Datasets:

ramachetan22
/

sql-create-context-v2

The sql-create-context-v2 dataset enhances the original dataset built from WikiSQL and Spider, focusing on text-to-SQL tasks with a special emphasis on reducing hallucination of column and table names. This version introduces a JSONL format for more efficient data processing and iteration, alongside a structured approach to representing SQL queries in the dataset entries.

Key Enhancements

Dataset Format: Transitioned to JSON Lines (JSONL) format for improved handling of large datasets and streamlined processing of individual records.
Structured Query Representation: Each SQL query answer is now encapsulated within an object keyed by SQL_Query, facilitating clearer separation between the query text and other metadata.

Cleansing and Augmentation

Building upon the original dataset's cleansing and augmentation process, this version maintains the use of SQLGlot for parsing and inferring data types while introducing...

TODO

Convert queries and CREATE TABLE statements into different SQL dialects using SQLGlot.
Include references to the dialect in the question for better understanding.
Expand informative contexts beyond CREATE TABLE statements.
Enhance datatype parsing to address inconsistencies like numeric column names and strings as numbers.

Sample Entries

{
  "question": "Please show the themes of competitions with host cities having populations larger than 1000.",
  "context": "CREATE TABLE city (City_ID VARCHAR, Population INTEGER); CREATE TABLE farm_competition (Theme VARCHAR, Host_city_ID VARCHAR)",
  "answer": {"SQL_Query": "SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID WHERE T1.Population > 1000"}
},
{
  "question": "Please show the different statuses of cities and the average population of cities with each status.",
  "context": "CREATE TABLE city (Status VARCHAR, Population INTEGER)",
  "answer": {"SQL_Query": "SELECT Status, AVG(Population) FROM city GROUP BY Status"}
}

Citing this Work
If you use the sql-create-context-v2 dataset, please cite the following in addition to the original works:

@misc{sql-create-context-v2_2024, title = {sql-create-context-v2 Dataset}, author = Rama Chetan Atmudi, year = {2024}, url = {https://huggingface.co/datasets/ramachetan22/sql-create-context-v2}, note = {Enhancements and modifications to the original sql-create-context dataset for improved usability and processing.} }

Datasets Used to Create This Dataset

@misc{b-mc2_2023_sql-create-context, title = {sql-create-context Dataset}, author = {b-mc2}, year = {2023}, url = {https://huggingface.co/datasets/b-mc2/sql-create-context}, note = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.}, }

Datasets used to create this dataset @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} }

@article{yu2018spider, title = {Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, 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}, journal = {arXiv preprint arXiv:1809.08887}, year = {2018} }