| | --- |
| | task_categories: |
| | - question-answering |
| | - table-question-answering |
| | - text-generation |
| | - text2text-generation |
| | language: |
| | - en |
| | tags: |
| | - text2sql |
| | - text-to-sql |
| | - database |
| | - llm |
| | - llama |
| | pretty_name: birdbench |
| | size_categories: |
| | - 100M<n<1B |
| | --- |
| | ## BirdBench Dataset in DuckDB format |
| |
|
| | BirdBench is a benchmark for text-to-SQL capabilities, now available in DuckDB format for improved performance and usability. |
| |
|
| | ## About BirdBench |
| |
|
| | BirdBench is a comprehensive benchmark dataset for evaluating text-to-SQL capabilities of language models. It features a diverse collection of databases spanning various domains including: |
| |
|
| | - Business and finance |
| | - Entertainment and media |
| | - Sports and recreation |
| | - Health and medicine |
| | - Education |
| | - Travel and geography |
| | - And many more |
| |
|
| | ## Why DuckDB? |
| |
|
| | This repository contains the BirdBench dataset converted from SQLite to DuckDB format, which offers several advantages: |
| |
|
| | - **Performance**: DuckDB is significantly faster for analytical queries |
| | - **Integration**: Better integration with Python data science tools |
| | - **Features**: Support for vectorized operations and advanced analytical functions |
| | - **Compatibility**: Works well in environments where SQLite might have limitations |
| |
|
| | ## Dataset Structure |
| |
|
| | The dataset maintains the original BirdBench structure, with both training and validation databases converted to DuckDB format: |
| |
|
| | - `/train` - Contains training databases |
| | - `/validation` - Contains validation databases |
| |
|
| | Each database preserves the original schema and data from the SQLite version. |
| |
|
| | ## Usage |
| |
|
| | ### Loading a database |
| |
|
| | ```python |
| | import duckdb |
| | |
| | # Connect to a database |
| | conn = duckdb.connect('path/to/database.duckdb') |
| | |
| | # List tables |
| | tables = conn.execute('SELECT name FROM sqlite_master WHERE type="table"').fetchall() |
| | print(tables) |
| | |
| | # Run a query |
| | result = conn.execute('SELECT * FROM your_table LIMIT 5').fetchall() |
| | print(result) |
