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 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 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
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",
}