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-critic-1.0-sqlite
📢 Update 2026-03-23
We release BIRD-Critic-SQLite, a dataset containing 500 high-quality user issues focused on real-world SQLite database applications. Along with the dataset, we also release three RL-trained models: BIRD-Talon-14B, BIRD-Talon-7B, and BIRD-Zeno-7B. The schema file is included in the code repository https://github.com/bird-bench/BIRD-CRITIC-1/blob/main/baseline/data/sqlite_schema.jsonl
BIRD-CRITIC-1.0-SQLite
BIRD-Critic is the first SQL debugging… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird-critic-1.0-sqlite.BIRD-SQL-data-train
Dataset Card for "BIRD-SQL-data-train"
Data from BIRD-SQL benchmark training set.
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.SQL-Queries-DatasetSQL-Queries-Datasetlivesqlbench-base-lite-sqlite
🚀 LiveSQLBench-Base-Lite
A dynamic, contamination‑free benchmark for evaluating LLMs on complex, real‑world text‑to‑SQL tasks.
🌐 LiveSQLBench Website • 🌐 BIRD-INTERACT Project Page • 📄 Paper • 💻 LiveSQLBench GitHub • 💻 BIRD-INTERACT GitHub
Maintained by the 🦜 BIRD Team @ HKU & ☁️ Google Cloud
📊 LiveSQLBench Overview
LiveSQLBench (BIRD-SQL Pro v0.5) is a contamination-free, continuously evolving benchmark designed to evaluate LLMs on complex, real-world… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/livesqlbench-base-lite-sqlite.SQaLe-text-to-SQL-dataset
🧮 SQALE: A Large-Scale Semi-Synthetic Dataset
SQALE is a large-scale, semi-synthetic Text-to-SQL dataset grounded in real-world database schemas.
It was designed to push the boundaries of natural language to SQL generation, combining realistic schema diversity, complex query structures, and linguistically varied natural language questions.
The dataset was introduced in the paper SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas. The code for the generation pipeline of this… See the full description on the dataset page: https://huggingface.co/datasets/trl-lab/SQaLe-text-to-SQL-dataset.100000_text_to_sqlgretel-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.
sql-multiturn-training-dataset-combinedbird-sql-train-evalbird_text_to_sql
Dataset Card for "bird_text_to_sql"
More Information needed
spider-text-to-sql
Spider Text-to-SQL with LLM-Judge Labels
This dataset extends Spider 1.0 with SQL predictions from gpt-5.4-mini and two correctness labels per example: a hybrid ground truth label and an LLM judge label from gpt-5.4.
Files
File
Description
spider_dataset.parquet
Full dataset with predictions and labels
scripts/
Reproduction scripts (see below)
Dataset statistics
Source: Spider 1.0 training split (train_spider.json)
Databases: the… See the full description on the dataset page: https://huggingface.co/datasets/Glide-py/spider-text-to-sql.Text-to-sql-v1earthquakes
California Earthquakes 1967-2018
Location, magnitude and type of 2.5+ magnitude earthquakes in California from 1967 to 2018.
Source: https://kepler.gl/
six-gym-sqlite
📢 Update 2026-03-23
We release BIRD-Critic-SQLite, a dataset containing 500 high-quality user issues focused on real-world SQLite database applications. This dataset is the train split of BIRD-Critic-SQLite, comprising 5,000 data instances for model training and development. Along with the dataset, we also release three RL-trained models: BIRD-Talon-14B, BIRD-Talon-7B, and BIRD-Zeno-7B.
📋 Dataset Structure
Below is a description of the dataset fields and additional… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/six-gym-sqlite.spider_text_to_sql
Dataset Card for "spider_text_to_sql"
More Information needed
bird_spider_train_text_to_sql
Dataset Card for "bird_spider_train_text_to_sql"
More Information needed
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.b2_code_fasttext_pos_ioi_neg_sql_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_ioi_neg_sql_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
16.7
57.8
76.2
27.8
39.4
43.4
41.9
14.9
17.7
AIME24
Average Accuracy: 16.67% ± 1.33%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
10.00%
3
30
2
13.33%
4
30
3
16.67%
5
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_ioi_neg_sql_eval_636d.BIRD-SQL-TRAINBIRD-SQL-data
Dataset Card for "BIRD-SQL-data"
More Information needed
task076_splash_correcting_sql_mistake
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task076_splash_correcting_sql_mistake
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/task076_splash_correcting_sql_mistake.bird-sql-mini-devSQL-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.Persian-Business-Text-to-SQL-Gold-1K
Persian Business Text-to-SQL Gold-1K
1,000 Persian-native, execution-verified business Text-to-SQL examples for fine-tuning and benchmarking.
مجموعهای ۱۰۰۰ نمونهای برای تبدیل درخواستهای فارسی کسبوکار به SQL، همراه با دیتابیسهای SQLite اجرایی، schema کامل، متادیتای سختی/مهارت و ارزیابی مبتنی بر Execution Accuracy.
Motivation
BIRD emphasizes database-grounded Text-to-SQL and execution accuracy; Spider 2.0 pushes toward realistic enterprise database workflows.… See the full description on the dataset page: https://huggingface.co/datasets/jumplander/Persian-Business-Text-to-SQL-Gold-1K.BIRD-SQL-data-train-CoTPart of BIRD sql train dataset added with chain of thought distilled from DeepSeek-R1
mirror-sql
MIRROR-SQL
Provenance-Controlled Database Environments for Text-to-SQL Agents.
13 PostgreSQL environments · 176 tables · 2762 columns · 390 annotated question/SQL pairs.
MIRROR-SQL takes the opposite approach to contamination from every other text-to-SQL corpus.
Spider and BIRD sample public databases. BEAVER uses real private warehouses that cannot be
redistributed. LiveSQLBench out-runs leakage temporally by rebuilding from changing sources.
MIRROR-SQL instead purpose-builds… See the full description on the dataset page: https://huggingface.co/datasets/1digitaldesign/mirror-sql.