Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
text-to-sql
License:
HusnaManakkot
commited on
Commit
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Parent(s):
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Update README.md
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README.md
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tags:
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- text-to-sql
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pretty_name:
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size_categories:
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- 1K<n<10K
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---
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- en
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tags:
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pretty_name: new spider
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size_categories:
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- 1K<n<10K
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---
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# Dataset Card for Spider
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://yale-lily.github.io/spider
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- **Repository:** https://github.com/taoyds/spider
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- **Paper:** https://www.aclweb.org/anthology/D18-1425/
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- **Point of Contact:** [Yale LILY](https://yale-lily.github.io/)
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### Dataset Summary
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Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students
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The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases
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### Supported Tasks and Leaderboards
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The leaderboard can be seen at https://yale-lily.github.io/spider
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### Languages
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The text in the dataset is in English.
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## Dataset Structure
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### Data Instances
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**What do the instances that comprise the dataset represent?**
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Each instance is natural language question and the equivalent SQL query
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**How many instances are there in total?**
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**What data does each instance consist of?**
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[More Information Needed]
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### Data Fields
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* **db_id**: Database name
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* **question**: Natural language to interpret into SQL
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* **query**: Target SQL query
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* **query_toks**: List of tokens for the query
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* **query_toks_no_value**: List of tokens for the query
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* **question_toks**: List of tokens for the question
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### Data Splits
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**train**: 7000 questions and SQL query pairs
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**dev**: 1034 question and SQL query pairs
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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The dataset was annotated by 11 college students at Yale University
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#### Annotation process
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#### Who are the annotators?
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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## Additional Information
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The listed authors in the homepage are maintaining/supporting the dataset.
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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The spider dataset is licensed under
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the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
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[More Information Needed]
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### Citation Information
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```
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@article{yu2018spider,
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title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
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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},
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journal={arXiv preprint arXiv:1809.08887},
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year={2018}
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}
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```
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### Contributions
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Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
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