README.md

metadata

license: cc-by-sa-4.0
task_categories:
  - table-question-answering
  - question-answering
language:
  - en
size_categories:
  - 1K<n<10K

BIRD-SQL Mini-Dev

Update 2025-07-04

We are grateful for the valuable feedback from the community over the past year regarding BIRD Mini-Dev. Based on your suggestions, we have made significant updates to the BIRD Mini-Dev dataset.

For New Users

If you are new to BIRD Mini-Dev, you can download the complete databases and datasets using the following link: Download BIRD Mini-Dev Complete Package

For Existing Users

If you have already downloaded the BIRD databases, you can pull the latest data updates through Hugging Face using the following scripts:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("birdsql/bird_mini_dev")

# Access the SQLite version
print(dataset["mini_dev_sqlite"][0])

# Access the MySQL version
print(dataset["mini_dev_mysql"][0])

# Access the PostgreSQL version
print(dataset["mini_dev_pg"][0])

We appreciate the continuous support and feedback from the community.

Overview

Here, we provide a Lite version of developtment dataset: Mini-Dev. This mini-dev dataset is designed to facilitate efficient and cost-effective development cycles, especially for testing and refining SQL query generation models. This dataset results from community feedback, leading to the compilation of 500 high-quality text2sql pairs derived from 11 distinct databases in a development environment. To further enhance the practicality of the BIRD system in industry settings and support the development of text-to-SQL models, we make the Mini-Dev dataset available in both MySQL and PostgreSQL.

Additionally, we introduce two new evaluation metrics for the Mini-Dev dataset: the Reward-based Valid Efficiency Score (R-VES) and the Soft F1-Score. These metrics aim to evaluate the efficiency and accuracy of text-to-SQL models, respectively. It is important to note that the both metrics, currently in their beta version, applies exclusively to the Mini-Dev dataset using baseline models.

We welcome contributions and suggestions for enhancing these metrics, particularly regarding their integration into existing leaderboards. Please do not hesitate to contact us if you are interested in these developments or have any proposals for improvements.

Below are some key statistics of the mini-dev dataset:

Difficulty Distribution

• Simple: 30%
• Moderate: 50%
• Challenging: 20%

Database Distribution

• Debit Card Specializing: 30 instances
• Student Club: 48 instances
• Thrombosis Prediction: 50 instances
• European Football 2: 51 instances
• Formula 1: 66 instances
• Superhero: 52 instances
• Codebase Community: 49 instances
• Card Games: 52 instances
• Toxicology: 40 instances
• California Schools: 30 instances
• Financial: 32 instances

Keywords Statistic

• Main Body Keywords •SELECT •FROM •WHERE •AND •OR •NOT •IN •EXISTS •IS •NULL •IIF •CASE •CASE WHEN.
• Join Keywords • INNER JOIN • LEFT JOIN • ON • AS.
• Clause Keywords • BETWEEN • LIKE • LIMIT • ORDER BY • ASC • DESC • GROUP BY •HAVING •UNION •ALL •EXCEPT •PARTITION BY •OVER.
• Aggregation Keywords • AVG • COUNT • MAX • MIN • ROUND • SUM.
• Scalar Keywords • ABS • LENGTH • STRFTIME • JULIADAY • NOW • CAST • SUBSTR • INSTR.
• Comparison Keywords •= •> •< •>= •<= •!=.
• Computing Keywords •- •+ •* •/.

Dataset Introduction

The dataset contains the main following resources:

• database: The database should be stored under the ./mini_dev_data/dev_databases/. In each database folder, it has two components:
  • database_description: the csv files are manufactured to describe database schema and its values for models to explore or references.
  • sqlite: The database contents in BIRD.

    You have to download the latest dev databases in order to construct database in the MySQL and PostgreSQL. If you use the SQLite version only, you can use the original dev databases.

• data: Each text-to-SQL pairs with the oracle knowledge evidence is stored as a json file, i.e., mini_dev_sqlite.json is stored on ./mini_dev_data/mini_dev_sqlite.json. In each json file, it has three main parts:
  • db_id: the names of databases
  • question: the questions curated by human crowdsourcing according to database descriptions, database contents.
  • evidence: the external knowledge evidence annotated by experts for assistance of models or SQL annotators.
  • SQL: SQLs annotated by crowdsource referring to database descriptions, database contents, to answer the questions accurately.
• ground-truth SQL file: The SQL file should be stored at ./llm/mini_dev_data/mini_dev_sqlite_gold.sql.
• llm: It contains source codes to convert texts to SQLs by calling APIs from LLMs, such as GPT35-turbo-instruct, gpt-35-turbo, gpt-4, gpt-4-32k, and gpt-4-turbo.

Mini-Dev Dataset in MySQL and PostgreSQL

You can locate the SQL queries within the mini_dev_mysql.json and mini_dev_postgresql.json files. These queries have been transpiled from the original SQLite versions using the sqlglot package, then refined manually and with GPT-4 Turbo. After downloading the Mini-Dev dataset, each database folder will contain .sql and command.script files. Follow the instructions below to set up the database in MySQL and PostgreSQL:

MySQL

1. Download and install the MySQL from the official website: https://dev.mysql.com/downloads/mysql/
2. Set the environment variables:

export PATH=$PATH:/usr/local/mysql/bin

3. Start the MySQL server:

sudo /usr/local/mysql/support-files/mysql.server start

4. Login to the MySQL server and create the database (password will be the one you set during the installation)

mysql -u root -p
CREATE DATABASE BIRD;

5. Construct the database by run the following command (You can find MySQL version database: BIRD_dev.sql in the MINIDEV_mysql folder):

mysql -u root -p BIRD < BIRD_dev.sql

6. Examples that how to run mysql query in the Python (with pymysql) can be find in the examples/mysql_example.ipynb file.

7. If you encounter the error: "this is incompatible with sql_mode=only_full_group_by", you can run the following command to disable the sql_mode:

select @@global.sql_mode;
SET GLOBAL sql_mode='{EVERYTHING SHOW IN THE ABOVE COMMAND EXCEPT ONLY_FULL_GROUP_BY}';

PostgreSQL

1. Download and install the postgresql from the official website: https://www.postgresql.org/download/
2. Download the pgAdmin4 from the official website: https://www.pgadmin.org/download/ (Recommended to monitor the database)
3. In pgADmin4/terminal create a new database called BIRD
4. Construct the database by run the following command (You can find PostgreSQL version database:BIRD_dev.sql in the MINIDEV_postgresql folder):

psql -U USERNAME -d BIRD -f BIRD_dev.sql

5. Examples that how to run postgresql query in the Python (with Psycopg) can be find in the examples/postgresql_example.ipynb file.

Baseline performance on Mini-Dev Dataset

EX Evaluation

	SQLite	MySQL	PostgreSQL
mixtral-8x7b	21.60	13.60	12.40
llama3-8b-instruct	24.40	24.60	18.40
phi-3-medium-128k-instruct	30.60	25.00	21.60
gpt-35-turbo-instruct	33.60	31.20	26.60
gpt-35-turbo	38.00	36.00	27.40
llama3-70b-instruct	40.80	37.00	29.40
TA + gpt-35-turbo	41.60	-	-
TA + llama3-70b-instruct	42.80	-	-
gpt-4-turbo	45.80	41.00	36.00
gpt-4-32k	47.00	43.20	35.00
gpt-4	47.80	40.80	35.80
TA + gpt-4-turbo	58.00	-	-
TA + gpt-4o	63.00	-	-

R-VES Evaluation

	SQLite	MySQL	PostgreSQL
mixtral-8x7b	20.41	12.99	14.16
llama3-8b-instruct	23.27	23.66	17.90
phi-3-medium-128k-instruct	29.54	24.12	21.07
gpt-35-turbo-instruct	32.28	30.39	26.14
gpt-35-turbo	37.33	34.94	26.80
llama3-70b-instruct	39.02	35.82	28.80
TA + gpt-35-turbo	40.59	-	-
TA + llama3-70b-instruct	41.37	-	-
gpt-4-turbo	44.79	39.37	35.23
gpt-4-32k	45.29	42.79	34.59
gpt-4	45.91	39.92	35.24
TA + gpt-4-turbo	56.44	-	-
TA + gpt-4o	60.86	-	-

Soft F1-Score Evaluation

	SQLite	MySQL	PostgreSQL
mixtral-8x7b	22.95	13.79	14.70
llama3-8b-instruct	27.87	27.49	19.35
phi-3-medium-128k-instruct	35.33	28.73	24.11
gpt-35-turbo-instruct	36.34	33.85	28.30
gpt-35-turbo	41.84	40.75	30.22
TA + gpt-35-turbo	44.25	-	-
llama3-70b-instruct	44.38	40.95	31.43
TA + llama3-70b-instruct	46.66	-	-
gpt-4-turbo	50.08	45.96	38.36
gpt-4-32k	51.92	47.38	39.55
gpt-4	52.69	45.78	38.96
TA + gpt-4-turbo	62.40	-	-
TA + gpt-4o	66.97	-	-

Citation

Please cite the repo if you think our work is helpful to you.

@article{li2024can,
  title={Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls},
  author={Li, Jinyang and Hui, Binyuan and Qu, Ge and Yang, Jiaxi and Li, Binhua and Li, Bowen and Wang, Bailin and Qin, Bowen and Geng, Ruiying and Huo, Nan and others},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}