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.synthetic_text_to_sql
Image generated by DALL-E. See prompt for more details
synthetic_text_to_sql
gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples,
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details.
The dataset includes:
105,851 records partitioned into 100,000 train and 5,851 test records
~23M total tokens, including ~12M SQL tokens
Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql.bird_sql_dev_20251106
BIRD-SQL Dev
🆕 Update 2025-11-06
We would like to express our sincere gratitude to the community for their continuous support and constructive feedback on the BIRD-SQL Dev dataset. Over the past year, we have received valuable suggestions through GitHub discussions, emails, and user reports. Based on these insights, we organized a quality review program led by a team of five PhD researchers in Data Science and AI, supported by a globally distributed group of industry… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird_sql_dev_20251106.gretel-synthetic-text-to-sql
Fork of gretelai/synthetic_text_to_sql
The gretelai/synthetic_text_to_sql dataset is a large, Apache 2.0 licensed, synthetic Text-to-SQL dataset consisting of 105,851 high-quality records across 100 diverse domains, designed for training language models. It includes comprehensive SQL tasks with varying complexities, database contexts, natural language explanations, and contextual tags, outperforming existing datasets in SQL correctness and standards compliance.
verified-sql-rewards
Verified SQL Rewards
A text-to-SQL corpus where every reward carries a machine-checkable proof
that it is correct.
Questions, all independently verified
109,306
Databases
1,400 across 7 schema families
Tables / data rows
4,400 / ~19.6 million
Unique (question, answer) pairs
102,764
Candidates refused and published
12,150
Verification pass rate
90.00%
Trivial baseline (always answer 0)
1.83%
Each item is a natural-language question, a gold SQL query… See the full description on the dataset page: https://huggingface.co/datasets/rasinmuhammed/verified-sql-rewards.omnimcp_sql_bigquery_analytics_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_sql_bigquery_analytics_teaser.omnimcp_python_sqlalchemy_orm_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_python_sqlalchemy_orm_teaser.omnimcp_sql_postgres_pro_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_sql_postgres_pro_teaser.omnimcp_sql_snowflake_warehouse_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_sql_snowflake_warehouse_teaser.omnimcp_sql_dbt_transformations_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_sql_dbt_transformations_teaser.SQL-API-Bench
Dataset Card for Dataset Name
This dataset contains QA that requires DB and API access at the same time. It is composed of two new benchmarks consisting of questions whose answers require a
combination of database and API calls, both of
which are augmentations of the popular Spider
dataset and benchmark.
Benchmark I replaces a fraction of the real Spider database tables with
equivalents that are executed via APIs. This allows us to directly test the mechanism by which
database and… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/SQL-API-Bench.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-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-new-copy
Languages:
English
Data Splits
The following is taken from the corpus' source repsository:
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.LLM_SQL_BaseDatosEspanol
Usos
Usos directos
El objetivo principal de este dataset es proporcionar ejemplos simples para el fine-tuning de modelos
de procesamiento de lenguaje natural (NLP) en el contexto de consultas SQL.
Usos fuera de mira
Podria usarse para el entrenamiento de una IA que sirva como creadora de base de datos artificiales
Estructura del conjunto de datos
Question: Es la pegunta que el usuario le dara al chatbot
Answer: La respuesta el que chatbot le… See the full description on the dataset page: https://huggingface.co/datasets/somosnlp/LLM_SQL_BaseDatosEspanol.enterprise-sql-text-to-db-sft-dpo-2026
💎 Enterprise SQL, Text-to-DB & Schema-Aware Query Architect SFT/DPO Dataset (2026)
High-precision instruction tuning and preference optimization dataset with step-by-step Chain-of-Thought (<thought>) reasoning chains for fine-tuning LLMs (Llama-3.3, Qwen-2.5-Coder, DeepSeek-R1-Distill, Mistral, Phi-4) into Senior Enterprise Database Architects.
📊 Dataset Overview
Format: OpenAI ChatML & ShareGPT format with <thought> reasoning tokens.
Dialect Coverage (5… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/enterprise-sql-text-to-db-sft-dpo-2026.Stack2Graph_VD_sql
SQL StackOverflow Vector Dataset
Summary
This Hugging Face dataset repository contains the SQL shard of the Stack2Graph vector-database component as restorable Qdrant artifacts plus portable Parquet fallback files.
Hugging Face uses one dataset repository per programming language, so this repository is directly cloneable without an extra top-level archive wrapper.
The artifacts are intended for semantic and hybrid retrieval, graph entry-point finding, and… See the full description on the dataset page: https://huggingface.co/datasets/Mo7art/Stack2Graph_VD_sql.sql-parsedsql-qa-finetune-ds
Overview
This dataset is build from b-mc2/sql-create-context, Clinton/Text-to-sql-v1 and knowrohit07/know_sql.
A total of 148368 examples consist of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context.
Next Step
Further augment the data by converting queries and CREATE TABLE statements into different SQL dialects.
Support other informative contexts beyond CREATE TABLE.
synthetic_text_to_sql_th
Synthetic Text-to-SQL Thai Dataset
Thai translation of the gretelai/synthetic_text_to_sql dataset.
Dataset Description
This dataset contains Thai translations of synthetic text-to-SQL examples covering various domains and SQL patterns.
Source
Original Dataset: gretelai/synthetic_text_to_sql
Created by: Gretel.ai
Statistics
Split
Rows
Train
100,000
Test
5,851
Total
105,851
Columns
Column
Description… See the full description on the dataset page: https://huggingface.co/datasets/Porameht/synthetic_text_to_sql_th.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}.},
}
reasoning-sft-synthetic_text_to_sql-128K
synthetic_text_to_sql (converted)
Converted version of gretelai/synthetic_text_to_sql, reformatted to 100,000 rows for reasoning SFT training.
Format
Each row has three columns:
input — list of dicts [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}] (system prompt contains the database schema, user prompt contains the natural language question)
response — response string with <think> reasoning block (SQL explanation) followed by the SQL query… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/reasoning-sft-synthetic_text_to_sql-128K.synthetic-text-to-sql-tr
Cognipeer/synthetic-text-to-sql-tr
Overview
Cognipeer/synthetic-text-to-sql-tr is a Turkish translation of the gretelai/synthetic_text_to_sql dataset. gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated using Gretel Navigator, and released under Apache 2.0.
Dataset Features: The dataset includes 12 main feature:
id (int32)
domain (string)
domain_description (string)
sql_complexity (string)… See the full description on the dataset page: https://huggingface.co/datasets/Cognipeer/synthetic-text-to-sql-tr.synthetic_text_to_sql_d14
This dataset is subset of "gretelai/synthetic_text_to_sql"
Data Cleaning
The original dataset get filtered based on the following criteria:
(sql_context + sql) Validate against the SQLite database.
Tables must have some records.
We must get some output after running the "sql" query.
Subset Creation
I picked 3 sql complexity and 14 domains to create a subset.
SQL_COMPLEXITY = ['basic SQL', 'aggregation','single join']
DOMAINS = [
"technology", "sports", "logistics"… See the full description on the dataset page: https://huggingface.co/datasets/djagatiya/synthetic_text_to_sql_d14.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.synthetic_text_to_sql_en_es
Dataset basado en la versión de GretelAI - SyntheticSQL
synthetic_text_to_sql_en_es
Se trata de una expansión mediante la traducción al español de la columna 'sql_prompt'.
Se ha añadido una columna extra 'sql_prompt_es' que contiene el prompt original de inglés traducido al español.
Para obtener estas traducciones, se utilizó few-shot prompting + CoT mediante el modelo Qwen/Qwen2.5-32B-Instruct-AWQ
Actualización 6/27/25
En la versión pasada se encontraron… See the full description on the dataset page: https://huggingface.co/datasets/TafcoMetawireless/synthetic_text_to_sql_en_es.o3_sql_agent_datasetSynthetic Dataset generated with o3 model.
Source Input: Chinook.db file, the popular example used in many LangChain tutorials
import sqlite3
import requests
from langchain_community.utilities.sql_database import SQLDatabase
from sqlalchemy import create_engine
from sqlalchemy.pool import StaticPool
def get_engine_for_chinook_db():
"""Pull sql file, populate in-memory database, and create engine."""
url =… See the full description on the dataset page: https://huggingface.co/datasets/asvs/o3_sql_agent_dataset.database-sql-instruction-dataset
Database & SQL Instruction Dataset
High-quality instruction-response pairs covering PostgreSQL, advanced queries, indexing strategies, and database optimization.
Dataset Details
Dataset Description
This is a high-quality instruction-tuning dataset focused on Database topics. Each entry includes:
A clear instruction/question
Optional input context
A detailed response/solution
Chain-of-thought reasoning process
Curated by: CloudKernel.IO
Language(s):… See the full description on the dataset page: https://huggingface.co/datasets/bernabepuente/database-sql-instruction-dataset.