datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.earthquakes
California Earthquakes 1967-2018
Location, magnitude and type of 2.5+ magnitude earthquakes in California from 1967 to 2018.
Source: https://kepler.gl/
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.text-to-sql-shop
Text-to-SQL on a seeded store schema, with checkpoints
A question about an online store's database in, one PostgreSQL query out, graded by
a program: run the query, compare the result set to the gold query's result. The
schema (8 tables, seeded, schema.sql + seed.sql), the verifier, the trainer and
the benchmark runner are the
recipes/04-train/text-to-sql
recipe in the open-source whileai SDK.
Splits
config
rows
what
train
1764
tasks: question, gold_sql… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/text-to-sql-shop.av_sql_preprocessed_data
Dataset Card for Preprocessed Text-to-SQL Benchmarks
This repository contains preprocessed data for several text-to-SQL benchmarks, as presented in the paper AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views.
The official code for the AV-SQL framework can be found on GitHub: pminhtam/AV-SQL.
Dataset Summary
This repository contains preprocessed data for several text-to-SQL benchmarks:
BIRD
KaggleDBQA
Spider
sciencebenchmark
BEAVER
Spider2-Lite… See the full description on the dataset page: https://huggingface.co/datasets/griffith-bigdata/av_sql_preprocessed_data.ecommerce-analytics-sql-evaluation
Ecommerce Analytics SQL Evaluation (declared GMV, verified answer key)
An evalpack: an evaluation database generated from the answer key, not
annotated after the fact. A VLDB 2026 audit found 52.8% of BIRD Mini-Dev
answer keys wrong because benchmarks annotate answers onto existing
databases; this dataset inverts the order. The declared properties (curves,
shares, identities) are the specification, the database is generated to
satisfy them exactly, and every shipped question was… See the full description on the dataset page: https://huggingface.co/datasets/rasinmuhammed/ecommerce-analytics-sql-evaluation.saas-finance-sql-evaluation
SaaS Finance SQL Evaluation (MRR waterfalls that reconcile exactly)
An evalpack: an evaluation database generated from the answer key, not
annotated after the fact. A VLDB 2026 audit found 52.8% of BIRD Mini-Dev
answer keys wrong because benchmarks annotate answers onto existing
databases; this dataset inverts the order. The declared properties (curves,
shares, identities) are the specification, the database is generated to
satisfy them exactly, and every shipped question was… See the full description on the dataset page: https://huggingface.co/datasets/rasinmuhammed/saas-finance-sql-evaluation.enriched_sql_datasetedtech-sql-evaluation
EdTech SQL Evaluation (declared learning curve, verified answer key)
An evalpack: an evaluation database generated from the answer key, not
annotated after the fact. A VLDB 2026 audit found 52.8% of BIRD Mini-Dev
answer keys wrong because benchmarks annotate answers onto existing
databases; this dataset inverts the order. The declared properties (curves,
shares, identities) are the specification, the database is generated to
satisfy them exactly, and every shipped question was… See the full description on the dataset page: https://huggingface.co/datasets/rasinmuhammed/edtech-sql-evaluation.uk-electricity-generation-mix-2019-2026
UK Electricity Generation Mix & Carbon Intensity (2019–2026)
Half-hourly electricity generation by fuel type and carbon intensity for Great Britain, sourced from the NESO (National Energy System Operator) Data Portal. Update dataset can be found daily at - Update: 'https://github.com/rifkiamil/neso-uk-electricity-generation-mix-daily-update'
Key Facts
Time range
2019-01-01 00:00 → 2026-03-19 21:30 (UTC)
Granularity
30-minute intervals
Records
126,476… See the full description on the dataset page: https://huggingface.co/datasets/Rif-SQL/uk-electricity-generation-mix-2019-2026.stack_edu_sqlEffi-SQL
Effi-SQL
Update 2026-06-12
We release Effi-SQL, a dataset suite for SQL efficiency optimization.
This collection includes:
Effi-SQL Benchmark: a benchmark for evaluating SQL efficiency optimization methods.
Diff-SQL Training Dataset: training data used by Diff-SQL, including data for the Patch Generator and Constraint Aligner.
Dataset Fields
Effi-SQL Benchmark
id: A unique identifier for each benchmark instance.
db: The database… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/Effi-SQL.text-to-sql-eval-predictions
What the text-to-SQL models actually generated
Every prediction behind the numbers in
qwen3-8b-text2sql-qlora: the 453 test
questions of the enterprise text-to-SQL benchmark,
each answered by four configurations of the same model, each answer executed against the reference
PostgreSQL database and scored by comparing result sets. 1,812 rows.
I published this because the headline table (10.82 % → 50.99 % → 52.10 %) is the least interesting part of
that project. The interesting… See the full description on the dataset page: https://huggingface.co/datasets/hari-krishna-ai/text-to-sql-eval-predictions.text-to-sql-phrasing-robustness
Does sloppy phrasing break text-to-SQL?
The enterprise text-to-SQL benchmark
lists its own biggest caveat: every question is template-generated, so real user phrasing is untested.
This is the test. 35 test questions (one per template), each sent to the deployed
pipeline four ways: as written, with a typo, in business shorthand, and stripped to a terse fragment.
24 questions and 85 answers survive the filter described under Setup; every answer was
executed against the database.… See the full description on the dataset page: https://huggingface.co/datasets/hari-krishna-ai/text-to-sql-phrasing-robustness.Offres-Emplois-DB-Sql-LiteGelbooru-SQLiteThis is db dump(s) of booru-typed databases.
The codebase (https://github.com/aria1th/Booru-Unified-Sqlite) will be used for creating DB, to handle various types of DB + allowing multiple DBs being loaded in same program.
Danbooru DB, mainly, will be updated at https://huggingface.co/datasets/KBlueLeaf/danbooru2023-sqlite too.
the-stack-v2-dedup-sql-annotateThis dataset focuses the SQL corpus of the Stack v2 dedup dataset and provides annotations to each SQL file therein.
sqlparse is used to parse the SQL code, then count keywords and symbols.
Below are the annotation columns.
Column Name
Column Description
Keyword.DML
Data Manipulation Language commands for modifying database records, e.g. SELECT, INSERT, UPDATE, DELETE, COMMIT, MERGE, ROLLBACK.
Keyword.DDL
Data Definition Language commands for defining or altering database… See the full description on the dataset page: https://huggingface.co/datasets/onekq-ai/the-stack-v2-dedup-sql-annotate.SO-Python_QA-Database_and_SQL_class
Dataset Card for Dataset Name
Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/RazinAleks/SO-Python_QA-Database_and_SQL_class.sqlconsole-victimsqlautophagycode_D_test_Qwen3-8B_t1.25_g9_run0_metricsb2_code_fasttext_pos_all_neg_sql_10k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_all_neg_sql_10k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
19.7
60.8
73.8
25.2
41.3
39.1
36.1
12.2
14.1
AIME24
Average Accuracy: 19.67% ± 1.10%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
23.33%
7
30
2
16.67%
5
30
3
23.33%
7… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_all_neg_sql_10k_eval_636d.sqlautophagycode_D_test_Qwen3-8B_t1.25_g8_run0_metricsspider-text-to-sqltext_to_sql_ko
Korean Text to MySQL Dataset
Dataset Summary
Korean Text to MySQL is a dataset comprising approximately 3,300 samples generated using OpenAI's gpt-4o model. This dataset is designed to train models that convert natural language questions in Korean into MySQL queries. The data generation process was inspired by the Self-Instruct method and followed the steps outlined below.
Data Generation Process
1. Creation of SEED Dataset
Approximately 100 SEED samples were… See the full description on the dataset page: https://huggingface.co/datasets/won75/text_to_sql_ko.MycoBase-Large-Scale-Text-to-SQL
MycoBase: A Biologically Literate Text-to-SQL Dataset
MycoBase is a synthetic but biologically accurate dataset designed for stress-testing Text-to-SQL systems. It represents a research information system for the study of fungi, covering everything from taxonomy and genomics to morphology and cultivation.
Dataset Highlights
Schema Complexity: 2,016 tables with over 9,000 foreign key relationships.
Data Volume: 320,270 rows of realistic mycology data.
Realistic Names:… See the full description on the dataset page: https://huggingface.co/datasets/FatimahEmadEldin/MycoBase-Large-Scale-Text-to-SQL.sqlautophagycode_D_test_Qwen3-8B_t1.25_g3_run0_metricsa1_code_sql_create_context_1744691318_eval_1331
mlfoundations-dev/a1_code_sql_create_context_1744691318_eval_1331
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
GPQADiamond
JEEBench
MMLUPro
LiveCodeBench
CodeElo
Accuracy
9.7
40.0
52.0
26.9
20.4
28.4
11.7
4.3
AIME24
Average Accuracy: 9.67% ± 1.20%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
6.67%
2
30
2
16.67%
5
30
3
16.67%
5
30
4
6.67%
2
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/a1_code_sql_create_context_1744691318_eval_1331.sql-dataset-01sqlautophagycode_D_test_Qwen3-8B_t0.9_g6_run0_metrics