datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Text-to-sql-v1Persian-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.text-to-sql-shop
Text-to-SQL on a seeded store schema, with checkpoints
Recipe: recipes/04-train/text-to-sql · Collection: Analyst
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
|… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/text-to-sql-shop.enterprise-text-to-sql-benchmark
Enterprise Text-to-SQL Benchmark
3,045 natural-language questions paired with executable PostgreSQL, over a
12-table enterprise schema (sales, catalogue, logistics, HR).
Built to answer one question honestly: does fine-tuning actually improve
text-to-SQL? On this benchmark, a QLoRA fine-tune of Qwen3-8B took strict
execution accuracy from 10.82 % to 50.99 % — and the benchmark is designed
so that number cannot be inflated by leakage or by string-matching.
split
rows… See the full description on the dataset page: https://huggingface.co/datasets/hari-krishna-ai/enterprise-text-to-sql-benchmark.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.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-v1text-to-sql-dataset
English Text‑to‑SQL with Optional Schema
This dataset maps English user questions to SQL queries, with or without a compact database schema provided. The schema, when present, is a minimal representation of the database structure: a comma‑separated list of table names followed by their column names in parentheses. No data types are included.
Dataset Structure
Column
Type
Description
text
string
The user's request in English.
schema
string (optional)… See the full description on the dataset page: https://huggingface.co/datasets/sirunchained/text-to-sql-dataset.pt-br-agentic-text-to-sql-distilled-trajectories
PT-BR Agentic Text-to-SQL Distilled Trajectories
This dataset contains message-only distilled trajectories for training tool-using Text-to-SQL agents in Brazilian Portuguese. The trajectories were selected from LLM-judged correct conversations and preserve the agent protocol used in the released code.
Code and reproducibility repository:
https://github.com/Boakpe/distilled-slms-for-text-to-sql-pt-br
Related collection:… See the full description on the dataset page: https://huggingface.co/datasets/Boakpe/pt-br-agentic-text-to-sql-distilled-trajectories.dataframer_text_to_sqlsynthetic-text-to-sql-tangle
Synthetic Text-to-SQL — Tangle SFT demo subset
A small, fixed subset of gretelai/synthetic_text_to_sql
(Apache-2.0), prepared for an end-to-end supervised fine-tuning showcase running as a
Tangle pipeline.
train.jsonl — first 5,000 rows of the source train split.
eval.jsonl — first 500 rows of the source test split.
train.tiny.jsonl / eval.tiny.jsonl — 16 / 8 rows, for a fast CPU wiring dry-run.
Each row keeps five fields from the source: id, domain, sql_prompt (the… See the full description on the dataset page: https://huggingface.co/datasets/ml-infra-toloka/synthetic-text-to-sql-tangle.text-to-sql-struct-distillation-minidev
结构化 Text-to-SQL 蒸馏 Mini-Dev 派生 SFT
本仓库发布由 BIRD Mini-Dev 500 个样本构造的派生 SFT messages 数据，共 500 条。
文件与边界
minidev_sft_messages.jsonl：500 条 messages 格式的派生 SFT 样本。
不含 Mini-Dev SQLite 数据库、gold SQL、原始 schema 文件或上游数据库内容。
评测中使用 BIRD 作者提供的 SQLite 集合型 Execution Accuracy (EX) 定义；该评测代码与复现说明在 GitHub 工程中维护。
来源、署名与许可证
本数据为 BIRD Mini-Dev 的派生文本内容。上游仓库：https://github.com/bird-bench/mini_dev。上游 README 标明 CC BY-SA 4.0，因此本仓库按 CC BY-SA 4.0 发布。使用或再发布时请保留对 BIRD 与 Mini-Dev… See the full description on the dataset page: https://huggingface.co/datasets/craboy4/text-to-sql-struct-distillation-minidev.text-to-sql-nbaquery_text_to_sqltext_to_sql
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Text-to-SQL Dataset created with Dataframer
Generated programmatically using Dataframer. Read the full blog post →
Haiku… See the full description on the dataset page: https://huggingface.co/datasets/dataframer/text_to_sql.jsonL_text_to_sqlText-to-SQL-SoftlineTextToSQLforGaussalgodb_course-synthetic_text_to_sql_datasetMedical-Text-to-SQLText-to-SQLaihub_text_to_sqlAI HUB의 데이터를 처리한 데이터셋입니다. pyarrow에서 와일드카드 오류로 '*'이 사용불가 하여 제거하였으며 이에 따라 type에서도 0번째를 제거하여 맞췄습니다.
foreign_key는 처리하지 않았으니 사용할 경우 n-1로 처리하여 사용하시면 됩니다.
https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=71351
Text_to_SQLTextToSQLtext-to-sql_Llama-3.1-8Btest_texttosql_datasettext_to_sql