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language: |
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- en |
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This dataset is a merged collection of multiple text-to-SQL datasets, designed to provide a comprehensive resource for training and evaluating text-to-SQL models. It combines data from several popular benchmarks, including Spider, CoSQL, SparC, and others, to create a diverse and robust dataset for natural language to SQL query generation tasks. |
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Dataset Details |
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Dataset Description |
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Curated by: Mudasir Ahmad Mir |
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Language(s) (NLP): English |
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License: Apache 2.0 |
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This dataset is ideal for researchers and developers working on natural language processing (NLP), semantic parsing, and database query generation. It supports a wide range of SQL complexities, from simple queries to nested and multi-turn interactions, making it suitable for both beginner and advanced text-to-SQL tasks. |
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Dataset Sources [optional] |
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Repository: Hugging Face Dataset Link |
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Original Datasets: |
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Spider: Yale-LILY Lab |
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CoSQL: Yale-LILY Lab |
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SparC: Yale-LILY Lab |
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Uses |
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Direct Use |
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This dataset is intended for: |
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Training Text-to-SQL Models: Use this dataset to train models for converting natural language questions into SQL queries. |
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Benchmarking: Evaluate the performance of text-to-SQL models across diverse queries and domains. |
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Research: Study the challenges of semantic parsing, cross-domain generalization, and conversational SQL generation. |
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Out-of-Scope Use |
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This dataset is not suitable for: |
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Tasks requiring domain-specific knowledge beyond the included datasets. |
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Applications requiring real-time or low-latency SQL generation without further fine-tuning. |
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Dataset Structure |
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The dataset contains the following columns: |
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sentence: A natural language question or query. |
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sql: The corresponding SQL query for the given sentence. |
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Dataset Composition |
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The dataset is created by merging the following sources: |
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Spider Dataset: A cross-domain text-to-SQL dataset with complex SQL queries. |
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CoSQL Dataset: A conversational text-to-SQL dataset with multi-turn interactions. |
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SparC Dataset: A context-dependent text-to-SQL dataset for cross-domain tasks. |
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Custom Text-to-SQL Dataset: A dataset containing additional sentence-SQL pairs. |
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Train Dataset: A dataset with context-question-answer pairs, adapted for text-to-SQL tasks. |
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Dataset Statistics |
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Total Rows: [Insert total number of rows] |
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Unique SQL Patterns: [Insert number of unique SQL patterns] |
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Domains Covered: Academia, geography, conversational systems, and more. |
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Dataset Creation |
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Curation Rationale |
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This dataset was created to provide a unified and diverse resource for text-to-SQL tasks, combining high-quality datasets from multiple domains and use cases. The goal is to support research and development in natural language understanding and database query generation. |
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Source Data |
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Data Collection and Processing |
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The dataset was created by merging publicly available text-to-SQL datasets, including Spider, CoSQL, and SparC. The data was cleaned and standardized to ensure consistency in column names and formats. |
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Who are the source data producers? |
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Spider: Yale-LILY Lab |
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CoSQL: Yale-LILY Lab |
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SparC: Yale-LILY Lab |
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Custom Dataset: Mudasir Ahmad Mir |
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Annotations [optional] |
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The SQL queries in this dataset are manually curated and validated by the original dataset creators. |
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Annotation process |
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Annotations were created as part of the original datasets. For example: |
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Spider: Annotators were provided with database schemas and asked to write SQL queries for given natural language questions. |
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CoSQL: Annotators engaged in multi-turn conversations to generate context-dependent SQL queries. |
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Who are the annotators? |
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The annotators include researchers and contributors from Yale-LILY Lab and other organizations involved in the original datasets. |
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Personal and Sensitive Information |
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This dataset does not contain personal, sensitive, or private information. |
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Bias, Risks, and Limitations |
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Recommendations |
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Users should be aware of the following: |
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The dataset may contain biases inherent in the original datasets, such as domain-specific language or query complexity. |
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The dataset is not designed for real-time applications without further fine-tuning or optimization. |
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Citation [optional] |
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If you use this dataset in your research, please cite the original datasets (Spider, CoSQL, SparC, etc.) along with this merged version. |
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BibTeX: |
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bibtex |
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Copy |
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@misc{text-to-sql, |
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author = {Mudasir Ahmad Mir}, |
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title = {Text-to-SQL Dataset}, |
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year = {2023}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/datasets/Mudasir692/text-to-sql}} |
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} |
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APA: |
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Mudasir Ahmad Mir. (2023). Merged Text-to-SQL Dataset. Hugging Face. https://huggingface.co/datasets/Mudasir692/text-to-sql |
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Glossary [optional] |
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Text-to-SQL: The task of converting natural language questions into SQL queries. |
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Cross-Domain: Refers to datasets that cover multiple domains or topics. |
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Multi-Turn Interactions: Conversations where SQL queries depend on previous interactions. |
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More Information [optional] |
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For more information, visit the Hugging Face dataset page: Merged Text-to-SQL Dataset. |
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Dataset Card Authors [optional] |
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Mudasir Ahmad Mir |
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Dataset Card Contact |
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For questions or feedback, please contact Mudasir Ahmad Mir. |
