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@@ -4,15 +4,17 @@ task_categories:
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  - table-question-answering
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  ---
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- BiomedSQL
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  [GitHub](https://github.com/NIH-CARD/biomedsql)
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  [Paper](https://huggingface.co/papers/2505.20321)
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- Dataset Summary<br>
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  BiomedSQL is a text-to-SQL benchmark designed to evaluate Large Language Models (LLMs) on scientific tabular reasoning tasks. It consists of curated question-SQL query-answer triples covering a variety of biomedical and SQL reasoning types. The benchmark challenges models to apply implicit
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  scientific criteria rather than simply translating syntax.
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- benchmark_data: contains the question-SQL query-answer triples. Please note that you will have to replace the ```{project_id}``` string in the provided "gold" queries with the GCP project that you create your DB in if you wish to rerun them.<br>
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- db_data: contains the parquet files needed to recreate the tables in the evaluation database.
 
 
 
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  - table-question-answering
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  ---
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+ **BiomedSQL**
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  [GitHub](https://github.com/NIH-CARD/biomedsql)
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  [Paper](https://huggingface.co/papers/2505.20321)
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+ **Dataset Summary**<br>
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  BiomedSQL is a text-to-SQL benchmark designed to evaluate Large Language Models (LLMs) on scientific tabular reasoning tasks. It consists of curated question-SQL query-answer triples covering a variety of biomedical and SQL reasoning types. The benchmark challenges models to apply implicit
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  scientific criteria rather than simply translating syntax.
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+ **Repository Organization**
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+ - **benchmark_data**: contains the question-SQL query-answer triples.
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+ - Please note that you will have to replace the ```{project_id}``` and ```{dataset_name}``` placeholder strings in the ```benchmark_query``` column with the names of the GCP project and BigQuery dataset that you create if you wish to rerun them. Our GitHub code automatically handles this entity replacement through ```.env``` configurations when running experiment code.<br>
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+ - **db_data**: contains the parquet files needed to recreate the tables in the evaluation database.