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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: translation
---
### Performance on the BIRD Development Set
We further evaluate **DatA-SQL-1.5B** on the **BIRD** development set using self-consistency voting.
Under **Vote@8**, our model achieves an **execution accuracy (EX) of 55.86 %**, establishing a new state of the art among open-source Text-to-SQL systems at the 1.5 B scale.
This result demonstrates that even compact models can acquire strong reasoning and SQL generation abilities when trained with our **reasoning-guided data augmentation**.
The lightweight design ensures low training and inference costs while maintaining high execution robustness.
Overall, **DatA-SQL-1.5B** achieves competitive accuracy compared with much larger GPT-based pipelines, highlighting the efficiency and practicality of our approach.
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