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