| --- |
| license: bigscience-openrail-m |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen2.5-Coder-3B-Instruct |
| pipeline_tag: translation |
| --- |
| |
| ### Performance on the BIRD Development Set |
|
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| We further evaluate **DatA-SQL-3B** on the **BIRD** development set using different self-consistency voting sizes. |
| Under **Vote@8**, our model attains an **execution accuracy (EX) of 61.05 %**. |
| When the voting size increases to **Vote@32**, the EX further improves to **62.58 %**. |
| These results confirm that larger voting ensembles enhance semantic robustness and execution stability while maintaining nearly the same inference cost due to our lightweight multi-agent design. |
| Overall, **DatA-SQL** achieves competitive or superior accuracy compared with GPT-based pipelines at only a fraction of their computational expense. |
