task_categories:text2text-generation annotations_creators:crowdsourced language_creators:found language_creators:machine-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown text-to-sql arxiv:1709.00103 region:us
A large crowd-sourced dataset for developing natural language interfaces for relational databases
@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}
}
---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
- machine-generated
license:
- unknown
multilinguality:
- monolingual
pretty_name: WikiSQL
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: wikisql
tags:
- text-to-sql
dataset_info:
  features:
  - name: phase
    dtype: int32
  - name: question
    dtype: string
  - name: table
    struct:
    - name: header
      sequence: string
    - name: page_title
      dtype: string
    - name: page_id
      dtype: string
    - name: types
      sequence: string
    - name: id
      dtype: string
    - name: section_title
      dtype: string
    - name: caption
      dtype: string
    - name: rows
      sequence:
        sequence: string
    - name: name
      dtype: string
  - name: sql
    struct:
    - name: human_readable
      dtype: string
    - name: sel
      dtype: int32
    - name: agg
      dtype: int32
    - name: conds
      sequence:
      - name: column_index
        dtype: int32
      - name: operator_index
        dtype: int32
      - name: condition
        dtype: string
  splits:
  - name: test
    num_bytes: 32234761
    num_examples: 15878
  - name: validation
    num_bytes: 15159314
    num_examples: 8421
  - name: train
    num_bytes: 107345917
    num_examples: 56355
  download_size: 26164664
  dataset_size: 154739992
---

# Dataset Card for "wikisql"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Repository:** https://github.com/salesforce/WikiSQL
- **Paper:** [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://arxiv.org/abs/1709.00103)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 26.16 MB
- **Size of the generated dataset:** 154.74 MB
- **Total amount of disk used:** 180.90 MB

### 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

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 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`: a `int32` feature.
- `question`: a `string` feature.
- `header`: a `list` of `string` features.
- `page_title`: a `string` feature.
- `page_id`: a `string` feature.
- `types`: a `list` of `string` features.
- `id`: a `string` feature.
- `section_title`: a `string` feature.
- `caption`: a `string` feature.
- `rows`: a dictionary feature containing:
  - `feature`: a `string` feature.
- `name`: a `string` feature.
- `human_readable`: a `string` feature.
- `sel`: a `int32` feature.
- `agg`: a `int32` feature.
- `conds`: a dictionary feature containing:
  - `column_index`: a `int32` feature.
  - `operator_index`: a `int32` feature.
  - `condition`: a `string` feature.

### Data Splits

| name  |train|validation|test |
|-------|----:|---------:|----:|
|default|56355|      8421|15878|

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 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](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf) for adding this dataset.