauritian-creole is a groundbreaking dataset containing 176,999 programming conversations, originally sourced from glaiveai/glaive-code-assistant-v2 and translated into Mauritian Creole, making coding education accessible to Mauritian Creole speakers.
🌟 Key Features
176,999 high-quality conversations about programming and coding
Pure Mauritian Creole language - democratizing coding education
Multi-turn dialogues covering various programming… See the full description on the dataset page: https://huggingface.co/datasets/michsethowusu/Code-170k-mauritian-creole.French_Grammar_Explanations
This dataset contains 1500+ French grammar explanations. It's the one I used to train my finetuned LLM called FrenchLlama-3.2-1B-Instruct.
You can use this dataset for your own training purposes & find the aforementioned model on my HuggingFace profile.
Tri-NL
Tri-NL: A Multi-Domain Reasoning Dataset
A synthetic reasoning dataset spanning Mathematics, Coding, and Healthcare — designed for training and evaluating LLMs on step-by-step reasoning across domains.
Key Features
1,500 samples — 500 per domain (math, coding, healthcare)
Step-by-step reasoning — every answer follows a numbered Step 1: → Final Answer: format
Cross-domain overlap — ~15% of each domain intentionally overlaps with each other domain (e.g., biostatistics… See the full description on the dataset page: https://huggingface.co/datasets/crevious/Tri-NL.sql-create-context-thai
Overview
This dataset builds from sql-create-context.
@misc{b-mc2_2023_sql-create-context,
title = {sql-create-context Dataset},
author = {b-mc2},
year = {2023},
url = {https://huggingface.co/datasets/b-mc2/sql-create-context},
note = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.},
}
CreditQA
CCA Numerical Reasoning
CCA Numerical Reasoning is an 800-question dataset of credit-card-agreement questions designed to evaluate numerical reasoning over consumer finance terms. Each example contains a natural-language question, a ground-truth answer, output units, input-unit metadata, executable-style reasoning steps, a program trace, question tense metadata, financial-term tags, and the source credit card agreement identifier.
The dataset contains 800 numerical reasoning… See the full description on the dataset page: https://huggingface.co/datasets/gtfintechlab/CreditQA.indian-credit-card-facts
Indian Credit Card Facts — an open dataset
A dated, machine-readable record of the Indian credit-card market: what each card charges, what
it earns, how its terms have changed over time, where its points can be transferred, and what
those points are worth.
Four tables, JSON and CSV, CC BY 4.0. Every release is immutable and separately citable.
Table
Rows
Unit
As of
cards
280
cards
2026-08-15
changes
1617
events
2026-08-13
transfers
153
edges
2026-07-02… See the full description on the dataset page: https://huggingface.co/datasets/DevchandraSah/indian-credit-card-facts.Code-170k-seychellois-creole
Dataset Description
Code-170k-seychellois-creole is a groundbreaking dataset containing 176,999 programming conversations, originally sourced from glaiveai/glaive-code-assistant-v2 and translated into Seychellois Creole, making coding education accessible to Seychellois Creole speakers.
🌟 Key Features
176,999 high-quality conversations about programming and coding
Pure Seychellois Creole language - democratizing coding education
Multi-turn dialogues covering various… See the full description on the dataset page: https://huggingface.co/datasets/michsethowusu/Code-170k-seychellois-creole.sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column… See the full description on the dataset page: https://huggingface.co/datasets/dipanjanS/sql-create-context.create_qa_news
질문 생성: kullm3 모델 이용
답변 생성: GPT3.5 turbo API 이용
지문 원본: AI HUB 뉴스 기계독해 데이터셋
qemu_networking
Qemu Networking from Claude Haiku 4.5
A synthetic instruction-tuning dataset covering QEMU networking concepts, generated using Claude Haiku 4.5.
Dataset Summary
Total rows: 75
Topic: QEMU virtual networking
Difficulty distribution: Easy, Intermediate, Advanced
278 unique tags across networking subtopics
Splits
train: 75 rows
Columns
id: Stable entry ID
instruction: Instruction text for fine-tuning
input: Original prompt/question
output:… See the full description on the dataset page: https://huggingface.co/datasets/creeperdatasets/qemu_networking.CreativityBench-MM
MM-CreativityBench
MM-CreativityBench evaluates creative tool-repurposing in multimodal models
through visual, part-level grounding.
Splits
train: 868 examples
test: 333 examples
The loadable split files are:
data/train.jsonl
data/test.jsonl
Nested fields from the source JSON files are serialized as JSON strings in
columns such as setting_json, golds_json, entities_json, items_json,
and solution_json. The original unmodified files are also included under
raw/.… See the full description on the dataset page: https://huggingface.co/datasets/chengq9/CreativityBench-MM.silvia-creditcard-bench
Silvia Credit Card Bench (v1.0)
Silvia Credit Card Bench is a benchmark of 100 personalized credit-card advisory
scenarios. Each scenario pairs a realistic, anonymized user financial profile
(including their existing credit-card portfolio) with a question about card
selection, rewards optimization, and spend routing. Every scenario carries
per-configuration judge scores for answer accuracy and grounding.
Schema
One JSONL file (data/train.jsonl), config default… See the full description on the dataset page: https://huggingface.co/datasets/cfosilvia/silvia-creditcard-bench.Gemma3_4b-Created-Chats
This dataset is AI generated, the creator might have missed errors
This dataset contains 5110 lines of jsonl. It follows this format:
{"messages": [{"role" : "User", "content" : "..."}, {"role" : "Assistant", "content" : "..."}]
The dataset contains a variety of topics and a random number of turns per chat, with 14 turns as the maximum.
It was generated with Gemma3:4b using ollama with a temperature of 0.8 on a RTX 3050 8GB.in order to download the dataset, use the following code:… See the full description on the dataset page: https://huggingface.co/datasets/person65/Gemma3_4b-Created-Chats.Creative_LLM