Create README.md

README.md

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+---
+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)