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Dataset Card for "wikisql"
Dataset Summary
A large crowd-sourced dataset for developing natural language interfaces for relational databases.
WikiSQL is a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 26.16 MB
- Size of the generated dataset: 154.74 MB
- Total amount of disk used: 180.90 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"phase": 1,
"question": "How would you answer a second test question?",
"sql": {
"agg": 0,
"conds": {
"column_index": [2],
"condition": ["Some Entity"],
"operator_index": [0]
},
"human_readable": "SELECT Header1 FROM table WHERE Another Header = Some Entity",
"sel": 0
},
"table": "{\"caption\": \"L\", \"header\": [\"Header1\", \"Header 2\", \"Another Header\"], \"id\": \"1-10015132-9\", \"name\": \"table_10015132_11\", \"page_i..."
}
Data Fields
The data fields are the same among all splits.
default
phase
: aint32
feature.question
: astring
feature.header
: alist
ofstring
features.page_title
: astring
feature.page_id
: astring
feature.types
: alist
ofstring
features.id
: astring
feature.section_title
: astring
feature.caption
: astring
feature.rows
: a dictionary feature containing:feature
: astring
feature.
name
: astring
feature.human_readable
: astring
feature.sel
: aint32
feature.agg
: aint32
feature.conds
: a dictionary feature containing:column_index
: aint32
feature.operator_index
: aint32
feature.condition
: astring
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 56355 | 8421 | 15878 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{zhongSeq2SQL2017,
author = {Victor Zhong and
Caiming Xiong and
Richard Socher},
title = {Seq2SQL: Generating Structured Queries from Natural Language using
Reinforcement Learning},
journal = {CoRR},
volume = {abs/1709.00103},
year = {2017}
}
Contributions
Thanks to @lewtun, @ghomasHudson, @thomwolf for adding this dataset.
- Downloads last month
- 1,264
Repository:
github.com
Paper:
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Size of downloaded dataset files:
26.16 MB
Models trained or fine-tuned on Salesforce/wikisql
Text2Text Generation
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