README.md

---
language:
- en
license:
- cc-by-4.0
source_datasets:
- spider
tags:
- sql
- spider
- natsql
- text-to-sql
- sql finetune
dataset_info:
  features:
    - name: db_id
      dtype: string
    - name: prompt
      dtype: string
    - name: ground_truth
      dtype: string
---
# Dataset Card for Spider NatSQL Context Validation

### Dataset Summary

[Spider](https://arxiv.org/abs/1809.08887) is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students
The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases.

This dataset was created to validate LLMs on the Spider dev dataset with database context using NatSQL.

### NatSQL

[NatSQL](https://arxiv.org/abs/2109.05153) is an intermediate representation for SQL that simplifies the queries and reduces the mismatch between 
natural language and SQL. NatSQL preserves the core functionalities of SQL, but removes some clauses and keywords
that are hard to infer from natural language descriptions. NatSQL also makes schema linking easier by reducing the 
number of schema items to predict. NatSQL can be easily converted to executable SQL queries and can improve the 
performance of text-to-SQL models.

### Yale Lily Spider Leaderboards

The leaderboard can be seen at https://yale-lily.github.io/spider

### Languages

The text in the dataset is in English.

### Licensing Information

The spider dataset is licensed under 
the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)

### Citation
```
@article{yu2018spider,
  title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
  author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others},
  journal={arXiv preprint arXiv:1809.08887},
  year={2018}
}
```
```
@inproceedings{gan-etal-2021-natural-sql,
    title = "Natural {SQL}: Making {SQL} Easier to Infer from Natural Language Specifications",
    author = "Gan, Yujian  and
      Chen, Xinyun  and
      Xie, Jinxia  and
      Purver, Matthew  and
      Woodward, John R.  and
      Drake, John  and
      Zhang, Qiaofu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.174",
    doi = "10.18653/v1/2021.findings-emnlp.174",
    pages = "2030--2042",
}
```