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richardr1126/spider-context-instruct

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Hugging Face2023-07-18 更新2024-03-04 收录
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资源简介:
--- language: - en license: - cc-by-4.0 source_datasets: - spider pretty_name: Spider Context Instruct tags: - text-to-sql - SQL - Spider - fine-tune dataset_info: features: - name: db_id dtype: string - name: text dtype: string --- # Dataset Card for Spider Context Instruct ### 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 finetune LLMs in a `### Instruction:` and `### Response:` format with database context. ### 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} } ```
提供机构:
richardr1126
原始信息汇总

数据集概述

数据集名称

Spider Context Instruct

数据集简介

Spider是一个大规模、复杂且跨领域的语义解析和文本到SQL的数据集,由11名耶鲁大学学生标注。该数据集旨在开发用于跨领域数据库的自然语言接口。

数据集用途

用于微调LLMs(大型语言模型),采用### Instruction:### Response:格式,并包含数据库上下文。

数据集特征

  • db_id: 数据类型为字符串
  • text: 数据类型为字符串

语言

数据集中的文本语言为英语。

许可证

数据集根据CC BY-SA 4.0许可发布。

引用信息

@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} }

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