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--- |
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license: bigscience-openrail-m |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-Coder-3B-Instruct |
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pipeline_tag: translation |
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--- |
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### 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. |
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Under **Vote@8**, our model attains an **execution accuracy (EX) of 61.05 %**. |
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When the voting size increases to **Vote@32**, the EX further improves to **62.58 %**. |
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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. |
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Overall, **DatA-SQL** achieves competitive or superior accuracy compared with GPT-based pipelines at only a fraction of their computational expense. |
