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| license: cc-by-sa-3.0 |
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| # sql-create-context-v2 Dataset |
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| ## Overview |
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| 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. |
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| ### Key Enhancements |
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| - **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. |
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| ## Cleansing and Augmentation |
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| 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... |
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| ## TODO |
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| - 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. |
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| ## Sample Entries |
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| ```json |
| { |
| "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} |
| } |
| ``` |
| |
