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README.md
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@@ -12,17 +12,6 @@ The `sql-create-context-v2` dataset enhances the original dataset built from Wik
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- **Dataset Format:** Transitioned to JSON Lines (JSONL) format for improved handling of large datasets and streamlined processing of individual records.
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- **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.
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- Include references to the dialect in the question for better understanding.
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- Expand informative contexts beyond CREATE TABLE statements.
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- 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
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Citing this Work
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If you use the sql-create-context-v2 dataset, please cite the following in addition to the original works:
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```
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@misc{sql-create-context-v2_2024,
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title = {sql-create-context-v2 Dataset},
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note = {Enhancements and modifications to the original sql-create-context dataset for improved usability and processing.}
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}
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```
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Datasets Used to Create This Dataset
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```
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@misc{b-mc2_2023_sql-create-context,
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title = {sql-create-context Dataset},
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note = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.},
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}
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```
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```
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Datasets used to create this dataset
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@article{zhongSeq2SQL2017,
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year = {2017}
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}
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```
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```
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@article{yu2018spider,
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title = {Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
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journal = {arXiv preprint arXiv:1809.08887},
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year = {2018}
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}
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```
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- **Dataset Format:** Transitioned to JSON Lines (JSONL) format for improved handling of large datasets and streamlined processing of individual records.
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- **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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## Sample Entries
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```json
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Citing this Work
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If you use the sql-create-context-v2 dataset, please cite the following in addition to the original works:
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```
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@misc{sql-create-context-v2_2024,
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title = {sql-create-context-v2 Dataset},
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note = {Enhancements and modifications to the original sql-create-context dataset for improved usability and processing.}
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}
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```
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Datasets Used to Create This Dataset
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```
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@misc{b-mc2_2023_sql-create-context,
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title = {sql-create-context Dataset},
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note = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.},
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}
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```
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```
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Datasets used to create this dataset
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@article{zhongSeq2SQL2017,
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year = {2017}
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}
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```
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```
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@article{yu2018spider,
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title = {Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
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journal = {arXiv preprint arXiv:1809.08887},
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year = {2018}
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}
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```
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