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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

More Information Needed

Languages

More Information Needed

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

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

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Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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.

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Models trained or fine-tuned on Salesforce/wikisql