README.md

metadata

license: cc-by-4.0
task_categories:
  - text-generation
  - question-answering
  - table-question-answering
language:
  - id
tags:
  - SQL
  - code
  - NLP
  - text-to-sql
  - context-sql
  - spider
  - wikisql
  - sqlglot
pretty_name: sql-create-context-id
size_categories:
  - 10K<n<100K

Overview

This dataset is a fork from sql-create-context
This dataset builds from WikiSQL and Spider.

There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column names and their data types. By providing just the CREATE TABLE statement as context, we can hopefully provide better grounding for models without having to provide actual rows of data, limiting token usage and exposure to private, sensitive, or proprietary data.

Cleansing and Augmentation

Cleansing and data augmentation has been done on the combined WikiSQL and Spider data. I used SQLGlot on queries from Spider and WikiSQL and parsed them into different tables and columns, I then inferred column data types based on usage of > < operators as well as the use of MIN() MAX() AVG() SUM() on columns. While this isn't perfect, it increases the likelihood of inferring the correct datatype for a column, the columns otherwise default to VARCHAR type. These tables and columns are then used to generate CREATE TABLE statements using the inferred types. SQLGlot is used again to ensure both the SQL queries and CREATE TABLE statements parse without errors.

Some queries that do not have column names, e.g. SELECT * FROM table, have a default Id column added to the CREATE TABLE statement. Some other queries which use the generic table as the FROM table have instead been changed to a variation of table_name_1 or some other number which is also reflected in the CREATE TABLE statement.

TODO

• Further augment the data by converting queries and CREATE TABLE statements into different SQL dialects, this can be done with SQLGlot. Reference to the dialect might also be added to the question.
• Support other informative contexts beyond CREATE TABLE
• Better parse datatypes to clean up things like numbers for column names and other numbers as strings

If you have any edits you'd like to see in a version 2 of this dataset, let me know.

Random sample:

  {
    "question": "Berapa banyak kepala departemen yang lebih tua dari 56 tahun?",
    "context": "CREATE TABLE head (age INTEGER)",
    "answer": "SELECT COUNT(*) FROM head WHERE age > 56"
  },
  {
    "question": "Sebutkan nama, lahir negara bagian mana dan usia kepala departemen yang dipesan berdasarkan usia.",
    "context": "CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR)",
    "answer": "SELECT name, born_state, age FROM head ORDER BY age"
  },